Category: Business

  • How to Assess Your Review Collection Strategy – Business

    How to Assess Your Review Collection Strategy – Business

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    Some people collect rocks, others collect baseball cards. You, on the other hand, collect reviews. 

    User reviews are an invaluable asset for modern businesses, serving as a resource for product teams, salespeople, marketers, and prospective buyers alike. Reviews help showcase your product and build trust between your brand and the consumer. 

    The key to user reviews is consistency. In fact, 85% of consumers consider reviews more than three months old to be irrelevant. This means you can’t just collect a handful of reviews and call it quits on your strategy. 

    Luckily, implementing a robust review strategy doesn’t have to be a headache. This guide will walk you through everything you need to assess, implement, and optimize your review collection strategy.

    Looking ahead

    In this article you will learn how to:

    • Evaluate and complete your my.G2 profile
    • Implement changes to ramp up review collection
    • Qualify for G2 Reports and Best-Of lists
    • Review and optimize your review strategy

    Review collection 101

    It’s no secret that brands are in the midst of a crisis of trust with today’s consumers. The modern buyer is more socially aware, technologically-driven, and has higher expectations than their older counterparts. This shift in the buyer’s journey means that transparency is more important than ever before. 

    But what do reviews have to do with untrusting buyers? In a highly connected digital world, consumers are constantly exposed to negative news and reports. This means modern buyers are less likely to trust a company and much more likely to trust their peers. 

    Social proof is critical when it comes to building trust with consumers. Authentic feedback and testimonials from other consumers are incredibly important as they help validate product claims for the buyer. 

    93%

    of buyers say online reviews influenced their purchase decisions.

    Source: Qualtrics

    And reviews aren’t just beneficial for buyers. Companies can utilize product reviews to keep tabs on their current and previous customers’ sentiments about their products and experiences. 

    Information collected from user reviews can also be shared across departments to drive various initiatives, including: 

    • Marketing can use reviews to inform value propositions or as trusted assets for campaigns. 
    • Product teams can collect information from reviews to influence product development and enhancements. 
    • Customer success can use reviews to check the pulse on customer sentiment.   
    • Sales can leverage user reviews and testimonials when in discussions with prospective customers. 

    Why are user reviews important?

    Review collection is an invaluable strategy for every business. Implementing a strong review collection strategy will help:

    • Build trust with today’s wary buyer by providing authentic social proof
    • Consistently solicit valuable feedback for product development teams
    • Spread brand awareness and attract different buyer demographics
    • Buyers compare and evaluate solutions during the decision-making process

    Step 1: Evaluating your my.G2 profile

    You’re likely familiar with G2 product profiles. These are the pages where users can learn more about a product and, most importantly, read and leave reviews. my.G2 is simply the backend admin portal where companies can access their G2 product profile, reviews, content, and data. 

    First, it’s important to completely fill out your G2 product profile. This is how users learn more about your product on G2, so if there is limited information on your profile, they’ll likely move on to another solution. Remember, the more information the better. 

    my.g2-review-activity
    The Review Activity dashboard on my.G2

    Profile performance data is important, but putting this information into context is crucial. That’s why you can find G2 Analytics all in one place to help you understand how you stack up against your competition. 

    Your G2 profile gives you a chance to differentiate from your competitors and convert more visitors into buyers. Once your profile is up and running, you’re ready to take advantage of the valuable features of my.G2

    my.G2 provides valuable insights into your product profile, enabling you to see review activity, filter by approval status, and even respond to reviews. You can then easily export and utilize this information across your entire organization.

    Tips for completing your G2 profile

    Your G2 profile is the first place potential buyers will go to learn about you. Make sure your product profile includes:

    • Updated brand logos
    • An eye-catching banner image
    • Accurate product descriptions
    • Current product pricing

    Step 2: Implementing changes to ramp up review collection

    Once you establish your G2 product profile, there are simple tactics you can use to maximize your user reviews and ramp up feedback collection.

    First, rework your messaging. Communicate why a user’s feedback is valued and how your business leverages reviews to improve the product. For instance, you can ask users to specifically leave feedback on new product updates or launches. People are more likely to take action if they understand how their feedback impacts both the business and their own experience with the product. 

    If you feel your review collection has plateaued, try incentivizing it. Monetary incentives, like gift cards or discount codes, encourage users to take immediate action and leave authentic feedback. 

    Next, be sure to leverage all of your current user reviews by sharing them on your website, social media, and other marketing assets. If user reviews helped influenced a product change or development, share the cause and effect of this feedback to your audience. Show users that you take their feedback seriously by responding publicly to both positive and negative reviews. 

    89%

    of consumers read companies’ responses to user reviews.

    Source: BrightLocal

    However, one of the most important factors for review collection is timing. Leaving feedback should be simple for the user. Meet your customers exactly where they are and provide a seamless user experience by utilizing review levers and integrations. 

    Review integrations and levers

    G2’s review integrations and levers are useful tools brands can leverage to increase the quality, frequency, and number of reviews. Soliciting feedback is already part of your plan and G2 supports those initiatives with integrations and levers that are safe and simple to use. 

    The Integration Hub is the go-to place for G2 customers to safely connect their profile with various sales, marketing, and retention tools they already use and trust. G2 integrations help automate workflow to make review collection a breeze. 

    integration-hub@2xThe Integration Hub in my.G2

    Hot tip: Check out G2’s integrations with review collection tools Pendo and Medallia!

    G2 Review Levers are specifically designed to safely collect more and better reviews. These levers can help fuel your review engine so you can meet your customers in the right place, at the right time. 

    • In-app reviews: Activate in-app reviews by simply adding a form to your site. The in-app review lever helps you avoid legal roadblocks and protect your customer list.
    • Drupal widget: Showcase reviews to customers wherever (and whenever) they may need them. The Drupal widget collects G2 reviews and allows for custom placement based on your site’s needs.
    • Automated review updating: Ensure your user reviews are always up-to-date by automating the process of asking users for fresh, updated feedback.
    • InMail prompts: Enable your customers to answer questions and leave feedback directly inside emails with our InMail prompts. 

    Step 3: Qualifying for G2 Reports and Best-Of lists

    User reviews possess a lot of power. Not only do they help influence buying decisions and product development, but they also help your brand rank on G2 Reports. But ranking high on reports isn’t just for vanity. Your inclusion in reports can heavily influence a prospective customer’s purchase. 

    Here’s how it works: Your customers leave reviews. Those reviews affect how you rank on G2 software reports. Those reports influence other buyers’ purchase decisions. Wash, rinse, repeat. 

    Below are the four types of G2 Reports: 

    • Grid® Reports compare products within a particular software category based on satisfaction and market presence scores. 
    • Index Reports provide a score for evaluating a single factor in the software purchase process, highlighting usability, implementation, relationship, or results. 
    • Compare Reports feature side-by-side comparisons of up to four competitors based on satisfaction rating.
    • Momentum Reports display a product’s growth trajectory in their respective category over the last year, based on user satisfaction scores, digital growth, and employee growth.

    G2 scores products based on reviews from our user community, as well as aggregated data from online sources and social networks. Our unique algorithm then calculates the Satisfaction and Market Presence scores in real-time. 

    Although scoring is different for each report, factors such as pricing information, ROI, results data, user adoption, usability data, feature level data, as well as your Satisfaction and Market Presence scores can affect your rank. 

    Tips for qualifying for G2 Reports

    Harness the power of your customers’ voices with G2 Reports. Here’s how you can start qualifying for inclusion on reports:

    • Fill out your G2 profile with updated visuals and high-value product information like pricing.
    • Regularly solicit feedback and collect at least 100 reviews on your G2 product profile.
    • Implement product changes based on user feedback to positively influence future reviews.
    • Ask for updates to ensure your reviews reflect the most current customer sentiment.

    If you’re interested in learning how your company can rank on G2 Best-Of software lists, stay tuned! G2 will be announcing how to qualify for our 2022 Best-Of lists soon.

    Step 4: Optimizing your review strategy

    So you kickstarted a review collection strategy – now what? After you get the basics up and running, you can focus on continuously improving your collection methods and the user experience to make the most of your reviews.

    Stay consistent with your strategy

    It’s important to run regular review campaigns and monitor the activity on my.G2. This will provide you with invaluable insight into your users’ sentiment that can help drive product development.

    Engage and respond to user reviews so you can establish trust and transparency between you and your consumers.

    Improve your user experience

    The best way to ensure your strategy works (and continues to work) is to listen and adapt to your users’ preferences. Use G2 integrations and levers to make the review process as easy and convenient for your users as possible.

    You can also create automatic triggers so your review collection is always on.

    Leverage content off G2

    Capturing reviews for your G2 profile is valuable – but it doesn’t have to stop there. Display reviews or badges on your website’s home page, product pages, and testimonial pages. You can include relevant reviews next to demo request forms and other contact pages to boost prospective buyers’ confidence.

    G2’s Content Subscription gives you access to social assets that showcase your profile’s star rating and user reviews.

    The 4 pillars of review collection

    Follow the four pillars of a review collection strategy for long-term success: 

    • Ask every user for reviews
    • Solicit reviews consistently
    • Request updates for old reviews
    • Respond to reviews and provide customer service

    Keep on adding to your collection

    After all, a good collector never stops collecting.

    Your review collection strategy should operate like a well-oiled machine, only requiring minor fixes to help improve processes. Committing to a set strategy and working to optimize the experience will help you get users to share authentic, marketable feedback about your product.

    Not a G2 customer yet? Launch a review campaign built around trust and credibility. Schedule a 15-minute consultation with a G2 expert today. 

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  • GoCardless Taps into North American Market Using G2 Solutions – Business

    GoCardless Taps into North American Market Using G2 Solutions – Business

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    Expanding an established business overseas is easier said than done. GoCardless, however, successfully broke into a new market in under one year with the help of G2 Seller Solutions. 

    GoCardless is a global leader in account-to-account payments, making it easy to collect both recurring and one-off payments directly from customers’ bank accounts. The company currently works with over 60,000 businesses worldwide, ranging from small businesses to enterprises. Its payment solutions help businesses reduce transaction costs, lower churn, and gain better oversight of revenue. 

    But GoCardless wasn’t always operating at the global scale it is today.

    After finding great success in the United Kingdom, starting with small businesses and moving up-market to the enterprise, GoCardless began branching out to the North American market. At the end of 2019, GoCardless opened an office in the United States to begin scaling business in North America. 

    Jennifer Ellis, North American Marketing Director at GoCardless, reflects on the expansion, “We’ve really scaled the business in North America, even during COVID-19. But given we were new to the market, the biggest hurdle we faced was that businesses weren’t familiar with the service GoCardless provides: using bank debit as a payment option to improve business.”

    “Businesses in the U.S. are used to paying by checks, wire transfers, and credit cards,” Ellis explains. “But with bank debit, they are able to save money that is being spent on wire transfers and credit card fees. Companies can reduce their day sales outstanding (DSO) when they use bank debit versus checks.”

    A fresh strategy to help break into a new market

    The biggest hurdle GoCardless faced when breaking into this new market was awareness. GoCardless needed to spread the word about their brand and mission while also educating prospective buyers on the benefits of their solution. 

    “In North America, we were challenged with building awareness for both the brand as well as the pain of collecting payments,” Ellis explained. “Businesses traditionally pay by check or wire transfer, putting payments on credit cards because those payments have worked for so many years. We wanted people to see that there is a better way to collect payments, and that’s what we are focused on.” 

    Problems

    GoCardless wanted to break into the North American market by:

    • Spreading brand awareness and sharing the GoCardless mission and vision
    • Finding high-intent buyers and educating them on the pain point and solution

    Since GoCardless is headquartered in the UK, many businesses in the North American market weren’t familiar with them just yet. These prospective customers also didn’t understand the pain points they currently face with collecting payments – they just accepted them as part of doing business. 

    “Credit cards aren’t always the best solution for a recurring payment,” Ellis says about GoCardless’ bank debit solution. “Cards expire, get deactivated, frozen, or lost – plus the processing fees can add up and become very costly. With bank debit, account numbers don’t change, making automatic recurring payments much easier and cheaper than credit cards and their corresponding fees.”

    Utilizing resources to identify prospects and spread awareness

    GoCardless’ North American team became laser-focused on leveraging G2 solutions in their quest to expand within the market. 

    Solutions

    GoCardless partnered with G2 to:

    • Run G2 Review Campaigns to solicit authentic, marketable user feedback to continuously attract new visitors and fuel their buyer intent data
    • Tap into G2 Buyer Intent data to unveil high-intent prospects and identify the right contact within the company for outreach
    • Utilize the G2 Content Subscription to build brand awareness and access custom reports for mid-to-bottom-funnel prospects

    To establish their brand and build credibility in the North American market, GoCardless runs G2 Review Campaigns to collect valuable user reviews that fuel placement on the G2 rankings and grid. The traffic attracted from these campaigns helps spread brand awareness and ultimately influences the brand’s buyer intent data. 

    GoCardless’ presence on G2 has been invaluable for brand awareness. The more reviews the brand captures on their profile, the higher they rank on G2 category pages. This results in high visibility and traffic to their page and, ultimately, even more buyer intent data for the GoCardless sales team. 

    “It helps for us to be listed within the rankings when customers are visiting G2 to research,” Ellis says. “They see GoCardless and can easily learn more about us.”

    g2-sell-buyer-intent-data-screenshor-header@2x

    Example of G2 Buyer Intent Activity

    G2 Buyer Intent data has helped the GoCardless sales team identify surging accounts. As the most powerful intent data on the market, G2 Buyer Intent helps brands like GoCardless learn which companies are researching their products and competitors, then find the right person at those companies to contact. 

    Ellis discusses how a GoCardless sales rep utilized buyer intent:  “There was a company looking at our G2 profile, clicking our ads, and comparing us to competitors. They were able to take that intent data and see where the company was located – in this case, Livermore, California. Using G2 integration tools like LinkedIn Sales Navigator with our buyer intent data, they were able to discover the right contacts in this company: decision-makers in the finance department. They then narrowed it down to two or three individuals in finance so they could reach out to the prospect.” 

    “We found the right account with G2 Buyer Intent data and the right person with G2 + CRM Connector for Salesforce integration.”

