Category: Business

As more people opt for quick and engaging visuals that instantly capture attention, so does the number of brands who include short-form video content (or shorts) in their digital marketing strategies. YouTube Shorts offer eCommerce businesses a powerful platform for capitalizing on these bite-sized video consumption trends. 

The bottom line is this format is versatile for storytelling, showcasing products, and driving audience engagement, making it a critical digital marketing tool.

But is it for everyone? 

This post will answer exactly that! We will explain everything you need to know about using YouTube Shorts videos for your brand and why you absolutely should, and include some inspiring YouTube Shorts examples throughout. 

Let’s jump in!

Understanding YouTube Shorts

Similar to TikTok, YouTube Shorts cater to the growing demand for quick, engaging content and provide a new way for users to connect and share on the platform. 

Simply put, they are short-form videos on YouTube that allow creators and brands to upload content up to 60 seconds in length. And unlike regular YouTube videos, Shorts include vertical video perspectives, unlike the horizontal mode of longer videos on the platform. 

Integrated within YouTube’s vast ecosystem, Shorts are no more than a minute long and were designed to enhance visibility and reach, particularly among younger audiences. They can be viewed on YouTube’s homepage, the app’s Shorts tab, and on channel homepages.

Debuted as a beta in India in September 2020, YouTube Shorts is riding the wave of rising popularity for short-form video content. Following its success, the feature went global, with an extensive rollout that started in March 2021. Since then, brands such as Fashion Nova have been jumping onto the YouTube Shorts video bandwagon. 

YouTube Shorts Statistics 

• By the end of 2023, YouTube Shorts hit 70 billion daily views
• The average time spent per day on YouTube increased since adding Shorts, which is now around 48.7 minutes 
• The largest age group using YouTube is between 25 and 34
• YouTube has the highest average session duration compared to other platforms 

Talking about comparing…

YouTube Shorts vs. TikTok vs. Instagram Reels 

YouTube Shorts, TikTok, and Instagram Reels each carve out their niche in the growing consumption of short-form video content, targeting different audiences with various content styles. While TikTok is designed to captivate with highly creative, trend-setting content, Instagram Reels’ integration with its social media features capitalizes on an established user base. 

YouTube Shorts, on the other hand, leverages the extensive YouTube ecosystem, offering videos up to 60 seconds that benefit from YouTube’s powerful search and recommendation algorithms.

Let’s take a quick look at each of its main distinguishing features.

Gymshark is a brand that understands the advantages of YouTube Shorts and dominates with them. Take a look at the view metrics for some of their latest Shorts. 

You can read more about TikTok in the following guides: 

You can read more about Instagram Reels in the following guides: Instagram Reels for Business: Tips, Hacks, Strategies & Examples and 9+ High-Performing Instagram Ads Examples to Steal.

YouTube Shorts Benefits 

Now, let’s quickly explore the distinctive features and opportunities YouTube Shorts offer eCommerce marketers! 

A Gateway to Richer Content

Being part of YouTube means that Shorts serve as a gateway for discovering long-form videos — allowing eCommerce brands to blend short and long videos. This enables you to accommodate varying consumer interests, turning short video viewers into channel lovers, and, ultimately, product buyers. 

More Evergreen Content Opportunities

Unlike other, more momentary channels, Shorts remain accessible indefinitely, enhancing long-term visibility. This means that your evergreen Shorts will continue to attract views and other content engagements months after posting. 

Broader Audience Reach

Launching within YouTube’s massive user base, Shorts instantly tapped into a broader audience spectrum. By leveraging this reach, you can pinpoint and engage specific segments to refine your targeting strategies and ensure content consistency that resonates with existing subscribers while appealing to potential customers.

Wider Audience Engagement 

The wide demographic reach highlighted above also means that brands have the opportunity to engage with broad audiences, from younger short-form enthusiasts to older audiences that other platforms (like Reels or TikTok) may not appeal to. This enables more tailored content strategies that appeal to professionals, older generations, or specific consumer interests.

Stability and Trustworthiness

Lastly, because YouTube is a well-established platform with a longstanding reputation, Shorts is a stable and trustworthy channel for eCommerce marketing. The lower risk of unexpected restrictions or disruptions allows for a more secure investment in content development.

How to Get Started with YouTube Shorts

To start your YouTube Shorts strategy off right, you need a blend of creativity and strategy. Let’s outline the key steps you need to take to kickstart your journey, from establishing your presence with a dedicated YouTube channel to crafting and sharing your first engaging Short.  

