October 18, 2018

A Brief Report on Reporting

Why Incremental Lift is Key To Product Page Analytics

When it comes to optimizing product page performance, you need more than just sales numbers and ROI on marketing initiatives. You need real insights based on data gathered from reliable analytics. You need a platform that calculates incremental lift.

Incremental lift is much more valuable than metrics that suggest correlation (like revenue or conversion rates) because it utilizes A/B testing to isolate what effect a particular marketing initiative had on your product page performance.

Using incremental lift you can also calculate incremental revenue. Incremental revenue tells you exactly how much additional revenue is attributable because of incremental lift. It provides clear evidence and attribution of the returns for your business from your marketing investment.

That’s why SellPoints exclusive reporting gives you easily digestible insights such as incremental lift to better understand what your customers want and how you can reach them more effectively. This is product page optimization made simple.

What’s missing from my sales numbers?

Sales numbers don’t make a complete connection between marketing content and revenue. They don’t give you a roadmap for how to improve your sales funnel for specific products because they don’t tell you who is buying your products and why. Incremental lift does.

What if your product detail page could analyze engagement and sales for you?

If every engagement with the content on your product pages was analyzed in a dashboard that calculated key data points like incremental lift and page traffic, wouldn’t that be something you’d be interested in? That data could be the basis of a product page optimization strategy.

You’ve heard why sales numbers aren’t enough, but now you need the details on incremental lift. Let’s dive into the current state of e-commerce analytics, define incremental lift and incremental revenue, and tell you why A/B testing is the key to unlocking product page optimization.

What is the state of e-commerce analytics?

Brands live in a sea of reporting that claims to provide the best information about where to deploy your time and budgets. Good brands are always on the lookout for better data that can give them an edge on their competitors, but determining whose data is reliable is a challenge. Before deciding on a product page analytics platform, make sure you understand what metrics are out there and how to use them.

Return on Investment

Return on Investment (ROI) is a common metric that is favored for its ease of use. It’s calculated using just two numbers: revenue and cost. Take how much you made and divide it by how much it cost to generate that revenue. That is your ROI.

While simplistic, this calculation lacks the specificity to tell you what part of your costs actually drove sales. With ROI you can begin to see correlation, but not causation. You may find that your ROI when you add instructional videos to your product detail pages is 4:1, but how much of that revenue is attributable to the video itself?

ROI is greatly affected by changes unrelated to your marketing efforts because it doesn’t go deep enough into measuring the effectiveness of your specific initiatives.  In order to determine if a new product page layout increases revenue for the product, you need to be able to isolate all other factors in your purchase funnel. In theory this is an easy equation to calculate, but in a real-life scenario it is nearly impossible.  

Think about how many ROI data points you would need to isolate in order to account for things like seasonality, paid impressions, and external campaigns. Creating appropriate hurdles rates (the minimum ROI to consider a marketing effort successful) using ROI may take a year or more. That is too long to wait.

Conversion Rate

Looking at conversion rate is another popular type of e-commerce analytics. This metric focuses specifically on product page performance. Conversion rate is a measure of the percentage of product page visitors who performed a desired action.

In e-commerce, a commonly referenced conversion rate is the add to cart rate. The goal of e-commerce retailers is to get shoppers to add products to their cart and ultimately make a purchase. When it comes to product page optimization, the conversion rate we want to focus on is add to cart rate.

However, add to cart rate has a similar issue to return on investment: how do you figure out what marketing initiatives are making it change? Your product detail page analytics dashboard may tell you the add to cart rate on a particular product is 10% this month and 8% last month, but how do you use that info?

A time series for conversion rate doesn’t tell the whole story. Conversion rate only analyzes the final action: did users add to cart or not? It doesn’t look at what motivated users to add to cart. It does not ask questions to figure out whether that user would have made a purchase in the absence of your marketing initiative (or, if you substituted a different initiative).

Product page optimization is more effective when your data is more granular. Different consumers are motivated by different types of content. Understanding these consumer preferences and how to serve them appropriate content is a core competency that an insightful e-commerce analytics platform needs to provide.

Does add to cart rate go up or down when you change your brand photography after a new photoshoot? Does a product video for a riding lawn mower getting more engagement from consumers in New York or Iowa?

A map layer of the United States with geographic hotspots representing user e-commerce engagement in a reporting dashboard.

See where your consumers are coming from with geographic reporting.


