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How to use Cohort Analysis to Increase Sales, Ad Spend, + More

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.

What is cohort analysis?

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

  • Grouping users by first purchase date
  • Grouping users by first interaction date (from a given marketing campaign) 
  • Or, grouping users by the first time they downloaded your eCommerce app or landed on your website.

However, you can also create customer cohorts based on behaviors taken or not taken. Examples here include visiting certain pages or opting into loyalty programs.

One important benefit of cohort analysis is being able to see how each cohort acts across time periods. Because of this, it is often used for cohort retention analysis.

However, as we will see, eCommerce stores can utilize cohort analysis for much more than retention.

“eCommerce stores can utilize cohort analysis for much more than retention” 

Cohort analysis benefits: How to use cohort data to improve eCommerce success

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.

1. Understand how customers behave over time

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. 

2. Track and understand your churn rate

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.

3. Identify behavioral segments to create personalized campaigns and experiences for

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.

Cohort analysis best practices and examples

1. Optimize post purchase campaigns

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

  • Product price savings - "Save up to 15% on skincare essentials"
  • Free shipping - "Score free shipping every time"
  • Bundled gifts - "Enjoy a free gift on us with each order"

2. Transform broad LTV metrics into useful 30 day, 60 day or other payback period metrics

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. 

3.  Understand LTV and payback metrics with behavioral segmentation

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

  • Marketing channels and campaigns - This is especially  important if you are using cohort analysis to optimize your ad campaigns.
  • Products - Determine which products lead to repeat purchases. This is ideal for surfacing which products you should create ad campaigns around. 
  • Customer segments - Finally, your customer segments do not act alike. You can get a better understanding of important segments by pulling them apart from the rest of the data. For example, you may want to look at how first time buyers engage with your brand vs how customers enrolled in your loyalty program engage. 

4. Afford higher CAC

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

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.

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.

5. Determine if discounts work

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. 

6. Build better welcome campaigns

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.

Next Steps

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.

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