    Jennifer Ellis
    North American Marketing Director at GoCardless

    Ellis also says that GoCardless completely revamped the company’s tech stack. This robust bank of software means the GoCardless team can utilize G2 Buyer Intent data in tandem with other helpful tools, such as Demandbase and Chili Piper, to pin down the right prospect and tailor their messaging appropriately. 

    GoCardless has also reaped the benefits of the G2 Content Subscription, a solution that provides the company licensing for G2 quarterly reports, social assets, video reviews, and other content to help influence prospects. 

    The Content Subscription provided the GoCardless sales team access to game-changing tools during live calls with prospects. Ellis explains, “If [a salesperson] is talking with a prospective customer who is scoring us against our competitors, they can utilize a custom comparison report for those conversations right on the spot. Our sales team can use one-to-one or one-to-many G2 comparison reports to show prospects how we stack up against our competitors.” 

    G2-content-subscription-example
    Example of assets from the G2 Content Subscription

    Leveraging review campaigns and pinpointing high-value prospects

    Initially, Ellis and her team thought these solutions would be heavily driven by marketing. “I thought [the G2 solutions] would be helpful for driving MQLs,” Ellis recalls. “From a marketing perspective, I was hoping to leverage the customer voice to build awareness for GoCardless.” 

    In the end, G2’s solutions far exceeded GoCardless’ expectations. “Based on reporting, I’ve also found it to be really helpful for prospects that are mid-to-late-funnel. Prospects who were considering or evaluating our product have now moved on to having conversations with our sales team,” Ellis explains. 

    More authentic user reviews

    Starting in August 2020, GoCardless implemented numerous G2 Review Campaigns to help attract visitors and build their G2 product profile. GoCardless was able to leverage G2 Review Campaigns to drive 97 new reviews, propelling them to a #1 ranking on nine different G2 reports – all between August 2020 and July 2021. 

    Prior to these campaigns, GoCardless was already listed as a leader in their primary software category payment processing. However, GoCardless is now a high-ranking software in other relevant categories: Payment Gateways, Subscription Revenue Management, Enterprise Payment, and Installment Payment. 

    97

    new approved user reviews between August 2020 and July 2021.

    Better prospects, higher engagement

    In a 6 month period, between September 2020 and March 2021, GoCardless found that G2 was the first touchpoint for over 400 accounts in North America. These prospects may have seen a sponsored content ad on G2, viewed GoCardless’ G2 profile, or compared GoCardless against their competitors prior to visiting the GoCardless site.

    For marketing, this helps reaffirm that prospects are finding and researching GoCardless on G2 before visiting the site. And for sales, the buyer intent data is extremely valuable to help prioritize the teams’ outreach.

    “G2 Buyer Intent gives additional help with prospects and the Content Subscription helps build awareness, credibility, and leadership in a new market.”

    Jennifer Ellis
    North American Marketing Director at GoCardless

    Across all geographies, G2 provides GoCardless with higher-value visitors. This is especially true in the U.S., which has helped the brand break into the North American market. 

    28%

    lower bounce rate for users who come from G2 to the GoCardless site.

    Compared to the average GoCardless U.S. site visitor, those who have come from G2 have lower bounce rates (40% compared to 68%), visit more pages per session (3.62 vs. 1.44), and have a higher average session duration (5:25 minutes vs. 1:06 minutes). 

    393%

    increase of average session duration for users who come from G2 to the GoCardless site.

    151%

    increase of pages per session for users who come from G2 to the GoCardless site.

    GoCardless saw the value of incorporating G2 Review Campaigns, G2 Buyer Intent, and Content Subscription solutions into their sales and marketing playbooks. What was initially meant to complement the marketing team’s awareness efforts turned into a useful toolkit for GoCardless reps working the top, middle, and bottom of the funnel. 

    Not only has GoCardless been able to expand their reach and spread awareness, but their teams are now also equipped with the proper resources to pinpoint high-value opportunities – making the sales process more efficient on both ends. 

    “Our sales and marketing teams have been really pleased by bringing G2 into our tech stack,” Ellis says. “We’re excited to see how G2 can continue to help us scale GoCardless not just in North America, but in other regions as well.”

    Implement G2 Seller Solutions today

    Millions of people research, compare, and buy software on G2 each month. And this extremely valuable buyer data is right at your fingertips. 

    Looking to accelerate the sales cycle and drive more revenue? Schedule a demo to learn more about G2 Seller Solutions. 

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  • Your Comprehensive Guide to Manufacturing Sales – Business

    Your Comprehensive Guide to Manufacturing Sales – Business

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    Ask any manufacturer what they think of manufacturing sales; they’d reply to your question with one word: complex.

    What is manufacturing sales? 

    Manufacturing sales happens when a manufacturer sells their products – finished goods from raw materials or components. Well, at least that was an appropriate definition pre-Industry 4.0, pre-digitalization, and pre-consumerization.

    Nowadays, manufacturing sales needs to do so much more. Smart manufacturing methods drive rapid innovation across all verticals with 24/7 online sales is becoming the norm in the industry, and manufacturers are making every effort to adapt to B2C expectation levels.

    What makes manufacturing sales complex? 

    For the answer to become obvious, you need to understand the key difference between B2B and B2C sales. In B2C, a company sells to a consumer; for example, Tesla sells a car to your neighbor. In B2B, a company sells to another business; for example, a manufacturer of steering systems sells to Tesla.

    In the first case, the transaction is relatively straightforward. It involves one or very few decision-makers, is driven by personal expectations, and sold through a price that is known, and in comparison, small.

    In the second, the business buying the product looks for manufacturers that meet their technical requirements for a specific component of their vehicle. They then start a selection process that involves multiple suppliers of the same component submitting their proposals.

    Throughout this process, various stakeholders from Tesla can be involved in the requirements setting, budgeting, and research process. Meanwhile, the suppliers need to engage in a series of tasks. It starts from gathering requirements, and using the information gathered to begin design, engineering, calculations, part configurations, material and production simulations, pricing and ultimately creating a tender document.

    This process involves salespeople, engineers, material and production experts, procurement, and middle-management decision-makers. The entire quoting process can take up to several weeks and incur a high cost of sales.

    The B2B buying and selling journey in manufacturing is complex

    At this point, we have not even painted a complete picture of this journey. Often, negotiations or corrections can easily send this process into a repeat loop. And when the buying business makes the final order, another set of complexities come into play. Factors such as labor dynamics, material costs, or any unforeseen production circumstances could threaten the successful delivery of the product or manufacturing solution.

    You can conclude that in B2B manufacturing sales, the buying journey and the selling process are a winding path of multiple dependencies. It involves a high number of stakeholders and carries significant risk from errors or delays for both the buying business and the manufacturer.

    Having a complicated buying process in the manufacturing industry was accepted as the norm. But research has shown that B2B enterprises like manufacturers, need to overcome it in order to recover during economic downturns.

    Evolving market challenges for manufacturers

    Adding to the complexities of the B2B sales process, manufacturers are faced with four main market challenges that force them to rethink their sales and business models. 

    1. Global competition

    Globalization is not a new challenge for manufacturers. But with the rise of digital opportunities, manufacturers across the globe are able to sell and market to the same customers. The digital economy is giving manufacturers longer reach than ever. Local manufacturers now have to compete against manufacturers based in other parts of the world. With competition intensifying, slim margins are expected to tighten even more.

    2. Customer expectations

    B2B decision-makers are now increasingly consumerist. They are demanding a seamless and smooth customer experience. They expect customization for already complex products and solutions. They want matching service offers, convenient purchasing and self-service models, and at the very least, high responsiveness and better customer service.

    3. Market volatility

    Manufacturers traditionally struggle with market volatility. Factors such as raw material prices, labor costs, or political changes can affect their supply chains and sales markets. Global economic fluctuations can impact a less agile industry such as manufacturing disproportionally harder than other sectors. 

    4. Industry 4.0 opportunities

    The fourth industrial revolution has opened new horizons for manufacturers. They now have access to technology that can transform their productivity, speed, and flexibility. Emerging technology like cyber-physical production systems (CPPSs), Industrial Internet of Things (IIoT), Assisted Intelligence, and cloud computing, help manufacturers build smarter factories.

    One of the primary opportunities arising is the cost-efficient production of mass-customized products in a lot size of one. The challenge, however, is that manufacturers are not agile enough to adopt these new technologies, and lack clear strategies to successfully implement emerging technology.

    Market challenges for manufacturers in a global and digital economy

    Manufacturers leading the market today have strategies that help them stay ahead. They overcome market challenges by constantly adapting how they sell and operate their business so they can solidify their market position and mitigate the risk of revenue loss.

    5 industries that use advanced manufacturing sales

    In manufacturing, each sub-vertical has its own way of operating. The differences can be found in the way manufacturers use labor, machines, tools, and chemical or biological processing. Understanding the differences can help manufacturers look for strategies to cope with market challenges, or sales complexities.

    Medtech and high technology

    The medical device and technology industry (medtech) covers medical instruments and equipment used for diagnostics, monitoring, and therapeutics. Medical device manufacturers produce surgical instruments, diagnostics apparatus such as ultrasound instruments, and medical devices like pacemakers. Technologies such as 3D imaging, additive manufacturing, coatings and surface treatments, micromanufacturing, and even nanotechnology come into play.

    The high-tech manufacturing industry produces cutting-edge technology products from computers to plane engines. Advanced machinery is used to manufacture components and parts (such as semiconductors and circuit boards).Processes such as forming, casting, molding, and laser engineering are used.

    Sales in these industries are characterized by a high complexity in; production and sales, a competitive market landscape, and high costs. Manufacturers in these sub-verticals focus on supply chain efficiency and demonstrate high agility while keeping costs and inventory levels low. Increasing market share and expanding a global sales force or distribution network are significant challenges for businesses in medtech and high-tech. 

    Steel and metal

    The steel and metals production industry uses various raw metals to fabricate structures, machines, tools, and parts. Value-added processes include welding, cutting, forming, metal stamping, forging, casting, and machining  all of which require engineering designs. This industry struggles with volatile raw material prices and a general slowdown in demand. As a result, steel and metal manufacturers take extra care in aligning supply and demand while keeping track of their highly diverse customer requirements.

    Machinery and equipment

    Industrial machinery and equipment manufacturers design, fabricate and assemble various products. These products can range from small-scale machines to industrial components or tools. Buyers are from sectors like agricultural, construction, mining, aviation and aerospace, defense, and maritime. 

    Machinery and equipment manufacturers also serve other verticals that require industrial equipment like robotics and pumps. This industry primarily deals with rising global competition and commoditization of hardware which causes massive price pressure.

    Electrical equipment

    The electrical equipment and electrical components industry manufactures products that generate, distribute, or use electrical power. Products can be things such as batteries, sensors, electrical wiring, lighting equipment or motors, generators, and transformers. This industry depends on efficient operations, economies of scale in purchasing materials and production, and of course, technological expertise. In addition, manufacturers in this vertical seek to balance costs versus customer requirements and short innovation cycles while keeping a close eye on margins.

    Automotive and suppliers

    The automotive industry is a significant economic global force that designs, develops, and manufactures motor vehicles. Auto parts and components manufacturers are suppliers for the automotive industry. They produce components that become part of the automobile, such as brakes, suspension, steering systems, engine parts, safety management, climate control equipment, engine cooling and exhaust systems, and interior and cockpit modules.

    They can be classified in tiers one to three in a supplier pyramid depending on the hierarchical order (tier one supplies directly to the manufacturer). The entire industry is transforming at lightning speed, resulting in short innovation cycles. Suppliers are facing massive price pressure through global competition and volatile material costs. At the same time, suppliers in the industry usually commit to multi-year contracts. Their focus must be on margins, successful international sales, and new business opportunities in digital channels. 

    6 types of manufacturing that defines sales 

    By now, you learnt that manufacturing sales strategies are dependent on market conditions and can be influenced by industry-specific factors. Another factor that can determine the way manufacturers sell, is the manufacturing system or type used to produce their products. Finally, various business models are developed to balance out the costs of producing the product while meeting customers’ demands.

    1. Make-to-stock (MTS)

    This method is used by factories that produce goods that are then stocked in warehouses or stored in showrooms. Manufacturers that found success with this business model understand that demand must be somewhat predictable and precisely forecasted. That is because the manufacturer expends capital to produce goods in advance. The capital then ends up bound in the finished goods until they are sold.

    When a manufacturer overproduces goods, they would often apply aggressive discounts that eats into their margins to sell their goods and avoid writing them off. Underproduction means some market demand remains unserved, which gives competitors a chance to steal market share. In the case of MTS manufacturers, the sales complexity lies in forecasting demand before production.

    2. Make-to-order (MTO)

    In MTO businesses, goods are manufactured and sold when orders are received. In this model, inventories are easily managed, and the risk of overproducing is eliminated as market demands are clear. But in today’s consumerized B2B world, customers are increasingly becoming impatient. They don’t want to wait for their products to be produced.

    Also, economies-of-scale in mass production is rarely applicable. Manufacturers cannot guarantee a steady stream of orders, so production costs and prices are likely higher due to low quantities. In the recent years however, Industry 4.0 technologies such as additive manufacturing (3D printing) are helping MTO businesses produce goods in lower quantities at lower costs. The industry term is lot size of one, where an MTO can produce a single product at the lowest possible cost.

    Demand for customization in MTO is also on the rise. However, it is incredibly complex to sell. Cost calculations need to be immaculate for MTO businesses to achieve profitability.

    3. Make-to-assemble (MTA) 

    Make-to-assemble or assemble-to-order (ATO) is a hybrid between make-to-stock and make-to-order. The manufacturer will produce components or parts in anticipation of orders for assembly. While the manufacturer is ready to fulfill customer orders instantly, he can also be left with unwanted parts or components when demand is low.

    4. Configure-to-order (CTO) 

    Configure-to-order is a hybrid of make-to-stock and make-to-order. In CTO manufacturing, products are assembled and configured according to customer requirements. The subassemblies are made to stock and need to be immediately available at a well-predicted inventory levels to fulfill customer orders quickly. The final assembly is postponed until the order comes in, which helps manufacturers remain flexible enough to offer high product variety.

    Just as in MTO, this model helps manufacturers cope in today’s competitive markets where tailored products with unique requirements are in high demand. The CTO system enables both mass customization, and fast response time in order fulfillment. In CTO manufacturing sales, the complexity lies in the configuration. End-product standards are predefined, but must be flexible enough to allow viable configuration.