1. Create a YouTube channel. If you haven’t already set one up, you will need a YouTube channel to post Shorts. (You can follow our guide on doing just that, here.) 
2. Download the YouTube app. You will want to ensure you have the latest version of the YouTube app to take advantage of the newest Shorts features. Use the Shorts camera in the YouTube app for easy recording, video editing, and music uploading.
3. Build your YouTube Shorts content plan. Here, you can identify critical product features, upcoming promotions, trending topics, and customer success stories that align with your brand’s narrative, and build your content plan accordingly. 
4. Record your Short. Your YouTube app’s Shorts camera allows you to record video. Ideally, record in segments, adding each part to create a complete video.   
5. Edit your video and add tags. Next, you will use the YouTube app video editing tool to trim videos, add text overlays, or incorporate music. Then craft compelling titles that include keywords and hashtags to boost discoverability.
6. Publish your Short. Finally, you are ready to publish, monitor, and engage with audience comments. 

Here’s a comprehensive video made by YouTube creators on how you can create Shorts. 

But you don’t have to start shooting everything from scratch — you can also make Shorts from your existing videos. Let’s quickly run through how. 

How to Make YouTube Shorts from Existing YouTube Videos 

To craft a YouTube Short from an existing video or livestream, whether it’s your creation or someone else’s, start by clicking the “Create” button beneath the video. Decide if you want to “Cut” a clip to maintain the original audio or create a fresh “Sound” for a personalized audio touch. 

After selecting and refining your segment:

• Hit “Next”
• Input your Short’s specifics
• Upload YouTube Shorts to share your snippet with the world

Tips for Creating Engaging YouTube Shorts

When you start creating YouTube Shorts from scratch, how do you best do it to boost engagement, brand awareness, and, ultimately, sales? In this section, we give you a few YouTube Shorts creation tips to get you started! 

1. Grab Attention from the First Second 

As with any short video content, you need to hook your audience early. For YouTube Shorts, this could mean starting with an intriguing question, a surprising fact, or a compelling visual within the first few seconds to grab attention immediately. 

You also want to use engaging thumbnails to make your content stand out and elicit that click-to-view. Cosmetic giants Lush are experts at this. Here’s one of their most popular videos that catches your attention right away.

2. Get Creative with Music and Effects 

Other important elements of Shorts are royalty-free background music and sound effects. While it is essential to assume some viewers mute tvideos, you still want to make sure you create a vibe for those who do. A catchy tune or comedic sounds can go a long way to doing just that. 

Here’s a YouTube Shorts example from Warby Parker’s YouTube channel. 

3. Jump on Trends and Challenges

Unlike longer videos, trends and viral content aren’t in short supply with short-form content! 

Participating in trends is a good way to get traction with your Shorts. To keep your content plans relevant, you need to stay up to date with the latest trends and challenges on YouTube and other social media platforms. 

You could create your own YouTube Shorts challenge to encourage community participation and sharing. Here are some of the more popular challenges: 

• Ghost pepper challenges
• Pancake art challenges
• Random exercise challenges 
• Try-not-to-laugh challenges
• 7-second challenges
• 24-hour challenges 
• The-floor-is-lava challenges
• Blindfolded makeup challenges

You might also want to take advantage of some of YouTube Shorts’ newer features, such as COLLAB, to collaborate with creators, like-minded brands, and your customers for challenges. 

4. Add Text for Clarity 

Another Short video tip is using text and graphics for effect. 

Text overlays are vital to emphasize important points or add context where audio can’t be used. In addition, including captions will make your YouTube Shorts more accessible to a wider audience, including those who prefer to watch videos without sound or those who are hearing impaired.

In fact, more and more people report that they prefer videos with subtitles, with captions said to dramatically increase video engagement. One study found that as many as 80% of viewers are more likely to watch a video to the end if it has subtitles! 

5. End with a Strong CTA 

Like with any marketing element, you need a strong CTA to encourage interaction. You want your YouTube Shorts video to be clear, directing your viewers to like, comment, share, or subscribe, fostering engagement and building your community.

And the more engagement and response your Shorts get, the more significantly you can boost conversions. They also help you gain invaluable insights into specific actions that you can use for other marketing elements. Here are a few examples to get you started. 

Sometimes, your message needs just a few more words! Take Sephora, for instance. Even though this whole YouTube Short is a call to action to recycle beauty product containers, it still ends with a CTA note. 

6. Experiment, Analyze, and Tweak 

When it comes to any successful content marketing strategy, you want to continuously test, tweak, and analyze. This means experimenting with different content types and analyzing their results to mine for vital insights you can use to drive content ideas further down the line. 

And don’t be afraid to experiment with different styles or types of content to see what resonates most with your audience. Then regularly review your Shorts’ performance analytics to understand viewer preferences and refine your content strategy. 

Hacks to Maximize Your YouTube Shorts’ Reach

Expanding your YouTube Shorts’ visibility is crucial to broadening your content’s reach and ensuring your Shorts captivate as many viewers as possible. Let’s look at some of the top ways you can do that. 

