Cohort analysis can multiply sales. Unfortunately, many eCommerce stores limit cohort data to improving retention, or worse, don't use it at all.

This guide showcases how eCommerce brands can use cohort analysis to improve key operations such as post purchase campaigns, ad spend optimization, and personalizing offers for high impact cohorts and customer segments.

If you'd like to skip straight to how to use cohort analysis with examples, click here. Otherwise, read on for what cohort analysis is and why you should invest the time to set it up.

Cohort analysis is a type of behavioral segmentation that groups users by one or more characteristics and tracks their behavior over time.

The most common cohort analysis groups users by acquisition date. Examples here include

Cohort analysis is an excellent tool for eCommerce companies to use. Below are some of the most common benefits you can get from designing and using a cohort report.

The primary benefit of cohort analysis is understanding how your customers act over time.

Without segmenting by cohort,  a company's growth or decline will obfuscate the impact of personalization, conversion optimization, or other efforts. 

Cohort analysis is the single best way to see and understand your brand's churn rate. Given existing customers spend on average 73.72% more than new ones, finding ways to maximize retention rate is one of the primary benefits of cohort retention analysis.

Image Credit

Lastly, you can use cohort analysis to effectively segment customers.

For example, what personal experiences should you create for customers who have recently lapsed versus those that return every week versus those that are brand new to your site. Do customers who come from different ad campaigns act differently? And if so, how can you create better offers for them?

These are the types of questions and answers you can get from using cohort analysis.

According to our last study on email marketing statistics, post purchase campaigns converted an outstanding 8.2% of the time.

Cohort analysis allows you to understand when your customers naturally return and make repeat purchases. With this data, you can optimize your post purchase campaigns and augment them with replenishment reminder triggered messages.

Above, Tula uses cohort analysis to understand when their customers naturally make another purchase. They trigger their replenishment emails as reminders before this date, and add on extra incentives after. In this email, they highlight three specific benefits of

Lifetime value (LTV) is often treated as the holy grail of eCommerce revenue metrics. 

However, time matters for both cash flow and ROI calculations. In this regard, payback periods are much more actionable than broad LTV metrics. 

From customer acquisition, how often are your customers coming back, how soon are they coming back, and how much are your profiting when they do. 

With cohort analysis you can clearly understand how much revenue customers are generating per period. 

Below is one example of how to see this data. Here we are charting how much revenue a cohort generates over time. 

To make payback metrics more actionable, you should segment the data by various properties. Properties can include

Barilliance empowers eCommerce stores with a variety of out of the box behavioral segmentations.  Broadly, Barilliance can track on-site engagements such as which pages are visited, items added to carts, a product display pages viewed. 

Contextual information can also be a source of behavioral segmentation, such as referring domain or whether the user interacted with a marketing campaign. Finally, we allow customers to define specific customer profiles or retention segments. Customers are automatically enrolled or fall out based on their actions. 

The costs of customer acquisition continue to increase. Below is an example of Amazon CPC by category. 

Image Credit

Here is another example showing how CAC have changed over time across multiple industries. Here we see how customer acquisition costs have increased 70% in B2B industries and just over 60% for B2C brands compared to four years ago.

Image Credit

We've discussed before how using a ROAS metric to determine ad efficacy will put you at a disadvantage, and why you should instead focus on ROMI (return on marketing investment). 

Cohort analysis is the only way to actually get a true return on marketing investment number.  As we've discussed above, you can use cohort data to understand how quickly you can expect payback from your acquisition channels. 

This empowers you to invest more, knowing exactly when you can expect payback and ultimately how profitable your ad campaigns will be.

Another interesting use case for cohort analysis is the effect of discounts. 

To do this, first create two behavioral cohort segmentations, one which purchased via a discount and the second which purchased without one. Then, compare how these cohorts behave over the next 90 days (or any timeframe that makes sense for your business). 

You can begin to see if discounts result in loyal customers, or are instead just losing revenue. 

There are other ways cohort analysis can help eCommerce stores optimize discount campaigns.

When leads do not convert, they can be placed into a separate behavioral cohort. You are then free to give more aggressive discount offers without fear of giving away margin unnecessarily.

Stitch Fix provides a great example. This message is sent if a user completes their initial fit assessment but do not complete a purchase. 

The offer is a clear, $35 credit that expires if unused.

Enriching profiles is a fundamental technique in improving cohort analysis' effectiveness. 

New visitors are often anonymous. Fashion Nova proactively addresses this issue with their initial welcome pop-up. In addition to offering an aggressive incentive (30% off), they also allow users to select what their preferences are. 

This data is then tied to the customer profile and used in cohort segmentations as well as personalizing future offers.

The goal of welcome campaigns is to establish relationships and create sales.

We covered how to create a multi-step welcome campaign, along with a best in class example from Sephora here. However, to dial in your own triggered email campaigns, you should create AB tests and analyze them through a cohort analysis.

You can quickly chart how various segments performed over the next 30 and 60 day periods, and ultimately understand which welcome series does a better job of transforming new visitors to repeat customers.

This guide showcased how eCommerce brands should be using cohort analysis to increase sales. Barilliance helps eCommerce brand act on the insights they obtain from cohort analysis.

With Barilliance, you can create personalized experiences and offers for each cohort, setup multivariate experiments to incrementally increase revenue, and ultimately grow your business.

For a glimpse on how to use eCommerce personalization to personalize cohort experiences, click here.

And, if you would like to speak to a personalization expert and see if Barilliance is the right partner for you, you can request a one on one demo here.