    5. Engineer-to-order (ETO)

    Engineer-to-order is a manufacturing system that is often used for very complex or specialized products. The process starts once an order is received and involves design, engineering, and production. Products or solutions are engineered (or customized) by the manufacturer according to the customer’s specifications.

    ETO manufacturing requires high subject matter expertise, meticulous requirements, engineering analysis, and high design effort. The costs tied to these requirements are high because of human resources needed, and must be maintained even before a customer orders. The manufacturer’s risk increases as production deadline, and cost estimates are added into the quote.

    6. Manufacturing-as-a-Service (MaaS)

    Manufacturing-as-a-service is a service-oriented business model that has existed since pre-industrial times when farmers brought their wheat to the mill to get flour. It is based on sharing manufacturing infrastructure to reduce costs and make better products.

    In practice, customers define what needs to be made, and the manufacturer makes it. With the availability of Industry 4.0 digital technology (IIoT, additive-manufacturing, connectivity, big data, and cloud computing), MaaS becomes a highly flexible model that can produce faster, cheaper, and in any quantity, while keeping resource consumption under tight control.

    On the other hand, it is highly dependent on streamlined and reliable supply chains. In the MaaS system, original equipment manufacturers (OEMs) effectively sell their process expertise. It requires a fully digitalized and connected production, and total transparency into their processes and costs. 

    Different manufacturing complexities

    As you can see, each manufacturing system have risks related to supply and demand. This ultimately determines the complexity of manufacturing sales. A successful manufacturing business requires sales strategies that consider supply chain, stock management, production, engineering costs, and quality control.

    The cost of sales usually increases with the level of complexity in engineering and design. To lower costs, manufacturers are turning to digital technology to automate time-consuming activities in the manufacturing sales process. 

    Manufacturing distribution models in B2B

    A distribution channel is the path the manufacturer uses to deliver its products or service to the customer. The route can be as short as a direct interaction between the manufacturer and the buyer. It can also include several interconnected intermediaries like wholesalers, distributors, retailers, and more.

    For manufacturers, it is vital to have a mix of distribution channels that complement their manufacturing type, create high availability for buyers and in target markets, and keep the cost of sales in check.

    Direct sales (manufacturer to B2B customer)

    In direct sales, no intermediaries are involved. The manufacturer or producer sells directly to its B2B customer. For example, a manufacturer of ophthalmic machines sells directly to an optics lab which uses it to produce spectacle lenses.

    Direct selling is an excellent way to manage costs as the manufacturer has complete control over marketing, sales, and shipping. However, manufacturers are also increasingly setting up online stores to meet customer demands for self-service buying in B2B environments. Other channels could be B2B fairs and traveling sales representatives.

    However, as the business grows, so does its distribution needs. To reach a larger consumer base or new markets, manufacturers might sell through intermediaries. 

    Indirect sales: distributor or partner sales

    In indirect sales, manufacturers build up a network of partners, wholesalers, and retailers to meet business goals such as: 

    • Faster expansion
    • Increasing brand recognition in larger sales networks
    • Outsourcing sales operations costs
    • Gaining access to new groups of customers or new market segments 

    In general, a wholesale buyer will stock large quantities of the manufacturer’s products, and sell them to further intermediaries in smaller amounts for a profit. Retailers, on the other hand, are store owners that sell the products to their customers directly.

    Partner sales is an option when commercial knowledge in regions or language is beneficial towards the sale of goods. The manufacturer outsources the marketing and sales processes, but shipping remains in the manufacturer’s hands.

    Risks for indirect distribution models are: 

    • A general lack of managerial control
    • Lower profit per unit sold
    • Risk of creating channel competition 
    • Coordination during the introduction of new products or implementation of changes
    • Higher effort in brand maintenance
    • Managing the sales network relationship

    Manufacturing sales software solutions 

    The world of manufacturing is changing faster than ever before, and manufacturers need to adapt their selling methods constantly to meet their business goals. The tools they use vary in effectiveness, efficiency, and future viability.

    Traditional industrial sales processes and software

    Manual tasks and siloed operations are the characteristics of a traditional manufacturing sales process. However, regardless of the type of manufacturing, the manufacturer’s offer – also called quotation, request for quote (RFQ), request for information (RFI), or tender – always starts the seller-buyer relationship.

    It often contains the details of the product, service, or solution requested. Its level of complexity depends on the type of manufacturing the product needs. In essence, it is a critical part of the manufacturing sales process, as it also sets the margins, and can determine a deal’s profitability.

    Traditional sales methods rely on manual extraction of sales relevant data from sources such as the enterprise resource planning (ERP) system or product catalogs. This data is then processed through tools such as Excel to calculate costs, configure products or solutions and create offers.

    This process is slow, lacks accuracy, and is prone to human or system error. This is especially true for the more complex manufacturing types such as CTO, ETO, and MaaS.

    In addition, hundreds of dependencies, product variations, and cost factors require up-to-date information that cannot be guaranteed without connectivity. In addition, quotes often require input from various specialist departments, and final quotes need reviews and approvals. Errors and misalignment can send the entire process into a repeat loop.

    The outcome of such a manual process is inadequate in terms of quality, margins, sales cycle time, and cost of sales. It is also not sustainable in highly competitive markets with demanding customers.

    Digital sales software solutions: CRM, CPQ, e-commerce

    Over the years, manufacturers have acquired or custom-developed solutions that could deal with complexity in quote creation. On-premise software to configure, price, and quote (CPQ) manufacturing products and solutions were implemented to fit very individualized IT landscapes.

    With the rise of cloud computing and affordable internet around the globe, new solutions entered the market. The most relevant cloud software solutions for manufacturing sales are customer relationship management (CRM) software, CPQ, and e-commerce systems.

    CRM

    Manufacturers have evolved to be more innovative, connected, and customer-centric than ever. A manufacturing-focused CRM helps them achieve their new goals, and manage the complexity of all customer interactions.

    At the same time, it keeps sales teams and distribution partners updated with the latest data from the manufacturer’s ERP, CPQ, or e-commerce systems. It provides manufacturers with a 360-degree customer view and enables them to match customer’s demands for speed and customization.

    At its core, a CRM helps manufacturers manage and prioritize customers to: 

    • Drive successful sales outcomes with accurate customer data
    • Manage customers and their needs across all channels
    • Increase sales efficiency by automating the lead-to-order process

    Typical functionalities of a manufacturing-focused CRM are customer account and contact management, purchase and conversation tracking, pipeline management, forecasting and reporting, and sales collaboration features.

    CPQ

    A CPQ is a central element in manufacturing sales. It enables manufacturing sales teams or channels, unprecedented access to all sales relevant data. It then assists with generating quotes for complex products faster, more accurately.

    A tight integration to the manufacturer’s ERP is essential. But a CPQ’s true potential is realized when it is coupled with the CRM and e-commerce system.

    A CPQ system digitalizes and automates the entire quotation process to: 

    • Create best-match offers from standard, to fully customized products and solutions
    • Generate consistently accurate quotes and enable shorter sales cycles
    • Help exceed buyer expectations with outstanding offerings on all selected channels

    Classic features of a CPQ are product/service catalogs with smart selling functionalities, quote automation with workflows and approval routings, as well as product, service, and solution configuration.

    Increasingly popular are interactive 2D and 3D visualizations for products and solutions. In addition, an essential feature for CTO and ETO businesses is manufacturing costing and pricing. And finally, a CPQ should tie all quote details together through quote management and document creation functionalities.

    E-commerce

    Customers expect a seamless buying experience with 24/7 access, even from B2B businesses. E-commerce solutions for manufacturers can be anything from a digital product catalog, an online store for the entire product and service offering, a spare parts online shop, or a partner, distributor, and dealer portal. They satisfy the demand for 24/7 business operability and provide fast business growth options at a very low cost, and high returns.

    Commerce platforms make knowledge and products instantly available online to: 

    • Enable self-service purchasing for customers or partners who know what they want
    • Empower distributors or partners to inform themselves, make inquiries, or purchase
    • Deliver a seamless and error-free buying experience for after-sales and repeat-purchases

    A manufacturing commerce platform typically delivers online shop modules based on manufacturing, configuration, and customer data. It securely restricts data access, and provides personalized offers for different buying groups.

    It can include customer account discounts, promote install-base related products, or recommend spare parts and consumables that are relevant to the respective customer. Partner portals offer similar online buying experiences. In this case, the partner plays the role of the buyer, and can inquire, purchase, register equipment, or re-order consumables on behalf of their customers.

    The role of the ERP in manufacturing sales

    For manufacturers, the actual value of the before-mentioned software solutions comes into play when the software is interconnected and deeply integrated with the manufacturer’s ERP.

    The ERP is at the heart of all manufacturing processes, and it houses: 

    • Product data
    • Raw material information
    • Engineering knowledge
    • Configuration models
    • Costs and prices
    • Price lists, variant prices, account discounts 
    • Shipping costs and taxes
    • Status of business commitments such as orders and purchases 
    • Stock availabilities and delivery times 
    • Region and sales channel specific calculation schemes

    Years of work and evolving production knowledge are saved in a manufacturer’s ERP. A digital sales solution needs to make effective use of this knowledge and back-sync regularly to update information. Manufacturers looking to accelerate manufacturing sales can use a digital sales platform to connect all data sources, process steps, and stakeholders involved to build a seamless end-to-end process. 

    Customer centricity in manufacturing sales

    As hard as the manufacturing sales process is, the B2B buying journey can be just as difficult. Today, buying a complex manufacturing solution involves six to ten decision makers. They are expected to gather product and seller information and use it to make the best buying decision possible for their business. Globalized markets bring even more options and solution providers to the evaluation table.

    However, even when problems are well-defined, solutions are explored, and requirements clear, the buying group must still complete several tasks to select the best supplier, validate the process and find consensus. Circling back to our first graphic, you may notice that every complexity in the sales process matches equally complicated processes on the buyers’ side.

    Manufacturers that understand that they have to simplify selling, but at the same time simplify how customers buy, have made the necessary shift in perspective to be successful. In addition, a focus on customer experience and aligning the customer journey with the sales process can repay in terms of higher win rates, bigger deal sizes, and customer loyalty.

    Alignment of the customer journey with the sales process

    Conclusion

    You have learned just how complex manufacturing sales can be. It is shaken and shaped by market challenges, industry specifics, manufacturing type, and distribution mix. Digital sales solutions and industry 4.0 technology offer manufacturers the chance to react to these challenges and build agile and sustainable sales strategies for the future.

    With digital manufacturing sales, manufacturers can protect margins, improve customer satisfaction, and, more importantly, increase revenue. A customer-centric approach ensures that manufacturers become easy to buy from and are equipped for the future of a consumerized B2B world.

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  • G2 Partners With Pendo to Help Companies Solicit Quality Reviews – Business

    G2 Partners With Pendo to Help Companies Solicit Quality Reviews – Business

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    Collecting quality user feedback is challenging, but platforms like Pendo help solve this problem. Pendo is a product analysis app that allows users to easily submit comprehensive feedback that provides product teams with valuable insights. 

    We’re thrilled to announce the launch of a new partnership with Pendo. As of today, Pendo customers will have the ability to collect G2 Reviews with strategically timed and targeted in-app prompts.

    About G2 Reviews + Pendo

    The G2 Reviews + Pendo integration enables you to solicit organic, authentic, and comprehensive in-app user feedback. This integration helps you:

    • Gather comprehensive insights to help influence product development
    • Meet your customers in-app and eliminate the need for third-party intervention
    • Solicit higher quality feedback from your most engaged users
    • Ensure gradual and consistent feedback collection without the need for batch campaigns

    About Pendo

    Pendo is a product analysis tool that offers NPS surveys, in-app guidance, and feedback collection tools. Product usage analytics and customer sentiment collected by Pendo combine quantitative and qualitative data to help companies make better decisions. 

    Pendo can collect data across all desktop and mobile apps and integrate with multiple CRMs to send that data. The no-code in-app tool works to improve your product experience and exceed customer expectations.

    Pendo collects valuable customer feedback for product teams to build better product experiences.

    How Pendo + G2 Reviews work together

    This integration allows companies to utilize Pendo to set up workflows in their native apps and request a G2 Review from specific segments of users. 

    Here’s how it works: when your customers are using your app, Pendo will serve up different notifications and requests throughout the user journey. With this integration, one of the requests can be “Leave a G2 Review”. 

    If the user chooses to leave a G2 Review, they can complete the review right there without having to leave your app or search for your G2 profile on their own. Just like all other G2 Reviews, the review is then submitted to G2.com and published once approved. 

    pendo-integration-lp-header

    Improved product insights

    This integration helps companies collect feedback while the product is top-of-mind for the user that, in turn, can be used to drive important product decisions.

    G2 Reviews capture an array of answered questions to help inform product development. All of this user feedback can also provide marketing, sales, and customer success teams the insights they need to drive maximum adoption, conversion, retention, and expansion.

    Higher quality user feedback

    Pendo uses pre-building engagement-driven workflows to ask specific customer segments to take certain actions. This means you can choose to only solicit your most engaged users to leave a G2 Review.

    This feature helps you collect higher-quality reviews from actual users that are consistently spread out over the lifetime of your app. Pendo’s in-app prompts consistently solicit feedback from people who actually utilize your platform.

    Rachel Bentley, Director of Product Management at G2, says that soliciting reviews in-app is the best way to ensure quality feedback. Bentley explains, “Make it easy for someone to leave you feedback…you need to catch them at the right time and place when they’re actually using the product. Ask users to leave reviews consistently and in the moment.”

    “The most convenient time to ask the user for a review is on your platform while they’re using your functionality.”

    Rachel Bentley
    Director of Product Management at G2

    Optimal timing for customer sentiment

    When collecting user feedback, finding the balance between providing a convenient user experience without compromising the consistency, quality, and effectiveness of that feedback can be challenging. But by meeting your customers where they already are — in your app — you can secure the authentic feedback you need from the right users, all the time, without even asking them to abandon their workflow.

    But how well does it work? According to Pendo, users are twice as likely to give feedback when they are solicited in-app rather than by email. This helps ensure your user feedback is candid and influenced by your product and your product only — and there will be twice as much!

    With this integration, users no longer need to hop from platform to platform just to leave feedback. Asking for reviews in-app helps mitigate outside influences, like third-party sites, from compromising your user experience. In-app feedback is the closest you can get to truly understanding your customer’s sentiment.