1. Invest in a YouTube Video PPC Strategy 

Last year, YouTube started rolling out Shorts placement for video action campaigns. They then started showing product feeds connected to these campaigns in Shorts, making them shoppable. This means that if you’re running YouTube video campaigns, you should already be seeing Shorts placement. 

If you haven’t yet invested in eCommerce YouTube ads, now is the time. Not only to improve the reach of your Shorts, but to help boost traffic to your online store. 

Looking to scale your business?

Get Google Ads hacks for free (used by our 500K customers)

2. Optimize for Discoverability

You can put all the effort you can muster into your Shorts, but if you aren’t reaching the right viewer, you’re missing the mark. This is where discoverability optimization comes in. Here are a few ways you can do that: 

1. Exploit keyword-rich titles and descriptions to improve visibility in YouTube search results 
2. Add trending and relevant hashtags to increase the chances of being discovered by a wider audience 
3. Interact with comments to boost engagement metrics and improve chances for YouTube’s algorithm to recommend your content 
4. Leverage YouTube Stories and community posts to promote Shorts to existing subscribers and increase content engagement 
5. Partner with other brands and YouTube creators to tap into new niche audiences 

Talking about creators…

3. Collaborate with Creators or Brands 

Another way to get more eyes on your content is by collaborating with other people or brands. 

By working with creators or influencers, or partnering with other brands, you can tap into their audiences and introduce your brand channel to potential new subscribers — and shoppers. Here are a few ideas to get you started: 

1. Host a joint giveaway or contest with creators or brands whereby viewers need to engage with both channels 
2. Leverage cross-promotion on social media to share behind-the-scenes content, teaser clips, or to simply promote the collaboration across your Instagram, Twitter, and Facebook profiles to maximize reach 
3. Create a collaborative series of Shorts where you tackle topics, challenges, or create content that is relevant to both audiences

Let’s say you’re a fashion brand. You could partner with a beauty influencer to create a series of “Complete the Look” Shorts. In each Short, you could feature the influencer styling a fashion item from your store with their makeup tips. This type of collaboration not only helps you reach new audiences. It helps you showcase your products while adding real value for your audiences and establishing your brand as an expert in fashion. 

4. Leverage the Power of Thumbnails

YouTube Shorts thumbnails may be small, but they are often the first thing a potential viewer will see. By creating captivating custom thumbnails for your Shorts, you are able to grab attention on and off the social media platform. 

They also play a role in ensuring that the Shorts page of your channel is inviting and that all your content is easy to discover. Therefore, you want to ensure that you maintain a consistent style or branding across your thumbnails to make your content instantly recognizable and enticing for the view-click. Additionally, you may want to consider incorporating brief, compelling text overlays that tease the content of the Short or highlight the value within, further encouraging clicks. 

Or not! The best approach is one that perfectly fits your specific brand, vibe, and market. Take a look at eCommerce giant ASOS’s Shorts feed, which does just that. 

5. Consistent Posting Schedule

Like with any marketing platform, consistency is key! By maintaining a regular posting schedule, you keep your audience engaged. The more engaged your audience is, the more likely YouTube will recommend your content. 

If your aim is to quickly boost your account and catch that viral wave with your short-form videos, you may want to consider ramping up your posting frequency to two or three times daily. This may sound daunting — but you can start by repurposing your existing video content instead of starting from scratch. 

According to SocialPilot, the best times to post on YouTube are displayed in the chart below. Of course, these are average estimates, so you should test and tweak optimum post times for your specific brand and audience. 

Strategies for Driving Product Sales with YouTube Shorts

This part of our guide is dedicated to outlining actionable strategies that can turn your YouTube viewers into customers. This means being able to capitalize on the dynamic and engaging nature of Shorts to boost your eCommerce success. 

Through a mix of affiliate marketing, user-generated content, product teasers, and interactive demonstrations, these strategies are designed to not only capture attention but also to convert that attention into sales. 

Let’s dive in. 

1. Leverage Influencer Marketing and Affiliate Programs

Influencer marketing and affiliate programs can be good ways to drive product sales from YouTube Shorts. Both enable you to leverage the vast reach and credibility of established online personalities and platforms.

By partnering with YouTube influencers with niche audiences, you are tapping into their fans — fans who will trust their product recommendations. This also enables you to include more authentic endorsements in YouTube Shorts, which helps build product and brand trust while increasing visibility. 

Similarly, offering an affiliate program for your brand helps you to promote products. Because it’s permanence-based, this can be a cost-effective strategy. It also gives you additional (valuable) data that you can use to drive our marketing strategies beyond just YouTube optimization. 