If your reporting dashboard only tells you the add to cart rate over time and doesn’t allow you to A/B test why that rate is changing, it’s not telling you much.

How does incremental lift blow these other metrics out of the water with A/B testing?

Incremental lift is calculated through holdout tests. Holdout testing uses the principles of statistical randomness to create groups, send a targeted marketing initiative to test groups, and compare the sales conversion rate between the targeted test groups and the control group.

This is commonly referred to as A/B testing, where one group is Group A and the other Group B.

When incremental lift is applied to e-commerce marketing, it refers to the increase in performance due to a marketing effort (such as enhancing your product detail page content), or what marketers call “sales lift”.

The formula for calculating incremental lift is:


Incremental lift = (Test Group Conversion Rate – Control Group Conversion Rate) / Control Group Conversion Rate


How does testing and targeting make incremental lift more valuable than other metrics?

Here’s an example. You want to add a new product video to your digital camera product detail page. You have two cuts of the video: one is 30-seconds and the other is 60-seconds. Which should you use?

A still capture of a product video with a play button in the center. The product is a fireplace/credenza hybrid.

Is 30 or 60 seconds the optimal video length?


In the first month of the product page, you use the 30-second video and run an A/B test on its effectiveness. You learn that the incremental lift of the 30-second video is 15%. In the second month, you run the 60-second video and conduct another holdout test, finding an incremental lift of 12%. After both tests you can conclude the 30-second video is more effective because it has a higher incremental lift.

Since incremental lift is calculated relative to a holdout group, there’s no need to account for temporal factors. Even if one month had a higher baseline conversion rate, factoring in the holdout group will account for that difference.

Incremental lift is the unquestioned best-on-the-market metric for product page optimization because it incorporates scientific A/B testing into your marketing strategy to isolate variables so that you can truly measure the effects of your initiatives.

How does incremental lift relate to revenue?

Incremental lift is calculated as a percentage–useful metrics for genius marketers–but other stakeholders may be more interested in metrics related to cold hard cash. For them, there is incremental revenue.

Incremental revenue is the additional revenue that can be attributed from incremental lift. Unlike incremental lift, which is measured as a percentage, incremental revenue is expressed as a dollar value. The formula for calculating incremental revenue is:


Incremental Revenue = Incremental lift (percent) * Average Order Value in Test Group (dollars) * Number of Test Group Orders


For example, if you ran a A/B test for the month of July, you may have found an incremental lift of 20% in the test group who saw your awesome SellPoints interactive content experiences. The question is how much revenue did this generate?

An illustration of a series of items flowing into a shopping cart. The image is cartoony and colorful.

Incremental revenue is money in the bank.


If your test group had 1000 orders with an average order value of $25, your incremental revenue from the campaign would be $5,000.


0.20 (incremental lift) * 1000 (number of orders) * 25 (average order value) = $5,000 (incremental revenue).


In other words, $5,000 of revenue can be attributed to your marketing activities. If your costs were less than $5,000, your marketing initiative had incremental revenue that justifies the investment.

How do I calculate the incremental lift on my product detail pages?

Incremental lift cuts through the noise of other metrics that don’t provide business insights. Unfortunately, most brands don’t have access to these kinds of analytics because they are selling on third-party retailers that don’t release that data. Brands need an e-commerce analytics platform that has the ability to produce reliable data, isolate key marketing factors, and generate actionable business insights: all at the same time.

Even among providers that are passing data to brands, so many of those reporting dashboards are just data dumps rather than digestible presentations that allow you to make confident business decisions.


We thought, “you shouldn’t have to be a data analyst to analyze your data.”


That’s why SellPoints exclusive reporting gives you easily digestible insights such as incremental lift to better understand what your customers want and how you can reach them more effectively. This is product page performance made simple.

A bar chart that shows four columns: incremental lift for a brand versus the category average, and calculated before and after enhanced content is added.

SellPoints reporting tells you how your incremental lift compares to other brands in your product category.


In addition to A/B testing, SellPoints reporting can tell you:

  • How much traffic is going to your product detail page
  • Where those visitors are coming from
  • Which content is driving engagement
  • How your ad campaigns are performing

Fill out this form below to start A/B testing your product pages and begin calculating incremental lift. SellPoints has over 15 years of trailblazing experience in the e-commerce marketing space. Incremental lift is just the beginning.

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