    Meet your customers where they are to capture feedback

    Pendo + G2 Reviews helps streamline your review collection strategy and makes it easier for your customers to submit feedback. This integration enables you to consistently capture quality reviews so you can focus on product innovation.

    Capture G2 Reviews by meeting your customers where they already are: your product. Get started with G2 Reviews + Pendo today.

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  • G2 + Medallia Partnership Makes User Feedback Simple and Secure – Business

    G2 + Medallia Partnership Makes User Feedback Simple and Secure – Business

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    Traditional routes of collecting feedback require third-party tools and multiple accounts, but in today’s age, companies cannot afford to sacrifice their customer data security and user experience. Luckily, Medallia helps solve this problem with its top-to-bottom customer sentiment and feedback platform. 

    Today, G2 is pleased to announce the launch of a new partnership with Medallia. Medallia customers can now collect G2 Reviews with strategically timed and targeted in-app prompts.

    About G2 Reviews + Medallia

    The G2 Reviews + Medallia integration enables you to safely solicit authentic and comprehensive in-app user feedback. Companies using this integration can now:

    • Capture G2 Reviews without sharing customer information with G2
    • Solicit feedback securely without taking users out of your brand experience
    • Utilize helpful user feedback across all departments
    • Create a consistent, review collection engine
    • Mitigate outside influences to ensure reviews are focused on your product

    About Medallia

    Medallia is a customer feedback management software platform that enables companies to improve the customer experience. The platform works to provide personalized and predictive insights that can drive action and results. 

    Medallia captures feedback signals from various sources, including in-person, digital channels, social media, and IoT interactions. The platform puts this data through artificial intelligence and machine learning engines to analyze customer sentiment and provide companies with topics and themes they can act on. 

    Companies can use this always-on tool to solicit feedback from users without needing to leave their app.

    How the G2 Reviews + Medallia integration works

    With the G2 Reviews + Medallia integration, companies can create workflows in their native apps to request G2 Reviews from specific user segments. 

    The process is simple: Medallia serves up different notifications and requests while customers are using your app. With this integration, one of the requests can prompt users to leave a G2 Review. 

    The user can then complete a G2 Review without having to leave your app or find your G2 profile on their own. Like all other G2 Reviews, the review is submitted to G2.com and published once approved.

    medallia-integration-generate-more-reviews@2x

    Better customer insights 

    G2 Reviews ask customers questions aimed at providing companies with insights to inform every stage of business, from roadmap to retention. This provides all departments in your organization with the valuable information they need to drive adoption, retain key accounts, and identify opportunities for expansion. 

    The G2 Reviews + Medallia integration solicits feedback from users while they are using the product. This helps ensure more comprehensive and insightful user reviews that can influence business strategies across all departments.

    And since users are much more likely to provide feedback when solicited in-app compared to being asked to take the same action elsewhere, it’s a win-win for both you and the user. 

    Consistent feedback collection

    With the ability to customize who receives prompts, Medallia gives you the power to only solicit your most engaged app users to leave a G2 Review. This means you can target review collection from actual users on a consistent basis over the life of your app — driving higher conversion rates, and higher quality feedback. 

    By only soliciting reviews from regular app users, Medallia can help you gain insightful feedback that is both fresh and accurate.

    Aubyn Casady, Principal Product Marketing Manager at G2, says organizations can ensure fresh and accurate feedback by soliciting regular app users with this integration. Casady explains, “Finally, Medallia and G2 have partnered to create a solution that makes review collection convenient for everyone, and most importantly, the user. This integration removes the cumbersome steps of creating accounts on multiple platforms and driving traffic to third-party review platforms.”

    “You can simply meet your users right where they already are: your app.”

    Aubyn Casady
    Principal Product Marketing Manager at G2

    Increased security for customers

    Collecting reviews from your users while they’re in-app is the most effective and secure way to capture candid customer feedback influenced by the experience that matters most: using your product. 

    This means you no longer have to sacrifice user experience or risk third-party security issues just to collect customer sentiment. Soliciting reviews in-app also mitigates outside influences from compromising your user feedback, bringing you as close to truly understanding your customer sentiment as possible.

    Don’t sacrifice security for user reviews

    Medallia + G2 Reviews will help you streamline your review collection strategy while making it easy for your customers to submit feedback. This integration allows you to consistently capture quality feedback with strategically timed and targeted in-app prompts. 

    Deliver the user experience your customers expect while capturing the feedback you need. Get started with G2 Reviews + Medallia today.

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  • What Is Logistic Regression? Learn How to Use It – Business

    What Is Logistic Regression? Learn How to Use It – Business

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    Life is full of tough binary choices.

    Should I have that slice of pizza or not? Should I carry an umbrella or not?

    While some decisions can be rightly made by weighing the pros and cons – for example, it’s better not to eat a slice of pizza as it contains extra calories – some decisions may not be that easy.

    For instance, you can never be fully sure whether or not it’ll rain on a specific day. So the decision of whether or not to carry an umbrella is a tough one to make.

    To make the right choice, one requires predictive capabilities. This ability is highly lucrative and has numerous real-world applications, especially in computers. Computers love binary decisions. After all, they speak in binary code.

    Machine learning algorithms, more precisely the logistic regression algorithm, can help predict the likelihood of events by looking at historical data points. For example, it can predict whether an individual will win the election or whether it’ll rain today.

    If you’re wondering what regression analysis is, it’s a type of predictive modeling technique used to find the relationship between a dependent variable and one or more independent variables.

    An example of independent variables is the time spent studying and the time spent on Instagram. In this case, grades will be the dependent variable. This is because both the “time spent studying” and the “time spent on Instagram” would influence the grades; one positively and the other negatively.

    Logistic regression is a classification algorithm that predicts a binary outcome based on a series of independent variables. In the above example, this would mean predicting whether you would pass or fail a class. Of course, logistic regression can also be used to solve regression problems, but it’s mainly used for classification problems.

    Another example would be predicting whether a student will be accepted into a university. For that, multiple factors such as the SAT score, student’s grade point average, and the number of extracurricular activities will be considered. Using historical data about previous outcomes, the logistic regression algorithm will sort students into “accept” or “reject” categories.

    Logistic regression is also referred to as binomial logistic regression or binary logistic regression. If there are more than two classes of the response variable, it’s called multinomial logistic regression. Unsurprisingly, logistic regression was borrowed from statistics and is one of the most common binary classification algorithms in machine learning and data science.

    Did you know? An artificial neural network (ANN) representation can be seen as stacking together a large number of logistic regression classifiers.

    Logistic regression works by measuring the relationship between the dependent variable (what we want to predict) and one or more independent variables (the features). It does this by estimating the probabilities with the help of its underlying logistic function.

    Key terms in logistic regression

    Understanding the terminology is crucial to properly decipher the results of logistic regression. Knowing what specific terms mean will help you learn quickly if you’re new to statistics or machine learning.

    The following are some of the common terms used in regression analysis:

    • Variable: Any number, characteristic, or quantity that can be measured or counted. Age, speed, gender, and income are examples.
    • Coefficient: A number, usually an integer, multiplied by the variable that it accompanies. For example, in 12y, the number 12 is the coefficient.
    • EXP: Short form of exponential.
    • Outliers: Data points that significantly differ from the rest.
    • Estimator: An algorithm or formula that generates estimates of parameters.
    • Chi-squared test: Also called the chi-square test, it’s a hypothesis testing method to check whether the data is as expected.
    • Standard error: The approximate standard deviation of a statistical sample population.
    • Regularization: A method used for reducing the error and overfitting by fitting a function (appropriately) on the training data set.
    • Multicollinearity: Occurrence of intercorrelations between two or more independent variables.
    • Goodness of fit: Description of how well a statistical model fits a set of observations.
    • Odds ratio: Measure of the strength of association between two events.
    • Log-likelihood functions: Evaluates a statistical model’s goodness of fit.
    • Hosmer–Lemeshow test: A test that assesses whether the observed event rates match the expected event rates.

    What is a logistic function?

    Logistic regression is named after the function used at its heart, the logistic function. Statisticians initially used it to describe the properties of population growth. Sigmoid function and logit function are some variations of the logistic function. Logit function is the inverse of the standard logistic function.

    logistic function

    In effect, it’s an S-shaped curve capable of taking any real number and mapping it into a value between 0 and 1, but never precisely at those limits. It’s represented by the equation:

    f(x) = L / 1 + e^-k(x – x0)

    In this equation:

    • f(X) is the output of the function
    • L is the curve’s maximum value
    • e is the base of the natural logarithms
    • k is the steepness of the curve
    • x is the real number
    • x0 is the x values of the sigmoid midpoint

    If the predicted value is a considerable negative value, it’s considered close to zero. On the other hand, if the predicted value is a significant positive value, it’s considered close to one.

    Logistic regression is represented similar to how linear regression is defined using the equation of a straight line. A notable difference from linear regression is that the output will be a binary value (0 or 1) rather than a numerical value.

    Here’s an example of a logistic regression equation:

    y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

    In this equation:

    • y is the predicted value (or the output)
    • b0 is the bias (or the intercept term)
    • b1 is the coefficient for the input
    • x is the predictor variable (or the input)

    The dependent variable generally follows the Bernoulli distribution. The values of the coefficients are estimated using maximum likelihood estimation (MLE),  gradient descent, and stochastic gradient descent.

    As with other classification algorithms like the k-nearest neighbors, a confusion matrix is used to evaluate the accuracy of the logistic regression algorithm.

    Did you know? Logistic regression is a part of a larger family of generalized linear models (GLMs).

    Just like evaluating the performance of a classifier, it’s equally important to know why the model classified an observation in a particular way. In other words, we need the classifier’s decision to be interpretable.

    Although interpretability isn’t easy to define, its primary intent is that humans should know why an algorithm made a particular decision. In the case of logistic regression, it can be combined with statistical tests like the Wald test or the likelihood ratio test for interpretability.

    When to use logistic regression

    Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them, and there’s no middle ground.

    In the case of predictor variables, they can be part of any of the following categories:

    • Continuous data: Data that can be measured on an infinite scale. It can take any value between two numbers. Examples are weight in pounds or temperature in Fahrenheit.
    • Discrete, nominal data: Data that fits into named categories. A quick example is hair color: blond,  black, or brown.
    • Discrete, ordinal data: Data that fits into some form of order on a scale. An example is telling how satisfied you’re with a product or service on a scale of one to five.

    Logistic regression analysis is valuable for predicting the likelihood of an event. It helps determine the probabilities between any two classes.

    In a nutshell, by looking at historical data, logistic regression can predict whether:

    • An email is a spam
    • It’ll rain today
    • A tumor is fatal
    • An individual will purchase a car
    • An online transaction is fraudulent
    • A contestant will win an election
    • A group of users will buy a product
    • An insurance policyholder will expire before the policy term expires
    • A promotional email receiver is a responder or non-responder

    In essence, logistic regression helps solve probability and classification problems. In other words, you can expect only classification and probability outcomes from logistic regression.

    For example, it can be used to determine the probability of something being “true or false” and also for deciding between two outcomes like “yes or no”.

    A logistic regression model can also help classify data for extract, transform, and load (ETL) operations. Logistic regression shouldn’t be used if the number of observations is less than the number of features. Otherwise, it may lead to overfitting.

    Linear regression vs. logistic regression

    While logistic regression predicts the categorical variable for one or more independent variables, linear regression predicts the continuous variable. In other words, logistic regression provides a constant output, whereas linear regression offers a continuous output.

    Since the outcome is continuous in linear regression, there are infinite possible values for the outcome. But for logistic regression, the number of possible outcome values is limited.

    In linear regression, the dependent and independent variables should be linearly related. In the case of logistic regression, the independent variables should be linearly related to the log odds (log (p/(1-p)).

    Tip: Logistic regression can be implemented in any programming language used for data analysis, such as R, Python, Java, and MATLAB.

    While linear regression is estimated using the ordinary least squares method, logistic regression is estimated using the maximum likelihood estimation approach.

    Both logistic and linear regression are supervised machine learning algorithms and the two main types of regression analysis. While logistic regression is used to solve classification problems, linear regression is primarily used for regression problems.

    Going back to the example of time spent studying, linear regression and logistic regression can predict different things. Logistic regression can help predict whether the student passed an exam or not. In contrast, linear regression can predict the student’s score.

    Logistic regression assumptions

    While using logistic regression, we make a few assumptions. Assumptions are integral to correctly use logistic regression for making predictions and solving classification problems.

    The following are the main assumptions of logistic regression:

    • There is little to no multicollinearity between the independent variables.
    • The independent variables are linearly related to the log odds (log (p/(1-p)).
    • The dependent variable is dichotomous or binary; it fits into two distinct categories. This applies to only binary logistic regression, which is discussed later.
    • There are no non-meaningful variables as they might lead to errors.
    • The data sample sizes are larger, which is integral for better results.
    • There are no outliers.

    Types of logistic regression

    Logistic regression can be divided into different types based on the number of outcomes or categories of the dependent variable.

    When we think of logistic regression, we most probably think of binary logistic regression. In most parts of this article, when we referred to logistic regression, we were referring to binary logistic regression.

    The following are the three main types of logistic regression.

    Binary logistic regression

    Binary logistic regression is a statistical method used to predict the relationship between a dependent variable and an independent variable. In this method, the dependent variable is a binary variable, meaning it can take only two values (yes or no, true or false, success or failure, 0 or 1).

    A simple example of binary logistic regression is determining whether an email is spam or not.

    Multinomial logistic regression

    Multinomial logistic regression is an extension of binary logistic regression. It allows more than two categories of the outcome or dependent variable. 

    It’s similar to binary logistic regression but can have more than two possible outcomes. This means that the outcome variable can have three or more possible unordered types –  types having no quantitative significance. For example, the dependent variable may represent “Type A,” “Type B,” or “Type C”.

    Similar to binary logistic regression, multinomial logistic regression also uses maximum likelihood estimation to determine the probability. 

    For example, multinomial logistic regression can be used to study the relationship between one’s education and occupational choices. Here, the occupational choices will be the dependent variable which consists of categories of different occupations.