Let’s say you’re an eCommerce brand specializing in sustainable fashion. You could collaborate with eco-conscious fashion influencers on YouTube to create Shorts videos showcasing how to style different pieces for different occasions. These influencers could then use your affiliate links in their video descriptions, directing viewers to purchase the outfits directly from the brand’s website. 

This approach not only highlights the versatility and appeal of the products but also aligns the brand with the values of both the influencer and their audience, driving targeted traffic and increasing sales conversions.

New to affiliate marketing? Here’s a guide from Shopify. 

2. Showcase User-Generated Content and Testimonials

Another way you can use YouTube Shorts to push product conversions is with UGC (user-generated content) or testimonials. This doesn’t have to be a huge undertaking. 

For instance, you could encourage your customers to share their own Shorts using your products with a specific hashtag. Perhaps for an incentive like a promotional code or a free gift? Then, you could select the best Shorts to feature on your channel and/or offer further incentives for sharing. 

This not only provides social proof but also builds a community around your brand, increasing trust and driving sales.

For example, let’s say you are a beauty eCommerce store launching a new line of skincare products. 

You could run a campaign asking your followers to post YouTube Shorts of their skincare routines featuring these new products, using a dedicated hashtag. You could offer a prize for the best video, like a month’s supply of skincare. You could then highlight these UGC Shorts on your feed and on other social media platforms to showcase real-life examples of product effectiveness, while cultivating brand loyalty and building trust.  

3. Use YouTube Shorts for Product Teasers and Countdown Launches

Short-form video content is a great element you can use to create anticipation and excitement around new product launches. By sharing teaser Shorts that highlight key features or unique selling points, it can be extremely effective. 

Even better if it’s paired with a countdown series leading up to a launch date, which encourages viewers to subscribe and stay tuned for the reveal. This strategy not only generates buzz on your channel but primes audiences to cash out their carts on release day. 

Let’s imagine that you are a tech eCommerce brand gearing up to launch your latest smartwatch. In the weeks leading up to the launch, you could release a series of YouTube Shorts, each unveiling a sneak peek of a smartwatch feature — like its sleek design, innovative health tracking, or seamless connectivity — without showing the full product. Each Short ends with a countdown timer to the launch, sparking curiosity and excitement. 

In theory, by the time the final Short reveals the launch, the audience is already primed and eager to purchase, significantly boosting sales from the get-go.

4. Plan Interactive Product Demonstrations and Tutorials

The last must-try strategy for driving product sales with YouTube Shorts is interactive product demos and tutorials. Short video content is a great medium to offer engaging tutorials or how-to guides that demonstrate the practical uses and benefits of your products. 

To capitalize on every second and ensure and ensure your videos are relatable, you want to highlight the problems your products solve in everyday scenarios. Then, you could add interactive elements — such as asking viewers to comment on how they would use the product or what they want to see next — to enhance engagement and drive interest toward making a purchase.

This is a Shorts strategy that Selena Gomez’s makeup brand, Rare Beauty, is crushing with. Sure, it helps that she hosts the tutorials herself, but the concept is still easily duplicatable on a smaller scale. 

Final Thoughts: Measuring Success with YouTube Shorts

No matter which YouTube Shorts tip or hack you use for your short video planning, the metrics will matter in a big way. Not just for the success of your Shorts but your overall video marketing strategy. This goes for all your Shorts, even those posted in collaboration with your YouTube Shorts creators.

From the time you (or a short creator) uploads YouTube Shorts to the app, you should be keeping track of their performance. Metrics such as view count, likes, comments, shares, and subscriber growth provide insights into how effectively Shorts are resonating with audiences. Additionally, monitoring the watch time and audience retention rates can help identify which short video content keeps viewers engaged the longest. 

This data-driven approach ensures continuous improvement and maximization of the Shorts platform’s potential for growth and engagement.

Let’s say you’re a small business specializing in eco-friendly home goods, and you decide to leverage YouTube Shorts to showcase your products and share sustainability tips. After posting consistently for a month and seeing good daily metrics, you start analyzing your Shorts’ performance to gauge success.

You pinpoint a short, 30-second clip demonstrating a zero-waste kitchen hack using one of your products that significantly outperforms others, with 20,000 views, 500 likes, and numerous shares and comments praising it. The analytics also show that this Short led to a 10% increase in channel subscribers and drove a noticeable spike in traffic to your product page linked in the video description.

The success metrics — high view count, engagement rates, and direct impact on subscriber growth and website traffic — highlight the effectiveness of this Short, which you can, yes, use to plan content ideas in the future. But it’s useful for more than just content planning. Successful engagement and view feedback can also drive our product development and marketing strategies on other platforms — such as your Google video ads!! 

 

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As more people opt for quick and engaging visuals that instantly capture attention, so does the number of brands who include short-form video content (or shorts) in their digital marketing strategies. YouTube Shorts offer eCommerce businesses a powerful platform for capitalizing on these bite-sized video consumption trends. 

The bottom line is this format is versatile for storytelling, showcasing products, and driving audience engagement, making it a critical digital marketing tool.

But is it for everyone? 

This post will answer exactly that! We will explain everything you need to know about using YouTube Shorts videos for your brand and why you absolutely should, and include some inspiring YouTube Shorts examples throughout. 

Let’s jump in!

Understanding YouTube Shorts

Similar to TikTok, YouTube Shorts cater to the growing demand for quick, engaging content and provide a new way for users to connect and share on the platform. 

Simply put, they are short-form videos on YouTube that allow creators and brands to upload content up to 60 seconds in length. And unlike regular YouTube videos, Shorts include vertical video perspectives, unlike the horizontal mode of longer videos on the platform. 

Integrated within YouTube’s vast ecosystem, Shorts are no more than a minute long and were designed to enhance visibility and reach, particularly among younger audiences. They can be viewed on YouTube’s homepage, the app’s Shorts tab, and on channel homepages.

Debuted as a beta in India in September 2020, YouTube Shorts is riding the wave of rising popularity for short-form video content. Following its success, the feature went global, with an extensive rollout that started in March 2021. Since then, brands such as Fashion Nova have been jumping onto the YouTube Shorts video bandwagon. 

YouTube Shorts Statistics 

• By the end of 2023, YouTube Shorts hit 70 billion daily views
• The average time spent per day on YouTube increased since adding Shorts, which is now around 48.7 minutes 
• The largest age group using YouTube is between 25 and 34
• YouTube has the highest average session duration compared to other platforms 

Talking about comparing…

YouTube Shorts vs. TikTok vs. Instagram Reels 

YouTube Shorts, TikTok, and Instagram Reels each carve out their niche in the growing consumption of short-form video content, targeting different audiences with various content styles. While TikTok is designed to captivate with highly creative, trend-setting content, Instagram Reels’ integration with its social media features capitalizes on an established user base. 

YouTube Shorts, on the other hand, leverages the extensive YouTube ecosystem, offering videos up to 60 seconds that benefit from YouTube’s powerful search and recommendation algorithms.

Let’s take a quick look at each of its main distinguishing features.

Gymshark is a brand that understands the advantages of YouTube Shorts and dominates with them. Take a look at the view metrics for some of their latest Shorts. 

You can read more about TikTok in the following guides: 

You can read more about Instagram Reels in the following guides: Instagram Reels for Business: Tips, Hacks, Strategies & Examples and 9+ High-Performing Instagram Ads Examples to Steal.

YouTube Shorts Benefits 

Now, let’s quickly explore the distinctive features and opportunities YouTube Shorts offer eCommerce marketers! 

A Gateway to Richer Content

Being part of YouTube means that Shorts serve as a gateway for discovering long-form videos — allowing eCommerce brands to blend short and long videos. This enables you to accommodate varying consumer interests, turning short video viewers into channel lovers, and, ultimately, product buyers. 

More Evergreen Content Opportunities

Unlike other, more momentary channels, Shorts remain accessible indefinitely, enhancing long-term visibility. This means that your evergreen Shorts will continue to attract views and other content engagements months after posting. 

Broader Audience Reach

Launching within YouTube’s massive user base, Shorts instantly tapped into a broader audience spectrum. By leveraging this reach, you can pinpoint and engage specific segments to refine your targeting strategies and ensure content consistency that resonates with existing subscribers while appealing to potential customers.

Wider Audience Engagement 

The wide demographic reach highlighted above also means that brands have the opportunity to engage with broad audiences, from younger short-form enthusiasts to older audiences that other platforms (like Reels or TikTok) may not appeal to. This enables more tailored content strategies that appeal to professionals, older generations, or specific consumer interests.

Stability and Trustworthiness

Lastly, because YouTube is a well-established platform with a longstanding reputation, Shorts is a stable and trustworthy channel for eCommerce marketing. The lower risk of unexpected restrictions or disruptions allows for a more secure investment in content development.

How to Get Started with YouTube Shorts

To start your YouTube Shorts strategy off right, you need a blend of creativity and strategy. Let’s outline the key steps you need to take to kickstart your journey, from establishing your presence with a dedicated YouTube channel to crafting and sharing your first engaging Short.  