    Ordinal logistic regression

    Ordinal logistic regression, also known as ordinal regression, is another extension of binary logistic regression. It’s used to predict the dependent variable with three or more possible ordered types – types having quantitative significance. For example, the dependent variable may represent “Strongly Disagree,” “Disagree,” “Agree,” or “Strongly Agree”.

    It can be used to determine job performance (poor, average, or excellent) and job satisfaction (dissatisfied, satisfied, or highly satisfied).

    Advantages and disadvantages of logistic regression

    Many of the advantages and disadvantages of the logistic regression model apply to the linear regression model. One of the most significant advantages of the logistic regression model is that it doesn’t just classify but also gives probabilities.

    The following are some of the advantages of the logistic regression algorithm.

    • Simple to understand, easy to implement, and efficient to train
    • Performs well when the dataset is linearly separable
    • Good accuracy for smaller datasets
    • Doesn’t make any assumptions about the distribution of classes
    • It offers the direction of association (positive or negative)
    • Useful to find relationships between features
    • Provides well-calibrated probabilities
    • Less prone to overfitting in low dimensional datasets
    • Can be extended to multi-class classification

    However, there are numerous disadvantages to logistic regression. If there’s a feature that would separate two classes perfectly, then the model can’t be trained anymore. This is called complete separation.

    This happens mainly because the weight for that feature wouldn’t converge as the optimal weight would be infinite. However, in most cases, complete separation can be solved by defining a prior probability distribution of weights or introducing penalization of the weights.

    The following are some of the disadvantages of the logistic regression algorithm:

    • Constructs linear boundaries
    • Can lead to overfitting if the number of features is more than the number of observations
    • Predictors should have average or no multicollinearity
    • Challenging to obtain complex relationships. Algorithms like neural networks are more suitable and powerful
    • Can be used only to predict discrete functions
    • Can’t solve non-linear problems
    • Sensitive to outliers

    When life gives you options, think logistic regression

    Many might argue that humans don’t live in a binary world, unlike computers. Of course, if you’re given a slice of pizza and a hamburger, you can take a bite of both without having to choose just one. But if you take a closer look at it, a binary decision is engraved on (literally) everything. You can either choose to eat or not eat a pizza; there’s no middle ground.

    Evaluating the performance of a predictive model can be tricky if there’s a limited amount of data. For this, you can use a technique called cross-validation, which involves partitioning the available data into a training set and a test set.

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  • What Is Training Data? How It’s Used in Machine Learning – Business

    What Is Training Data? How It’s Used in Machine Learning – Business

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    Machine learning models are as good as the data they’re trained on.

    Without high-quality training data, even the most efficient machine learning algorithms will fail to perform.

    The need for quality, accurate, complete, and relevant data starts early on in the training process. Only if the algorithm is fed with good training data can it easily pick up the features and find relationships that it needs to predict down the line.

    More precisely, quality training data is the most significant aspect of machine learning (and artificial intelligence) than any other. If you introduce the machine learning (ML) algorithms to the right data, you’re setting them up for accuracy and success.

    Training data is also known as training dataset, learning set, and training set. It’s an essential component of every machine learning model and helps them make accurate predictions or perform a desired task.

    Simply put, training data builds the machine learning model. It teaches what the expected output looks like. The model analyzes the dataset repeatedly to deeply understand its characteristics and adjust itself for better performance.

    In a broader sense, training data can be classified into two categories: labeled data and unlabeled data.

    labeled data vs. unlabeled data

    What is labeled data?

    Labeled data is a group of data samples tagged with one or more meaningful labels. It’s also called annotated data, and its labels identify specific characteristics, properties, classifications, or contained objects. 

    For example, the images of fruits can be tagged as apples, bananas, or grapes.

    Labeled training data is used in supervised learning. It enables ML models to learn the characteristics associated with specific labels, which can be used to classify newer data points. In the example above, this means that a model can use labeled image data to understand the features of specific fruits and use this information to group new images.

    Data labeling or annotation is a time-consuming process as humans need to tag or label the data points. Labeled data collection is challenging and expensive. It isn’t easy to store labeled data when compared to unlabeled data.

    What is unlabeled data?

    As expected, unlabeled data is the opposite of labeled data. It’s raw data or data that’s not tagged with any labels for identifying classifications, characteristics, or properties. It’s used in unsupervised machine learning, and the ML models have to find patterns or similarities in the data to reach conclusions.

    Going back to the previous example of apples, bananas, and grapes, in unlabeled training data, the images of those fruits won’t be labeled. The model will have to evaluate each image by looking at its characteristics, such as color and shape.

    After analyzing a considerable number of images, the model will be able to differentiate new images (new data) into the fruit types of apples, bananas, or grapes. Of course, the model wouldn’t know that the particular fruit is called an apple. Instead, it knows the characteristics needed to identify it.

    There are hybrid models that use a combination of supervised and unsupervised machine learning.

    How training data is used in machine learning

    Unlike machine learning algorithms, traditional programming algorithms follow a set of instructions to accept input data and provide output. They don’t rely on historical data, and every action they make is rule-based. This also means that they don’t improve over time, which isn’t the case with machine learning.

    For machine learning models, historical data is fodder. Just as humans rely on past experiences to make better decisions, ML models look at their training dataset with past observations to make predictions.

    Predictions could include classifying images as in the case of image recognition, or understanding the context of a sentence as in natural language processing (NLP).

    Think of a data scientist as a teacher, the machine learning algorithm as the student, and the training dataset as the collection of all textbooks.

    The teacher’s aspiration is that the student must perform well in exams and also in the real world. In the case of ML algorithms, testing is like exams. The textbooks (training dataset) contain several examples of the type of questions that’ll be asked in the exam.

    Tip: Check out big data analytics to know how big data is collected, structured, cleaned, and analyzed.

    Of course, it won’t contain all the examples of questions that’ll be asked in the exam, nor will all the examples included in the textbook will be asked in the exam. The textbooks can help prepare the student by teaching them what to expect and how to respond.

    No textbook can ever be fully complete. As time passes, the kind of questions asked will change, and so, the information included in the textbooks needs to be changed. In the case of ML algorithms, the training set should be periodically updated to include new information.

    In short, training data is a textbook that helps data scientists give ML algorithms an idea of what to expect. Although the training dataset doesn’t contain all possible examples, it’ll make algorithms capable of making predictions.

    Training data vs. test data vs. validation data

    Training data is used in model training, or in other words, it’s the data used to fit the model. On the contrary, test data is used to evaluate the performance or accuracy of the model. It’s a sample of data used to make an unbiased evaluation of the final model fit on the training data.

    A training dataset is an initial dataset that teaches the ML models to identify desired patterns or perform a particular task. A testing dataset is used to evaluate how effective the training was or how accurate the model is.

    Once an ML algorithm is trained on a particular dataset and if you test it on the same dataset, it’s more likely to have high accuracy because the model knows what to expect. If the training dataset contains all possible values the model might encounter in the future, all well and good.

    But that’s never the case. A training dataset can never be comprehensive and can’t teach everything that a model might encounter in the real world. Therefore a test dataset, containing unseen data points, is used to evaluate the model’s accuracy.

    training data vs. validation data vs. test data

    Then there’s validation data. This is a dataset used for frequent evaluation during the training phase. Although the model sees this dataset occasionally, it doesn’t learn from it. The validation set is also referred to as the development set or dev set. It helps protect models from overfitting and underfitting.

    Although validation data is separate from training data, data scientists might reserve a part of the training data for validation. But of course, this automatically means that the validation data was kept away during the training.

    Tip: If you’ve got a limited amount of data, a technique called cross-validation can be used to estimate the model’s performance. This method involves randomly partitioning the training data into multiple subsets and reserving one for evaluation.

    Many use the terms “test data” and “validation data” interchangeably. The main difference between the two is that validation data is used to validate the model during the training, while the testing set is used to test the model after the training is completed.

    The validation dataset gives the model the first taste of unseen data. However, not all data scientists perform an initial check using validation data. They might skip this part and go directly to testing data.

    What is human in the loop?

    Human in the loop refers to the people involved in the gathering and preparation of training data. 

    Raw data is gathered from multiple sources, including IoT devices, social media platforms, websites, and customer feedback. Once collected, individuals involved in the process would determine the crucial attributes of the data that are good indicators of the outcome you want the model to predict.

    The data is prepared by cleaning it, accounting for missing values, removing outliers, tagging data points, and loading it into suitable places for training ML algorithms. There will also be several rounds of quality checks; as you know, incorrect labels can significantly affect the model’s accuracy.

    What makes training data good?

    High-quality data translates to accurate machine learning models.

    Low-quality data can significantly affect the accuracy of models, which can lead to severe financial losses. It’s almost like giving a student a textbook containing wrong information and expecting them to excel in the examination.

    The following are the four primary traits of quality training data.

    Relevant

    The data needs to be relevant to the task at hand. For example, if you want to train a computer vision algorithm for autonomous vehicles, you probably won’t require images of fruits and vegetables. Instead, you would need a training dataset containing photos of roads, sidewalks, pedestrians, and vehicles.

    Representative

    The AI training data must have the data points or features that the application is made to predict or classify. Of course, the dataset can never be absolute, but it must have at least the attributes the AI application is meant to recognize.

    For example, if the model is meant to recognize faces within images, it must be fed with diverse data containing people’s faces from various ethnicities. This will reduce the problem of AI bias, and the model won’t be prejudiced against a particular race, gender, or age group.

    Uniform

    All data should have the same attribute and must come from the same source.

    Suppose your machine learning project aims to predict churn rate by looking at customer information. For that, you’ll have a customer information database that includes customer name, address, number of orders, order frequency, and other relevant information. This is historical data and can be used as training data.

    One part of the data can’t have additional information, such as age or gender. This will make training data incomplete and the model inaccurate. In short, uniformity is a critical aspect of quality training data.

    Comprehensive

    Again, the training data can never be absolute. But it should be a large dataset that represents the majority of the model’s use cases. The training data must have enough examples that’ll allow the model to learn appropriately. It must contain real-world data samples as it will help train the model to understand what to expect.

    If you’re thinking of training data as values placed in large numbers of rows and columns, sorry, you’re wrong. It could be any data type like text, images, audio, or videos.

    What affects training data quality?

    Humans are highly social creatures, but there are some prejudices that we might have picked as children and require constant conscious effort to get rid of. Although unfavorable, such biases may affect our creations, and machine learning applications are no different.

    For ML models, training data is the only book they read. Their performance or accuracy will depend on how comprehensive, relevant, and representative the very book is.

    That being said, three factors affect the quality of training data:

     

    1. People: The people who train the model have a significant impact on its accuracy or performance. If they’re biased, it’ll naturally affect how they tag data and, ultimately, how the ML model functions.

    2. Processes: The data labeling process must have tight quality control checks in place. This will significantly increase the quality of training data.

    3. Tools: Incompatible or outdated tools can make data quality suffer. Using robust data labeling software can reduce the cost and time associated with the process.

    Where to get training data

    There are several ways to get training data. Your choice of sources can vary depending on the scale of your machine learning project, the budget, and the time available. The following are the three primary sources for collecting data.

    Open-source training data

    Most amateur ML developers and small businesses that can’t afford data collection or labeling rely on open-source training data. It’s an easy choice as it’s already collected and free. However, you’ll most probably have to tweak or re-annotate such datasets to fit your training needs. ImageNet, Kaggle, and Google Dataset Search are some examples of open-source datasets.

    Internet and IoT

    Most mid-sized companies collect data using the internet and IoT devices. Cameras, sensors, and other intelligent devices help collect raw data, which will be cleaned and annotated later. This data collection method will be specifically tailored to your machine learning project’s requirements, unlike open-source datasets. However, cleaning, standardizing, and labeling the data is a time-consuming and resource-intensive process.

    Artificial training data

    As the name suggests, artificial training data is artificially created data using machine learning models. It’s also called synthetic data, and it’s an excellent choice if you require good quality training data with specific features for training an algorithm. Of course, this method will require large amounts of computational resources and ample time.

    How much training data is enough?

    There isn’t a specific answer to how much training data is enough training data. It depends on the algorithm you’re training – its expected outcome, application, complexity, and many other factors.

    Suppose you want to train a text classifier that categorizes sentences based on the occurrence of the terms “cat” and “dog” and their synonyms such as “kitty,” “kitten,” “pussycat,” “puppy,” or “doggy”. This might not require a large dataset as there are only a few terms to match and sort.

    But, if this was an image classifier that categorized images as “cats” and “dogs,” the number of data points needed in the training dataset would shoot up significantly. In short, many factors come into play to decide what training data is enough training data.

    The amount of data required will change depending on the algorithm used.

    For context, deep learning, a subset of machine learning, requires millions of data points to train the artificial neural networks (ANNs). In contrast, machine learning algorithms require only thousands of data points. But of course, this is a far-fetched generalization as the amount of data needed varies depending on the application.

    The more you train the model, the more accurate it becomes. So it’s always better to have a large amount of data as training data.

    Garbage in, garbage out

    The phrase “garbage in, garbage out” is one of the oldest and most used phrases in data science. Even with the rate of data generation growing exponentially, it still holds true.

    The key is to feed high-quality, representative data to machine learning algorithms. Doing so can significantly enhance the accuracy of models. Good quality training data is also crucial for creating unbiased machine learning applications.

    Ever wondered what computers with human-like intelligence would be capable of? The computer equivalent of human intelligence is known as artificial general intelligence, and we’re yet to conclude whether it will be the greatest or the most dangerous invention ever.

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  • What Is Logistic Regression? Learn When to Use It – Business

    What Is Logistic Regression? Learn When to Use It – Business

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    Life is full of tough binary choices.

    Should I have that slice of pizza or not? Should I carry an umbrella or not?

    While some decisions can be rightly made by weighing the pros and cons – for example, it’s better not to eat a slice of pizza as it contains extra calories – some decisions may not be that easy.

    For instance, you can never be fully sure whether or not it’ll rain on a specific day. So the decision of whether or not to carry an umbrella is a tough one to make.

    To make the right choice, one requires predictive capabilities. This ability is highly lucrative and has numerous real-world applications, especially in computers. Computers love binary decisions. After all, they speak in binary code.

    Machine learning algorithms, more precisely the logistic regression algorithm, can help predict the likelihood of events by looking at historical data points. For example, it can predict whether an individual will win the election or whether it’ll rain today.

    If you’re wondering what regression analysis is, it’s a type of predictive modeling technique used to find the relationship between a dependent variable and one or more independent variables.