1. Create a YouTube channel. If you haven’t already set one up, you will need a YouTube channel to post Shorts. (You can follow our guide on doing just that, here.) 
2. Download the YouTube app. You will want to ensure you have the latest version of the YouTube app to take advantage of the newest Shorts features. Use the Shorts camera in the YouTube app for easy recording, video editing, and music uploading.
3. Build your YouTube Shorts content plan. Here, you can identify critical product features, upcoming promotions, trending topics, and customer success stories that align with your brand’s narrative, and build your content plan accordingly. 
4. Record your Short. Your YouTube app’s Shorts camera allows you to record video. Ideally, record in segments, adding each part to create a complete video.   
5. Edit your video and add tags. Next, you will use the YouTube app video editing tool to trim videos, add text overlays, or incorporate music. Then craft compelling titles that include keywords and hashtags to boost discoverability.
6. Publish your Short. Finally, you are ready to publish, monitor, and engage with audience comments. 

Here’s a comprehensive video made by YouTube creators on how you can create Shorts. 

But you don’t have to start shooting everything from scratch — you can also make Shorts from your existing videos. Let’s quickly run through how. 

How to Make YouTube Shorts from Existing YouTube Videos 

To craft a YouTube Short from an existing video or livestream, whether it’s your creation or someone else’s, start by clicking the “Create” button beneath the video. Decide if you want to “Cut” a clip to maintain the original audio or create a fresh “Sound” for a personalized audio touch. 

After selecting and refining your segment:

• Hit “Next”
• Input your Short’s specifics
• Upload YouTube Shorts to share your snippet with the world

Tips for Creating Engaging YouTube Shorts

When you start creating YouTube Shorts from scratch, how do you best do it to boost engagement, brand awareness, and, ultimately, sales? In this section, we give you a few YouTube Shorts creation tips to get you started! 

1. Grab Attention from the First Second 

As with any short video content, you need to hook your audience early. For YouTube Shorts, this could mean starting with an intriguing question, a surprising fact, or a compelling visual within the first few seconds to grab attention immediately. 

You also want to use engaging thumbnails to make your content stand out and elicit that click-to-view. Cosmetic giants Lush are experts at this. Here’s one of their most popular videos that catches your attention right away.

2. Get Creative with Music and Effects 

Other important elements of Shorts are royalty-free background music and sound effects. While it is essential to assume some viewers mute tvideos, you still want to make sure you create a vibe for those who do. A catchy tune or comedic sounds can go a long way to doing just that. 

Here’s a YouTube Shorts example from Warby Parker’s YouTube channel. 

3. Jump on Trends and Challenges

Unlike longer videos, trends and viral content aren’t in short supply with short-form content! 

Participating in trends is a good way to get traction with your Shorts. To keep your content plans relevant, you need to stay up to date with the latest trends and challenges on YouTube and other social media platforms. 

You could create your own YouTube Shorts challenge to encourage community participation and sharing. Here are some of the more popular challenges: 

• Ghost pepper challenges
• Pancake art challenges
• Random exercise challenges 
• Try-not-to-laugh challenges
• 7-second challenges
• 24-hour challenges 
• The-floor-is-lava challenges
• Blindfolded makeup challenges

You might also want to take advantage of some of YouTube Shorts’ newer features, such as COLLAB, to collaborate with creators, like-minded brands, and your customers for challenges. 

4. Add Text for Clarity 

Another Short video tip is using text and graphics for effect. 

Text overlays are vital to emphasize important points or add context where audio can’t be used. In addition, including captions will make your YouTube Shorts more accessible to a wider audience, including those who prefer to watch videos without sound or those who are hearing impaired.

In fact, more and more people report that they prefer videos with subtitles, with captions said to dramatically increase video engagement. One study found that as many as 80% of viewers are more likely to watch a video to the end if it has subtitles! 

5. End with a Strong CTA 

Like with any marketing element, you need a strong CTA to encourage interaction. You want your YouTube Shorts video to be clear, directing your viewers to like, comment, share, or subscribe, fostering engagement and building your community.

And the more engagement and response your Shorts get, the more significantly you can boost conversions. They also help you gain invaluable insights into specific actions that you can use for other marketing elements. Here are a few examples to get you started. 

Sometimes, your message needs just a few more words! Take Sephora, for instance. Even though this whole YouTube Short is a call to action to recycle beauty product containers, it still ends with a CTA note. 

6. Experiment, Analyze, and Tweak 

When it comes to any successful content marketing strategy, you want to continuously test, tweak, and analyze. This means experimenting with different content types and analyzing their results to mine for vital insights you can use to drive content ideas further down the line. 

And don’t be afraid to experiment with different styles or types of content to see what resonates most with your audience. Then regularly review your Shorts’ performance analytics to understand viewer preferences and refine your content strategy. 

Hacks to Maximize Your YouTube Shorts’ Reach

Expanding your YouTube Shorts’ visibility is crucial to broadening your content’s reach and ensuring your Shorts captivate as many viewers as possible. Let’s look at some of the top ways you can do that. 

1. Invest in a YouTube Video PPC Strategy 

Last year, YouTube started rolling out Shorts placement for video action campaigns. They then started showing product feeds connected to these campaigns in Shorts, making them shoppable. This means that if you’re running YouTube video campaigns, you should already be seeing Shorts placement. 