    An example of independent variables is the time spent studying and the time spent on Instagram. In this case, grades will be the dependent variable. This is because both the “time spent studying” and the “time spent on Instagram” would influence the grades; one positively and the other negatively.

    Logistic regression is a classification algorithm that predicts a binary outcome based on a series of independent variables. In the above example, this would mean predicting whether you would pass or fail a class. Of course, logistic regression can also be used to solve regression problems, but it’s mainly used for classification problems.

    Another example would be predicting whether a student will be accepted into a university. For that, multiple factors such as the SAT score, student’s grade point average, and the number of extracurricular activities will be considered. Using historical data about previous outcomes, the logistic regression algorithm will sort students into “accept” or “reject” categories.

    Logistic regression is also referred to as binomial logistic regression or binary logistic regression. If there are more than two classes of the response variable, it’s called multinomial logistic regression. Unsurprisingly, logistic regression was borrowed from statistics and is one of the most common binary classification algorithms in machine learning and data science.

    Did you know? An artificial neural network (ANN) representation can be seen as stacking together a large number of logistic regression classifiers.

    Logistic regression works by measuring the relationship between the dependent variable (what we want to predict) and one or more independent variables (the features). It does this by estimating the probabilities with the help of its underlying logistic function.

    Key terms in logistic regression

    Understanding the terminology is crucial to properly decipher the results of logistic regression. Knowing what specific terms mean will help you learn quickly if you’re new to statistics or machine learning.

    The following are some of the common terms used in regression analysis:

    • Variable: Any number, characteristic, or quantity that can be measured or counted. Age, speed, gender, and income are examples.
    • Coefficient: A number, usually an integer, multiplied by the variable that it accompanies. For example, in 12y, the number 12 is the coefficient.
    • EXP: Short form of exponential.
    • Outliers: Data points that significantly differ from the rest.
    • Estimator: An algorithm or formula that generates estimates of parameters.
    • Chi-squared test: Also called the chi-square test, it’s a hypothesis testing method to check whether the data is as expected.
    • Standard error: The approximate standard deviation of a statistical sample population.
    • Regularization: A method used for reducing the error and overfitting by fitting a function (appropriately) on the training data set.
    • Multicollinearity: Occurrence of intercorrelations between two or more independent variables.
    • Goodness of fit: Description of how well a statistical model fits a set of observations.
    • Odds ratio: Measure of the strength of association between two events.
    • Log-likelihood functions: Evaluates a statistical model’s goodness of fit.
    • Hosmer–Lemeshow test: A test that assesses whether the observed event rates match the expected event rates.

    What is a logistic function?

    Logistic regression is named after the function used at its heart, the logistic function. Statisticians initially used it to describe the properties of population growth. Sigmoid function and logit function are some variations of the logistic function. Logit function is the inverse of the standard logistic function.

    logistic function

    In effect, it’s an S-shaped curve capable of taking any real number and mapping it into a value between 0 and 1, but never precisely at those limits. It’s represented by the equation:

    f(x) = L / 1 + e^-k(x – x0)

    In this equation:

    • f(X) is the output of the function
    • L is the curve’s maximum value
    • e is the base of the natural logarithms
    • k is the steepness of the curve
    • x is the real number
    • x0 is the x values of the sigmoid midpoint

    If the predicted value is a considerable negative value, it’s considered close to zero. On the other hand, if the predicted value is a significant positive value, it’s considered close to one.

    Logistic regression is represented similar to how linear regression is defined using the equation of a straight line. A notable difference from linear regression is that the output will be a binary value (0 or 1) rather than a numerical value.

    Here’s an example of a logistic regression equation:

    y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

    In this equation:

    • y is the predicted value (or the output)
    • b0 is the bias (or the intercept term)
    • b1 is the coefficient for the input
    • x is the predictor variable (or the input)

    The dependent variable generally follows the Bernoulli distribution. The values of the coefficients are estimated using maximum likelihood estimation (MLE),  gradient descent, and stochastic gradient descent.

    As with other classification algorithms like the k-nearest neighbors, a confusion matrix is used to evaluate the accuracy of the logistic regression algorithm.

    Did you know? Logistic regression is a part of a larger family of generalized linear models (GLMs).

    Just like evaluating the performance of a classifier, it’s equally important to know why the model classified an observation in a particular way. In other words, we need the classifier’s decision to be interpretable.

    Although interpretability isn’t easy to define, its primary intent is that humans should know why an algorithm made a particular decision. In the case of logistic regression, it can be combined with statistical tests like the Wald test or the likelihood ratio test for interpretability.

    When to use logistic regression

    Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them, and there’s no middle ground.

    In the case of predictor variables, they can be part of any of the following categories:

    • Continuous data: Data that can be measured on an infinite scale. It can take any value between two numbers. Examples are weight in pounds or temperature in Fahrenheit.
    • Discrete, nominal data: Data that fits into named categories. A quick example is hair color: blond,  black, or brown.
    • Discrete, ordinal data: Data that fits into some form of order on a scale. An example is telling how satisfied you’re with a product or service on a scale of one to five.

    Logistic regression analysis is valuable for predicting the likelihood of an event. It helps determine the probabilities between any two classes.

    In a nutshell, by looking at historical data, logistic regression can predict whether:

    • An email is a spam
    • It’ll rain today
    • A tumor is fatal
    • An individual will purchase a car
    • An online transaction is fraudulent
    • A contestant will win an election
    • A group of users will buy a product
    • An insurance policyholder will expire before the policy term expires
    • A promotional email receiver is a responder or non-responder

    In essence, logistic regression helps solve probability and classification problems. In other words, you can expect only classification and probability outcomes from logistic regression.

    For example, it can be used to determine the probability of something being “true or false” and also for deciding between two outcomes like “yes or no”.

    A logistic regression model can also help classify data for extract, transform, and load (ETL) operations. Logistic regression shouldn’t be used if the number of observations is less than the number of features. Otherwise, it may lead to overfitting.

    Linear regression vs. logistic regression

    While logistic regression predicts the categorical variable for one or more independent variables, linear regression predicts the continuous variable. In other words, logistic regression provides a constant output, whereas linear regression offers a continuous output.

    Since the outcome is continuous in linear regression, there are infinite possible values for the outcome. But for logistic regression, the number of possible outcome values is limited.

    In linear regression, the dependent and independent variables should be linearly related. In the case of logistic regression, the independent variables should be linearly related to the log odds (log (p/(1-p)).

    Tip: Logistic regression can be implemented in any programming language used for data analysis, such as R, Python, Java, and MATLAB.

    While linear regression is estimated using the ordinary least squares method, logistic regression is estimated using the maximum likelihood estimation approach.

    Both logistic and linear regression are supervised machine learning algorithms and the two main types of regression analysis. While logistic regression is used to solve classification problems, linear regression is primarily used for regression problems.

    Going back to the example of time spent studying, linear regression and logistic regression can predict different things. Logistic regression can help predict whether the student passed an exam or not. In contrast, linear regression can predict the student’s score.

    Logistic regression assumptions

    While using logistic regression, we make a few assumptions. Assumptions are integral to correctly use logistic regression for making predictions and solving classification problems.

    The following are the main assumptions of logistic regression:

    • There is little to no multicollinearity between the independent variables.
    • The independent variables are linearly related to the log odds (log (p/(1-p)).
    • The dependent variable is dichotomous or binary; it fits into two distinct categories. This applies to only binary logistic regression, which is discussed later.
    • There are no non-meaningful variables as they might lead to errors.
    • The data sample sizes are larger, which is integral for better results.
    • There are no outliers.

    Types of logistic regression

    Logistic regression can be divided into different types based on the number of outcomes or categories of the dependent variable.

    When we think of logistic regression, we most probably think of binary logistic regression. In most parts of this article, when we referred to logistic regression, we were referring to binary logistic regression.

    The following are the three main types of logistic regression.

    Binary logistic regression

    Binary logistic regression is a statistical method used to predict the relationship between a dependent variable and an independent variable. In this method, the dependent variable is a binary variable, meaning it can take only two values (yes or no, true or false, success or failure, 0 or 1).

    A simple example of binary logistic regression is determining whether an email is spam or not.

    Multinomial logistic regression

    Multinomial logistic regression is an extension of binary logistic regression. It allows more than two categories of the outcome or dependent variable. 

    It’s similar to binary logistic regression but can have more than two possible outcomes. This means that the outcome variable can have three or more possible unordered types –  types having no quantitative significance. For example, the dependent variable may represent “Type A,” “Type B,” or “Type C”.

    Similar to binary logistic regression, multinomial logistic regression also uses maximum likelihood estimation to determine the probability. 

    For example, multinomial logistic regression can be used to study the relationship between one’s education and occupational choices. Here, the occupational choices will be the dependent variable which consists of categories of different occupations.

    Ordinal logistic regression

    Ordinal logistic regression, also known as ordinal regression, is another extension of binary logistic regression. It’s used to predict the dependent variable with three or more possible ordered types – types having quantitative significance. For example, the dependent variable may represent “Strongly Disagree,” “Disagree,” “Agree,” or “Strongly Agree”.

    It can be used to determine job performance (poor, average, or excellent) and job satisfaction (dissatisfied, satisfied, or highly satisfied).

    Advantages and disadvantages of logistic regression

    Many of the advantages and disadvantages of the logistic regression model apply to the linear regression model. One of the most significant advantages of the logistic regression model is that it doesn’t just classify but also gives probabilities.

    The following are some of the advantages of the logistic regression algorithm.

    • Simple to understand, easy to implement, and efficient to train
    • Performs well when the dataset is linearly separable
    • Good accuracy for smaller datasets
    • Doesn’t make any assumptions about the distribution of classes
    • It offers the direction of association (positive or negative)
    • Useful to find relationships between features
    • Provides well-calibrated probabilities
    • Less prone to overfitting in low dimensional datasets
    • Can be extended to multi-class classification

    However, there are numerous disadvantages to logistic regression. If there’s a feature that would separate two classes perfectly, then the model can’t be trained anymore. This is called complete separation.

    This happens mainly because the weight for that feature wouldn’t converge as the optimal weight would be infinite. However, in most cases, complete separation can be solved by defining a prior probability distribution of weights or introducing penalization of the weights.

    The following are some of the disadvantages of the logistic regression algorithm:

    • Constructs linear boundaries
    • Can lead to overfitting if the number of features is more than the number of observations
    • Predictors should have average or no multicollinearity
    • Challenging to obtain complex relationships. Algorithms like neural networks are more suitable and powerful
    • Can be used only to predict discrete functions
    • Can’t solve non-linear problems
    • Sensitive to outliers

    When life gives you options, think logistic regression

    Many might argue that humans don’t live in a binary world, unlike computers. Of course, if you’re given a slice of pizza and a hamburger, you can take a bite of both without having to choose just one. But if you take a closer look at it, a binary decision is engraved on (literally) everything. You can either choose to eat or not eat a pizza; there’s no middle ground.

    Evaluating the performance of a predictive model can be tricky if there’s a limited amount of data. For this, you can use a technique called cross-validation, which involves partitioning the available data into a training set and a test set.

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  • Everything You Need to Know – Business

    Everything You Need to Know – Business

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    Behind every purchase a consumer makes, there is a decision-making journey. 

    This is a process that everyone goes through from discovering their problem or need to their final decision to buy. As a person progresses through their buying journey, they’ll have different questions and concerns. 

    BOFU (bottom-of-funnel) content comes at the very end of the decision-making journey, or the last step before buying. In content marketing, nailing the BOFU phase is absolutely essential for getting an ROI on your content. If this step isn’t right, your prospects won’t convert into customers, and all the work that came before would be for nothing.

    We’ll take you through the types of BOFU content, how to use them, and why they’re effective. 

    What is BOFU content?

    To understand BOFU content, it’s worth taking a step back to understand the decision-making funnel. The decision-making funnel is used to describe the step-by-step process consumers go through before most purchases. You’ll find variations of this model, but here’s the simplified version:

    • The user becomes aware of their problem or desire
    • They conduct research and find out which solutions are available to them
    • Decision: they do any final research, then make their purchase

    BOFU content, as it sounds, targets the end of the decision making process. People at this stage in their journey know about their problem and some of the solutions, and are preparing to make their final decision.

    That means you can (and should) be more aggressive with your copy and calls-to-action (CTAs). With this type of content, these kind of CTAs are all appropriate:

    • Get a free trial
    • Request a demo
    • Get a quote
    • Call us now
    • Buy now
    • Add to basket

    They may not, however, be appropriate further up the funnel. It’s important to match your language and CTAs to the correct intent and stage of the funnel. 

    If someone is searching for “what is Bitcoin?”, they probably don’t want to buy Bitcoin. Not yet anyway. A better next step might be to read about the price history, or “Is Bitcoin a good investment?” to help push this reader further down the funnel.

    This is a simple concept, but an important one to grasp. If you try to use more aggressive calls-to-action (CTAs) such as ‘book a demo’ or ‘add to basket’ in content that is not BOFU, then your conversion strategy simply won’t work. It’s important to match the right CTA to the right type of content.

    How does BOFU content differ from MOFU and TOFU content?

    Naturally, since we have ‘BOFU’ content, we also have ‘MOFU’ and ‘TOFU’. Middle of funnel, and top of funnel, respectively. Here’s an explanation of what each of those are, how they differ, and some quick examples to illustrate.

    Top-of-funnel (TOFU) content 

    At the top of the funnel, you have the biggest and broadest audience. These are people who are mostly simply looking for information, and may not yet be aware of a specific problem they need to solve. They are the furthest away from a purchase, which also means you have a lot of nurturing work to do before this audience converts.

    Here are some examples of TOFU content:

    TOFU content is informational. It is the first step in making a reader aware of your brand, and aware of a problem you could solve for them where possible too.

    Middle-of-funnel (MOFU) content 

    In the middle of the funnel, people are now aware of their problem. This is known as the evaluation stage, where the person is weighing up the different solutions to solve this problem.

    With MOFU content, it’s worth remembering that it doesn’t have to be YOU who nurtures the person to this stage. There’s already plenty of people out there who’ve got to this stage, and are ready to start researching solutions.

    You now just have to make sure that you produce the right content, and get it in the right places so that your target market finds you in this stage.