If you haven’t yet invested in eCommerce YouTube ads, now is the time. Not only to improve the reach of your Shorts, but to help boost traffic to your online store. 

Looking to scale your business?

Get Google Ads hacks for free (used by our 500K customers)

2. Optimize for Discoverability

You can put all the effort you can muster into your Shorts, but if you aren’t reaching the right viewer, you’re missing the mark. This is where discoverability optimization comes in. Here are a few ways you can do that: 

1. Exploit keyword-rich titles and descriptions to improve visibility in YouTube search results 
2. Add trending and relevant hashtags to increase the chances of being discovered by a wider audience 
3. Interact with comments to boost engagement metrics and improve chances for YouTube’s algorithm to recommend your content 
4. Leverage YouTube Stories and community posts to promote Shorts to existing subscribers and increase content engagement 
5. Partner with other brands and YouTube creators to tap into new niche audiences 

Talking about creators…

3. Collaborate with Creators or Brands 

Another way to get more eyes on your content is by collaborating with other people or brands. 

By working with creators or influencers, or partnering with other brands, you can tap into their audiences and introduce your brand channel to potential new subscribers — and shoppers. Here are a few ideas to get you started: 

1. Host a joint giveaway or contest with creators or brands whereby viewers need to engage with both channels 
2. Leverage cross-promotion on social media to share behind-the-scenes content, teaser clips, or to simply promote the collaboration across your Instagram, Twitter, and Facebook profiles to maximize reach 
3. Create a collaborative series of Shorts where you tackle topics, challenges, or create content that is relevant to both audiences

Let’s say you’re a fashion brand. You could partner with a beauty influencer to create a series of “Complete the Look” Shorts. In each Short, you could feature the influencer styling a fashion item from your store with their makeup tips. This type of collaboration not only helps you reach new audiences. It helps you showcase your products while adding real value for your audiences and establishing your brand as an expert in fashion. 

4. Leverage the Power of Thumbnails

YouTube Shorts thumbnails may be small, but they are often the first thing a potential viewer will see. By creating captivating custom thumbnails for your Shorts, you are able to grab attention on and off the social media platform. 

They also play a role in ensuring that the Shorts page of your channel is inviting and that all your content is easy to discover. Therefore, you want to ensure that you maintain a consistent style or branding across your thumbnails to make your content instantly recognizable and enticing for the view-click. Additionally, you may want to consider incorporating brief, compelling text overlays that tease the content of the Short or highlight the value within, further encouraging clicks. 

Or not! The best approach is one that perfectly fits your specific brand, vibe, and market. Take a look at eCommerce giant ASOS’s Shorts feed, which does just that. 

5. Consistent Posting Schedule

Like with any marketing platform, consistency is key! By maintaining a regular posting schedule, you keep your audience engaged. The more engaged your audience is, the more likely YouTube will recommend your content. 

If your aim is to quickly boost your account and catch that viral wave with your short-form videos, you may want to consider ramping up your posting frequency to two or three times daily. This may sound daunting — but you can start by repurposing your existing video content instead of starting from scratch. 

According to SocialPilot, the best times to post on YouTube are displayed in the chart below. Of course, these are average estimates, so you should test and tweak optimum post times for your specific brand and audience. 

Strategies for Driving Product Sales with YouTube Shorts

This part of our guide is dedicated to outlining actionable strategies that can turn your YouTube viewers into customers. This means being able to capitalize on the dynamic and engaging nature of Shorts to boost your eCommerce success. 

Through a mix of affiliate marketing, user-generated content, product teasers, and interactive demonstrations, these strategies are designed to not only capture attention but also to convert that attention into sales. 

Let’s dive in. 

1. Leverage Influencer Marketing and Affiliate Programs

Influencer marketing and affiliate programs can be good ways to drive product sales from YouTube Shorts. Both enable you to leverage the vast reach and credibility of established online personalities and platforms.

By partnering with YouTube influencers with niche audiences, you are tapping into their fans — fans who will trust their product recommendations. This also enables you to include more authentic endorsements in YouTube Shorts, which helps build product and brand trust while increasing visibility. 

Similarly, offering an affiliate program for your brand helps you to promote products. Because it’s permanence-based, this can be a cost-effective strategy. It also gives you additional (valuable) data that you can use to drive our marketing strategies beyond just YouTube optimization. 

Let’s say you’re an eCommerce brand specializing in sustainable fashion. You could collaborate with eco-conscious fashion influencers on YouTube to create Shorts videos showcasing how to style different pieces for different occasions. These influencers could then use your affiliate links in their video descriptions, directing viewers to purchase the outfits directly from the brand’s website. 