    Here are some examples of MOFU content:

    Types of BOFU content

    At the bottom of the funnel, we have prospective customers who know their problem, and know some of their potential solutions. At this stage, people are figuring out the details, and finalizing their decision. Here are five types of content you can produce to convert BOFU prospects into customers.

    1. Product comparisons

    Comparing product A to product B is a commonly used tactic to weigh up the pros and cons of each product, and identify the use cases where one is superior to the other.

    Why this works

    Someone reading comparison content is past their initial research stage. They know of at least these two solutions to their problem, and now they want to know the finer details of each. It’s a great opportunity to jump in and highlight key selling points.

    Example: GetResponse vs. Constant Contact

    getresponse vs constant contact

    This example is written by GetResponse. They’re aware that anyone buying GetResponse may also be considering Constant Contact, so they’ve put in the time to write up a comparison.

    It starts by drawing a comparison of the features, before moving onto pricing. It also gives GetResponse a chance to highlight other selling points that may affect the decision-making process, such as 24/7 support.

    A secondary benefit of producing this content is that it equips the sales team to deal with any questions about this comparison, and give resources for prospects to read.

    Example: Trello vs. Meistertask vs. Toggl Plan

    trello vs meistertack

    This article that compares Trello vs. Meistertask is written by Toggl Plan, a competitor of both tools.

    It is a creative tactic that allows you to leverage the brand awareness of your competitors with SEO and content. Toggl Plan has satisfied the search intent by pitting Trello against Meistertask, but simultaneously thrown their own hat in the ring as a third alternative.

    If you’re a newcomer to the market, try doing some keyword research. Look for people who are comparing some of your better known competitors, and see if there’s an opportunity to utilize that in your content marketing.

    2. A case study

    Case studies are used to show what kind of results other people have had with your solution. Often, they’ll focus on a specific problem that you solve, and take you through the journey to the results.

    Why this works

    A case study proves that your solution works. That customer chose you ahead of all their other options. What was the result? It lets you incorporate testimonials and show off the outcomes that other customers have achieved.

    A reader may find themselves in a similar situation to the person or company in your case study. Explaining step-by-step what happened in a real-world scenario makes it easy for the prospect to understand what can happen if they buy.

    Example: Attest’s Gymshark Case Study

    gymshark

    In this first example, Attest are able to boast about how their solution has enabled Gymshark to navigate a difficult market. Gyms closing was a new challenge, and Attest could equip brands in this space with market intelligence to figure out what to do next.

    The case study walks through the journey, starting with the problems and questions Gymshark had, then following with the results. It details how Gymshark made some key discoveries, such as data proving that people were anxious about returning to the gym amidst the pandemic.

    Example 2: Shopify’s Heinz Case Study

    heinz

    Shopify’s Heinz case study neatly lays out the challenge, solution, and results from using their e-commerce platform. It includes multiple testimonials from a key stakeholder at Heinz.

    Three key details are also highlighted above the fold:

    • Previous platform
    • Food and beverage
    • Use case

    These help a reader understand quickly how relevant the case study is. The closer to their situation, the more effective it will be, which makes having case studies across a number of different industries and use cases effective.

    3. An ‘alternatives’ listicle

    Sometimes a person is aware of one solution to their problem, but it just isn’t quite right. This is usually a market leader with lots of brand awareness. In that case, it’s common to search for product alternatives to see if there’s anything else out there that is a better fit.

    Why this works

    Someone searching for an alternative is ready to buy. They already know their problem, and just need to find the right solution. It might be that the solution they already know about is too expensive. Maybe it lacks a certain feature, or isn’t great for their specific use case. This is an opportunity for you as a content marketer to step in and offer the perfect alternative.

    Example 1: Benchmade Bugout Alternatives

    alt knivesThis affiliate article starts by identifying the reasons why someone might look for an alternative to this brand (Benchmade). The primary reason is that it’s an expensive brand. However, it’s also small, and very light – so perhaps someone would look for something bigger or sturdier, too.

    For each potential drawback, the article recommends a product that fits better. This is easily replicable across many physical products, SaaS products, and brands. Like with product comparison keywords, you can do keyword research to look for similar opportunities in your niche. See if you can leverage the brand awareness of market leaders in your space.

    Example: ClickUp Alternatives

    clickup alternatives

    This example is a little different in that ClickUp is the author of this post.

    If people are searching for alternatives to your product, you have an opportunity to address their concerns. Publishing and ranking content like this makes it harder for your competitors to do so, and it allows you a chance to change the reader’s mind.

    In this article, ClickUp lists out alternatives to their product, but also identifies the drawbacks of each and how ClickUp solves them. For example, Asana is one of the prospective alternatives. ClickUp are sure to highlight the limited privacy permissions which they can offer a better solution for.

    4. A free trial or demo landing page

    A free trial or demo is the perfect way to get prospects over the line. The very last step in the decision-making funnel.

    Why this works

    People this far down the funnel already believe you can offer them the solution they’re looking for. They’re nearly ready to pull the trigger and pay, but need to see it in action first to confirm. At this point, you don’t need to beat around the bush. CTAs can be straight to the point. People are ready to try your solution. 

    Example: LinkedIn Sales Navigator Free Trial

    LI sales navigator

    LinkedIn’s Sales Navigator trial page is a great example to look at, that includes many landing page best practices.

    Take note of:

    • A clear CTA above the fold
    • A product video
    • Social proof used (high profile customer logos)
    • FAQs to address potential concerns

    The page is short and to the point. You can also see that there’s no top bar navigation. LinkedIn has reduced the number of ways someone can get distracted and leave the page without starting a trial.

    Example: Perkbox’s Request a Demo

    perkbox

    For a self-serve product, a free trial works well. For a sales-led product, however, we usually see free demonstrations being offered. There are some similarities to the LinkedIn page: a clear CTA above the fold, and lots of social proof (client logos, testimonials, reviews).

    For a demo request form like this, it’s important to consider which fields to include. Typically, more fields will result in fewer form submissions, but potentially a higher quality of lead. Fewer fields will likely result in more leads, but they could be less qualified.

    In this case, Perkbox choose to add in fields for company size and level of seniority, since these are important factors in the sales process. 

    5. Free courses and educational materials

    Lastly, another type of BOFU content is educational materials. This can come in several forms, but video courses are particularly popular and effective.

    Why this works

    By creating free educational content, you’ll be able to show someone how they can achieve their goals by using your product. Prospects can see your product in action, understand the value it provides, and see whether or not it’s what they need.

    It allows you to give value up front, and showcase your expertise on the topic. It’s also an opportunity to grab email addresses and work on nurturing leads. 

    Example: Ahrefs Academy

    ahrefs

    This free course by Ahrefs is designed for absolute beginners in SEO. It teaches the fundamentals of things like keyword research and on-page SEO, showing how Ahrefs can help you with those things along the way. Getting a trial of Ahrefs is a logical next step after this course, to start applying the learnings.

    Example: Email Marketing Basics by Sendinblue

    email marketing fundnamentals

    Similarly to the Ahrefs example, this course by Sendinblue introduces users to the basics of email marketing. And naturally, to Sendinblue’s email marketing product. It’s a low-friction way to start delivering value and educating users on how to achieve their marketing goals with the product. 

    Both Ahrefs and Sendinblue’s courses are ungated, meaning there’s no email address required to access them. You’ll find others examples such as HubSpot Academy which do require a sign up. In that case, the creator has the opportunity to send further emails and nurture leads. 

    How long do prospects stay in the BOFU stage before a buying decision?

    Typically, once a prospect reaches the BOFU stage, they are close to buying. However, the precise timelines for that final decision can vary a lot. Some purchases are simply a bigger decision than others. 

    Keep in mind these key factors which could affect the timeline:

    • B2B vs. B2C
    • Number of stakeholders involved
    • Price point
    • Business model (sales-led vs. self-service)
    • Industry

    Think about how long it would take you to decide on which car to buy. Then compare that to how long it would take you to decide which brand of chips to buy.

    The price and the business model will also impact this a lot. For example, a sales-led B2B product may have a long sales cycle. With multiple demos, multiple decision-makers involved, and budgets to allocate, this can take months whereas a lower cost B2C product could take as few as a couple of hours. 

    Conclusion 

    BOFU content is the key to ROI on your content marketing. The whole purpose of everything that comes before (TOFU and MOFU marketing) is to get prospects to this point, and convert them into customers.

    Think about the questions, thoughts, and potential objections a prospect might have by this point in their buyer’s journey. Create content that aims to satisfy those concerns. Think about producing:

    • Product comparisons
    • Case studies
    • ‘Alternatives’ listicles
    • Free trial and/or demo landing pages
    • Free courses and educational content

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  • A Big-Picture Approach to Procurement – Business

    A Big-Picture Approach to Procurement – Business

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    As businesses increasingly focus on managing costs, finding the right vendors has become a top priority for procurement teams. 

    Consequently, the practice of strategic sourcing, which focuses on maximizing value and reducing risk through vendor partnerships, is transforming the way businesses view buyer-supplier relationships.

    Spending wisely can make or break your business. Admittedly, this idea isn’t new. Indeed, for finance and procurement teams, it’s an age-old, foundational principle. However, what is new is how they decide what wise spending looks like. As businesses increasingly seek to control costs while maximizing output, the procurement department is faced with the question of how to deliver results. For many, the answer is strategic sourcing.

    Strategic sourcing is a growing practice that embraces spend analysis, data-driven supplier selection and ongoing engagement with vendor partners. 

    This guide will explore strategic sourcing in detail. To start, learn the definition of strategic sourcing as well as key principles and benefits of the practice. Then, explore scenarios that are ideal for strategic sourcing as well as who is involved in the process. Next, you’ll see a detailed plan for implementing strategic sourcing. Finally, the article will share how technology enables strategic sourcing to deliver even more value.

    What is strategic sourcing?

    Strategic sourcing is an approach to procurement that weighs the overall value delivered through a vendor relationship rather than the simple cost of the product or service provided by a vendor. The practice is a part of supply chain management and emphasizes customized solutions and strategic partnerships.

    Furthermore, this big-picture perspective acknowledges and accounts for the many complex factors that influence value. Indeed, strategic sourcing is often viewed as a cycle that includes spend analysis, supplier selection and ongoing engagement.

    In addition, an emphasis on building meaningful buyer-vendor partnerships promotes collaboration, accountability and innovation throughout the vendor lifecycle. Ultimately, this approach achieves the overall goal of strategic sourcing – to reduce costs while improving the efficiency and reliability of the supply chain.

    strategic sourcing

    Traditional procurement vs. strategic sourcing

    Procurement has always been focused on controlling costs and improving company profitability. However, the way that organizations identify and connect with suppliers and vendors has changed significantly in the last several decades.

    Historically, vendor selection has largely been based on locality and peer referrals. As technology emerged, organizations gained access to a vast and competitive global network of suppliers. 

    Now, with visibility to a full range of suppliers, procurement teams focus on controlling procurement costs by finding the vendor offering the lowest price as quickly as possible. This tactical, traditional approach is widely practiced. However, a bad experience with the wrong vendor quickly teaches that the fastest, cheapest solution isn’t always the best fit. Consequently, strategic sourcing recognizes and accounts for factors outside of cost. 

    Strategic sourcing principles

    • Decisions based on big-picture value 
    • Partnerships preferred over transactional interactions
    • Considers total cost of ownership
    • Prioritizes the most important elements of a vendor’s offer
    • Highlights the value of the procurement department

    Strategic sourcing vendor considerations

    Taking a holistic approach that weighs all aspects of the vendor relationship, the strategic sourcing process digs into anything that influences value. The subsequent vendor evaluation process then identifies which of these many considerations are most important.

    • Quality of goods and services
    • Brand reputation
    • Financial stability
    • Customizability of solution
    • Technology adoption
    • Customer satisfaction and support
    • Vendor innovation
    • Subcontracting and outsourcing
    • Reliability and responsiveness
    • Scalability and growth opportunities
    • Seasonality and stability
    • Company culture and values
    • Sustainability and diversity

    Why is strategic sourcing gaining prominence in procurement?

    The concept of strategic sourcing isn’t exactly new. In fact, it started sometime in the late 1980s or early 1990s. Initially adopted by large companies to quantify and increase vendor return on investment (ROI), today, the practice is widespread among organizations of all sizes. 

    Regardless of size, businesses now have the ability to collect and evaluate extensive data between competitors. Indeed, while much of the value of strategic sourcing is derived from it’s in-depth approach, the multitude of factors also make it difficult to attribute procurement savings directly to a single factor. However, organizations that practice strategic sourcing report a number of benefits.

    Benefits of strategic sourcing 

    Let’s discuss a few benefits of strategic sourcing and how they’re used today.

    Cost savings

    Reducing costs is always a top priority for procurement teams. In fact, a recent Deloitte survey reports that reducing costs is a top priority for 76 percent of chief procurement officers (CPOs). Accordingly, cost savings is easily the biggest benefit of strategic sourcing.

    The entire process centers around the goal of reducing spend. Starting with the first step of the strategic sourcing cycle, procurement professionals identify current costs overdue for optimization. Then, they gather data, explore stakeholder needs, research the current market and eventually issue a detailed request for proposal (RFP) to evaluate and select the ideal vendor.

    Because the selection process is more detailed than a traditional procurement project, both parties are invested in building a mutually beneficial and long-term partnership. As such, the relationship is ongoing and collaborative, ultimately resulting in reduced costs. 

    Reduced risk

    At its core, strategic sourcing is just as much about avoiding a bad partnership as it is about finding a good vendor. Again, thanks to the detailed process you can consider every factor, dig into the vendor’s experience, explore their contingency plans and set a framework for ongoing communication. The Deloitte CPO survey indicates that 75 percent of CPOs identify improved vendor information sharing as their top risk-management strategy. Luckily, collaboration is a foundational component of strategic sourcing.

    rfp360 stat

    Enhanced innovation

    With the unique focus on partnership, strategic sourcing gives you the opportunity to provide your vendors with regular feedback. Likewise, they have a platform to proactively alert you to trends, keep you competitive and collaborate on innovative initiatives.

    Longer relationship, fewer RFPs

    It won’t surprise you to know that neither your procurement department nor the vendor’s proposal team relish the prospect of opening up a new RFP. For complex, high-value sourcing projects, the process could last months or even years. By leveraging strategic sourcing, you can rest assured that you’ve accounted for every important factor, selected the right partner and you’re both ready to meet your goals together. 

    When is strategic sourcing a good option? 

    While there are undoubtedly benefits to strategic sourcing, it’s not a fit for every procurement project. After all, it is an admittedly complicated, and occasionally time-consuming, process. Some projects just don’t require that level of detail. So, it’s important to always find a balance between the value of the item being procured and the time investment required to undertake a strategic sourcing project.

    Strategic sourcing is a good fit for projects that are: 

    • High value, well defined and essential to the organization
    • Complex and highly specialized
    • High stakes and part of an organizational change initiative

    Examples of strategic sourcing scenarios: 

    • Selection of a new customer relationship management software
    • Evaluation of financial services or employee benefits
    • Engaging an architecture firm for a new construction project

    Scenarios that are better suited to alternative procurement approaches

    The detail and time required for strategic sourcing means there are some situations that simply won’t be a good fit for the process. Here are a few examples.

    • Low value, low risk procurement like finding a project management solution for a single department
    • Collecting bids to meet policy requirements when there is an existing strong preference for a particular vendor
    • A routine, transactional purchase of office supplies – try an RFP lite
    • Gathering ideas, pricing and planning a potential purchase – consider a request for information (RFI)
    • Finding the best expert to consult on a marketing initiative – try a request for qualifications (RFQ)
    • Securing the lowest price for a low-complexity purchase — Consider a request for quotation (RFQ)

    Who is involved in the strategic sourcing process?

    Within your organization, a number of people and departments will play a role in your strategic sourcing efforts. Typically, the process is owned and managed by the procurement department. Indeed, many organizations have recently created strategic sourcing manager roles to encourage specialization in this area.  

    As with most procurement projects, a number of other stakeholders will inevitably participate. For example, for a CRM procurement project, stakeholders from sales, marketing and operations would contribute to the project. In addition, legal, finance and IT are also often involved. Finally, almost every strategic sourcing project will require executive review and approval. 

    How to shift to strategic sourcing

    Adopting strategic sourcing happens one procurement project at a time. Initially, it may feel like a big change, but parts of the process will feel familiar. Indeed, strategic sourcing shares a number of common principles with traditional procurement.

    As you identify purchases or existing vendor relationships that are a good fit for the approach, you’ll move through the strategic sourcing cycle, starting with spend analysis, then supplier selection and, finally, ongoing engagement.

    Spend analysis

    Whether you’re evaluating an existing vendor relationship or undertaking a brand new procurement project, you’ll start with research and analysis. The goal of this step is to define your current state, establish needs and goals, and research solutions.

    Define your current state

    Often, procurement projects start with a need. Either a new problem has emerged or a current process just isn’t working as expected. Regardless of the circumstances, clearly defining your current circumstances sets the foundation for everything that comes next. 

    As you engage with stakeholders, ask: What is the existing process or strategy? Who are the stakeholders and decision makers? What gaps or roadblocks exist? Are there internal or external factors influencing the current state? Who will have final approval?

    Establish needs and goals

    Before you buy, you have to know what you need. Furthermore, you also must understand what you’re trying to achieve. So, gather current contracts, talk to stakeholders and brainstorm a list of solution features as well as goals for the purchase. Try to get the perspective of a range of people involved in the current process at various stages. Once you have your list, it’s time to categorize your considerations.

    Making smart purchasing decisions requires a clear understanding of which features and factors are must-haves, which are nice to have and those that are out of scope. Review your list of requirements and label each accordingly to create your scope. Each of your must-have elements should tie directly to the stated goal of the project.

    Research solutions

    With your clearly defined needs and goals in mind, it’s time to explore potential solutions. The business landscape is constantly changing, so staying abreast of new developments requires some research. Explore online resources, gather supply market analysis reports, check customer reviews and tap into your network for recommendations. Ideally, this step will give you a framework as you determine next steps.

    Strategic sourcing considers the return on investment at every step. Consequently, you must now weigh the potential benefits of engaging with a new vendor against the time and cost required to move forward. 

    Ask yourself: do the potential vendors offer a cost savings that offsets the investment required to find a new solution? Can you invest time in an existing vendor relationship to avoid undertaking a new strategic sourcing project? Do you have benchmark data to validate future ROI? Which vendors are most likely to meet your immediate needs as well as empower future growth?

    Supplier selection

    If you decide to move forward to engage with a new vendor, you’ll now focus on finding the right partnership. On the other hand, if your research and analysis indicates that the best course of action is to focus on improving your current vendor relationship, you’ll skip this step and move forward to ongoing engagement. 

    Supplier selection is where strategic sourcing significantly diverges from traditional procurement. Indeed, you’ll find that creating a strategic sourcing RFP is more detailed and complex than routine RFPs. Likewise, the proposal evaluation process is more involved.

    Writing your RFP

    When it comes to strategic sourcing, your standard RFP template offers a good start, but updates are required. Indeed, by definition, a strategic sourcing RFP should be thorough and highly specific when it comes to the background information it provides as well as the questions it asks. Remember, comprehensive RFPs are more likely to yield thoughtful, relevant proposals.

    RFP background and information

    Strategic sourcing projects are often complex and require a customized solution. So, before you ask a single question, it’s important to provide as much information as possible about your company’s background, needs and goals. This enables potential vendors to fully understand your business and tailor their proposed solution to meet your unique requirements.

    Information to include in your strategic sourcing RFP:

    • Company information: History, about us, mission, vision, industry position and glossary
    • Project information: Background information, problem/need statement, current state summary, project contact and key stakeholders
    • Solution scope: Minimum vendor qualifications, project deliverables, vendor evaluation process details and proposed implementation timeline
    • Submission information: RFP timeline and milestones including proposal due date, evaluation criteria and proposal submission instructions

    RFP questions

    After you’ve provided key company background and project information, it’s time to ask the questions that will provide you with crucial decision-making data. 

    Standard RFP questions

    Your RFP should ask a combination of straightforward questions as well as more in-depth, nuanced questions. Luckily, your RFP template provides a foundation to build upon. For example, your template likely already includes these standard sections: 

    • General company information
    • Product and service descriptions
    • Business philosophy and approach
    • Competitive differentiators
    • Customer references and case studies
    • Cost model and proposed pricing
    • Customer success policies 
    • Data security information

    Strategic sourcing RFP questions

    Beyond the standard questions, strategic sourcing RFPs also address more in-depth topics. Naturally, the specific questions you’ll ask in your RFP depend on your project priorities and goals. However, here is a selection of sample strategic sourcing sections and questions to consider.

    Customer landscape and competency 

    • Does the company already work with your competitors?
    • Can you talk directly with a customer that has a similar use case to you?
    • Ask what challenges commonly face customers like you and how the vendor participates in collaborative problem solving?

    Finances

    • Is the company publicly or privately owned? If private, who are the current investors?
    • Are earnings, assets or profit and loss statements available for review?
    • Has the company been involved in a merger, acquisition or reorganization in the last few years?

    Supply chain stability and infrastructure

    • Who are the vendor’s primary suppliers? Subcontractors? Consultants?
    • Can the company’s production accommodate an increase in volume or change in scope?
    • How robust is their supply chain and how have they overcome supply chain challenges in the past? 

    Communication policies

    • What technology do you use to communicate with customers?
    • When is customer support available and how can they be contacted?
    • What is the average response time to customer questions?

    Implementation and time to value

    • Describe your standard customer onboarding process.
    • How long does onboarding take and what is the current timeframe for implementation if you are selected?
    • What resources or preparations are required from your organization to ensure on-time implementation?

    Ethics and social responsibility

    • Request a copy of the company’s corporate and employee policies.
    • What are the company’s sustainability policies?
    • Does the organization support social responsibility and charitable giving?

    Proposal evaluation

    RFPs are a useful data collection tool. However, as a procurement professional, you must be able to use that data to find the best vendor. With hundreds of data points per proposal, simply viewing the information side-by-side won’t make the best choice obvious. Fortunately, team proposal evaluation and weighted scoring offer a way to summarize the results of any procurement project.

    Team proposal evaluation

    Once you’ve received your RFP responses, it’s time to score your proposals. Start by verifying that each proposal meets your minimum requirements. Then, score any closed-ended questions. Finally, it’s time to engage your stakeholders again to help score the more nuanced responses.

    A strategic sourcing proposal covers a lot of detailed and technical topics. So rather than having a single procurement professional score the proposals, split the scoring into several groups. For instance, the legal team should score responses to questions that deal with terms and conditions. Likewise, your IT team is best equipped to score RFP responses about software integrations and capabilities. 

    As you engage with individual and team proposal reviewers, provide a scoring guide to ensure everyone is on the same page. Indeed, you’ll find your initial spend analysis documents helpful as you create a scoring rubric.  

    Weighted scoring

    While strategic sourcing considers a vast number of factors, not each of those factors have the same importance to the business. For example, the vendor’s history of on-time delivery is likely far more important to you than their fax number. To properly account for these different priorities, many strategic sourcing teams leverage weighted scoring.

    Weighted scoring assigns each question a point value based on its importance to the business, often a scale from one to five. Then, it weighs the score based on its value to the business. For example, several questions in the capabilities section may each be worth five points while a question about must-have functionality is worth 20 points. Then, the capabilities section as a whole is worth 40 percent of the entire proposal. 

    To get the final weighted score, multiply the point total for each section by weight percentage and then add the section scores together to get a total score for each vendor. Admittedly, it sounds complicated. However, when you break it down, a simple weighted scoring calculation may look something like this:

    strategic sourcing  vendor a vs b

    Certainly, it’s the best practice to ensure as much objectivity as possible by assigning an individual score for each question. However, some businesses prefer to score and weight each section collectively to save time. As with most procurement practices, your approach will vary based on the needs of the project. 

    Final vendor selection

    Once you’ve scored your proposals, made your comparisons and called customer references, hopefully you have a clear winner. If not, consider narrowing your vendors to a short list and requesting live RFP presentations or issuing a supplementary RFP.

    When you identify the best vendor fit, begin negotiations and contracting. During this step, it’s important to define and put in writing your expectations, strategy for meeting goals and metrics for evaluating performance. Indeed, make sure your team and the vendor are on the same page when it comes to timelines, deliverables and quality control. Proactively establishing this framework will make the next step in the strategic sourcing cycle significantly easier.

    Ongoing evaluation

    When it comes to maximizing the value of your vendor relationships, continual communication is key. Far too often in traditional procurement approaches, the relationship falls into maintenance mode after the contract is signed. Your vendor goes quiet and the partnership has a ‘no news is good news’ approach.

    Unfortunately, this greatly limits the value that the vendor partnership can provide your organization. Luckily, strategic sourcing leverages ongoing evaluation through regular performance reviews, continual communication and market research.

    Regular performance reviews

    Using the metrics established during negotiations and contracting, establish regular check-ins with your vendor contact. Generally, setting quarterly reviews provides enough oversight to proactively address problems while delivering sufficient feedback for continual improvement. Before these reviews, gather feedback from the individual departments and users that regularly interact with the vendor, so you get a complete picture of how the engagement is going.

    Ask what is working well in the partnership? Is the vendor delivering the products and services as expected? How can the process become more efficient? Where is there room for improvement?

    Vendor evaluation factors

    • Quality control metrics for products and services
    • Consistent and timely delivery
    • Customer support and responsiveness
    • Quantity and cost invoice accuracy
    • Expected and actual return on investment

    If you encounter challenges outside of the regular review period, reach out to your vendor. Don’t just assume that the vendor is aware of and apathetic to the problem. An article from business.org summarizes saying: 

    “Even the most reliable supplier can occasionally slip up. Make sure they have a direct contact point at your company and conduct regular performance reviews. This will help you keep tabs on their work and make sure they’re fulfilling their end of the agreement. These reviews will also help you when it comes time to talk about contract renewal, so you know where you stand.”

    Continual communication 

    Your partnership should be a two-way street that provides feedback to your vendor and encourages them to do the same. Ideally, transparent communication enables your vendors to become a trusted part of your network.

    Ongoing collaboration between your company and your vendors encourages efficiency, identifies roadblocks and fuels creativity. Unfortunately, vendor communication doesn’t always happen spontaneously, it must be planned for and encouraged.

    An article from Entrepreneur puts it like this:

    “Not every customer wants to buddy up to suppliers, so the fact that your suppliers aren’t offering to work closely with you to improve quality, reduce defects and cut costs doesn’t necessarily mean they don’t want to. They may be under the impression that you are the reluctant one. So, if you want a tighter working relationship with suppliers, let them know.”

    Each vendor in your supply chain has a unique perspective and valuable set of experiences. Indeed, staying in sync with your providers will help you identify trends, anticipate market changes and collaborate to find creative ways to improve efficiency.

    Market research

    It’s no secret that the business landscape is continually changing and evolving. Accordingly, you must watch for potential shifts in customer demand that may impact your needs. Even when you have a positive relationship with a current vendor, it’s important to stay up to date with what their competitors can offer. 

    Fortunately, creating and maintaining vendor profiles enables you to catalog key information and updates from potential suppliers. Staying abreast of the latest developments enables you to have a backup plan if your vendor fails to meet your needs. In addition, because these profiles contain background information and differentiators, they can help speed up shortlist selection in the event that a new RFP must be issued.

    Tools for strategic sourcing

    For organizations prioritizing procurement savings, strategic sourcing software is a must. Designed to centralize and automate strategic sourcing, these platforms are typically cloud-based and collaborative, enhancing efficiency and effectiveness in strategic sourcing.

    Strategic sourcing software may include features like: 

    • Project management
    • Savings tracking 
    • Digital RFP issuing and evaluation
    • RFP template library
    • Data collection and analysis
    • Internal and external collaboration
    • Vendor relationship management
    • Category management
    • Supplier marketplace

    Make strategic sourcing work for you

    Ultimately, strategic sourcing is the process of collecting data, partnering with the best vendors and ensuring ongoing procurement value and efficiency. It goes beyond basic, transactional purchases and focuses on the big picture – how to help achieve the goals of the organization. Likewise, the vendor partnerships that result from strategic sourcing help navigate challenges as they arise while improving outcomes and reducing costs. 

    Naturally, the specifics of strategic sourcing will change from one organization to the next. However, the foundation and goals of the process remain the same. Luckily, you can start your strategic sourcing efforts one project at a time.

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