This approach not only highlights the versatility and appeal of the products but also aligns the brand with the values of both the influencer and their audience, driving targeted traffic and increasing sales conversions.

New to affiliate marketing? Here’s a guide from Shopify. 

2. Showcase User-Generated Content and Testimonials

Another way you can use YouTube Shorts to push product conversions is with UGC (user-generated content) or testimonials. This doesn’t have to be a huge undertaking. 

For instance, you could encourage your customers to share their own Shorts using your products with a specific hashtag. Perhaps for an incentive like a promotional code or a free gift? Then, you could select the best Shorts to feature on your channel and/or offer further incentives for sharing. 

This not only provides social proof but also builds a community around your brand, increasing trust and driving sales.

For example, let’s say you are a beauty eCommerce store launching a new line of skincare products. 

You could run a campaign asking your followers to post YouTube Shorts of their skincare routines featuring these new products, using a dedicated hashtag. You could offer a prize for the best video, like a month’s supply of skincare. You could then highlight these UGC Shorts on your feed and on other social media platforms to showcase real-life examples of product effectiveness, while cultivating brand loyalty and building trust.  

3. Use YouTube Shorts for Product Teasers and Countdown Launches

Short-form video content is a great element you can use to create anticipation and excitement around new product launches. By sharing teaser Shorts that highlight key features or unique selling points, it can be extremely effective. 

Even better if it’s paired with a countdown series leading up to a launch date, which encourages viewers to subscribe and stay tuned for the reveal. This strategy not only generates buzz on your channel but primes audiences to cash out their carts on release day. 

Let’s imagine that you are a tech eCommerce brand gearing up to launch your latest smartwatch. In the weeks leading up to the launch, you could release a series of YouTube Shorts, each unveiling a sneak peek of a smartwatch feature — like its sleek design, innovative health tracking, or seamless connectivity — without showing the full product. Each Short ends with a countdown timer to the launch, sparking curiosity and excitement. 

In theory, by the time the final Short reveals the launch, the audience is already primed and eager to purchase, significantly boosting sales from the get-go.

4. Plan Interactive Product Demonstrations and Tutorials

The last must-try strategy for driving product sales with YouTube Shorts is interactive product demos and tutorials. Short video content is a great medium to offer engaging tutorials or how-to guides that demonstrate the practical uses and benefits of your products. 

To capitalize on every second and ensure and ensure your videos are relatable, you want to highlight the problems your products solve in everyday scenarios. Then, you could add interactive elements — such as asking viewers to comment on how they would use the product or what they want to see next — to enhance engagement and drive interest toward making a purchase.

This is a Shorts strategy that Selena Gomez’s makeup brand, Rare Beauty, is crushing with. Sure, it helps that she hosts the tutorials herself, but the concept is still easily duplicatable on a smaller scale. 

Final Thoughts: Measuring Success with YouTube Shorts

No matter which YouTube Shorts tip or hack you use for your short video planning, the metrics will matter in a big way. Not just for the success of your Shorts but your overall video marketing strategy. This goes for all your Shorts, even those posted in collaboration with your YouTube Shorts creators.

From the time you (or a short creator) uploads YouTube Shorts to the app, you should be keeping track of their performance. Metrics such as view count, likes, comments, shares, and subscriber growth provide insights into how effectively Shorts are resonating with audiences. Additionally, monitoring the watch time and audience retention rates can help identify which short video content keeps viewers engaged the longest. 

This data-driven approach ensures continuous improvement and maximization of the Shorts platform’s potential for growth and engagement.

Let’s say you’re a small business specializing in eco-friendly home goods, and you decide to leverage YouTube Shorts to showcase your products and share sustainability tips. After posting consistently for a month and seeing good daily metrics, you start analyzing your Shorts’ performance to gauge success.

You pinpoint a short, 30-second clip demonstrating a zero-waste kitchen hack using one of your products that significantly outperforms others, with 20,000 views, 500 likes, and numerous shares and comments praising it. The analytics also show that this Short led to a 10% increase in channel subscribers and drove a noticeable spike in traffic to your product page linked in the video description.

The success metrics — high view count, engagement rates, and direct impact on subscriber growth and website traffic — highlight the effectiveness of this Short, which you can, yes, use to plan content ideas in the future. But it’s useful for more than just content planning. Successful engagement and view feedback can also drive our product development and marketing strategies on other platforms — such as your Google video ads!! 

 

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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.

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. 

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. 

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. 

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.

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. 

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. 

The Integration Hub in my.G2

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. 

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.

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. 

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.” 

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. 

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.”

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.” 

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. 

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. 

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). 

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. 

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.

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.

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.

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.

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 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. 

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.

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 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.

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.

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.

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.

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 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)).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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)).

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.

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.

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.

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.