Cohort analysis is one of the most exciting types of behavioral analytics tools for your ecommerce store. This type of data crunching is one of the most actionable and useful methods for understanding your customers’ behavior, because it breaks the data into related groups that you can run through more specific analysis.
Let's walk through everything you need to know about cohort analysis; here's some of the most important questions we'll be answer and topics we'll cover in this guide:
- What is Cohort Analysis?
- How Does Cohort Analysis Work?
- How to Use Cohort Analysis in Your Business
- 4 Reasons You Need Automated Cohort Analysis
What is Cohort Analysis?
Cohort analysis is a type of behavioral analytics that groups people based on common characteristics prior to analysis. This is especially useful for your ecommerce store, as user behavior amongst your customers is some of the most important data for informing marketing campaigns and overall business strategy.
You may have thought about cohort analysis before and not realized it. This behavioral analytics method allows you to get more detailed feedback from your customers based on what they are purchasing, how often they are making purchases, and a whole range of other insightful actions.
Cohort analysis is a natural extrapolation from market segmentation. One of the most fundamental aspects of modern marketing is targeting different audiences and tailoring your marketing materials to appeal to them.
Online shoppers have become accustomed to personalized shopping experiences. The more tailored the experience for your shoppers, the better your customer lifetime value (CLV), customer retention, and conversion rate metrics will be. The goal is to always know whether or not your efforts are working, and the key to this is in your cohort analysis report.
The Behavioral Analytics Method in a Nutshell
Cohort analysis looks at the data from a given dataset. Instead of looking at all the users as one unit, it breaks them into related groups, or cohorts, for analysis. This allows teams to look at how those different customer cohorts behave over time.
A cohort is a specific group of people linked to a specific period or event. All customers who made their first orders in November 2021 are a cohort. All customers who purchased on Black Friday, all customers who bought product XYZ, or even all customers who purchased product XYZ on Black Friday, are examples of cohorts.
By running analyses on the behaviors of specific groups of people within a certain period of time for different metrics, you can spot trends that wouldn’t be visible otherwise. As a result, you can adjust your acquisition, retention, and pricing strategies to drive, sustain, and increase growth.
An Example of Cohort Analysis in Action
Imagine you have a cohort of people who bought your product in July of 2021. You can compute the retention rate, repurchase rate, average lifetime value, average lifetime revenue, and total lifetime revenue on each cohort. This allows you to:
• Identify the dollar value of each cohort
• Identify the size of each cohort
• Identify how many of these customers came back
• Identify the retention cutoff period
• Determine the repurchase rate for two, three, five, or more purchases of that cohort
• See what product was in their first time purchase and understand if it leads to more loyalty
You can also check the effectiveness of specific marketing engagement experiments that you ran on each type of cohort.
Your cohort analytics tool might tell you that 50% of this cohort was retained after month 3 but only 10% stayed by month 11. It can show you that one cohort value is a lot more lucrative than others and that they have a great retention month after month. This is valuable information to analyze.
Here are some questions you can ask while analyzing the data you’ve uncovered:
• Does the cohort size affect how lucrative it is?
• How did you acquire so many people that month?
• Was there a specific sale or promotion, or did you increase ad spend?
• Did you run a special campaign to retain that cohort?
When assessing your cohort data, you should always strive to replicate marketing actions that resulted in better metrics like: lower customer churn, more purchases, and increased average order value (AOV). The numbers are there to back you up when doubling down your successes.
How Does Cohort Analysis Work?
To use cohort analysis, you need data analytics tools that offer the method three boundaries — sometimes called anchors:
The cohort: This is the group of people you’re analyzing and the period during which they visited your site.
The lagging period: How long after that visit do you want to track your cohort? A lagging period of one month means you track each member of that cohort for four weeks after their first visit.
The termination time: This is when to stop tracking people and analyze the data. If you track people from July 1 to July 31 with a one-month lagging period, your termination date will be August 31.
These boundaries define what data you’re analyzing, so you can target exactly what you want to know. Once you’ve defined these anchors, your cohort analysis tool will take the data it collects and analyze it.
Consider the July 2021 cohort mentioned above. After August 31, your cohort analysis tool will have the data from the full month-long lagging period you set. With that information, you’ll be able to see how many of the July cohort came back over that month, whether they purchased anything in that time frame, and what sites they visited.
The right cohort analysis tool will provide you with a report that includes clear data visualization of how the cohort you selected behaved in your chosen period. When you have the data in hand, you can make more informed decisions about how to improve your business and marketing going forward.
How to Use Cohort Analysis in your Business
Cohort analysis can help you spot trends when it comes to how your customers interact with your product. However, it takes time to see results with cohort analysis. Because of the lag time that’s inherent in the process, cohort analysis requires patience.
For example: A cohort analysis of people who bought your product during the December holiday season will give you data about holiday consumers. However, the way customers behave during the holidays is almost always different from the rest of the year. To get data that you can generalize, you need to regularly perform cohort analysis on similar cohorts to identify trends.
That’s where automation comes in. Instead of having to manually run the same sets of analyses every quarter, month, or week, the right automated cohort analysis tool can do it for you — saving you time and effort. Automation means you can set up your data analysis once and build up a collection of useful, targeted data without having to lift a finger.
4 Reasons You Need an Automated Cohort Analysis Tool
1. Spot Trends in Customer Behavior
Cohort analysis is all about finding trends in particular datasets. While other types of data analysis can help you find general trends, behavioral cohorts you to get as specific as your data allows. You can drill down to identify the products that are linked to the highest customer retention, giving you the chance to focus on what your customers value.
When your cohort analysis is automatic, you can get results in minutes and identify trends that would have otherwise been lost in the noise of your data. When you have lots of customers, the ability to automatically separate the ones that interest you is critical.
2. Segment Customers Effectively
Cohort analysis allows you to group your customer base into effective segments. Not only can you segment customers by their previous purchases, but you can group them by time period, time spent on your site, and even the way they found your site in the first place.
With more effective segmentation, you can create email campaigns or SMS text campaigns with content targeted to keep your customers engaged. For example: You can retarget customers who haven’t bought anything in specific date range or send emails promoting related products to customers who’ve just made a purchase.
In a time when customer retention is increasingly essential for businesses, thoughtful engagement like this, driven by your business analytics, is more important than ever.
3. Identify Weak Promotions
Identifying when a promotion isn’t working is easier with cohort analytics. By choosing a cohort defined by the promotion that brought them to your site, you can check for churn, weak customer retention, and low average lifetime revenue.
Instead of simply checking whether a promotion led to conversions, you can find out the strength of those conversions. Information like this can help you refine good promotions into great ones and trim those that aren’t helping you achieve your goals.
4. Strengthen Customer Engagement
Over time, your cohort analysis tool will help you identify trends throughout your customer base. As you begin to spot patterns in different cohorts, you’ll be able to appeal more directly to the people who truly support your business.
In particular, you can focus on two metrics: retention and repurchase rates. As you use cohort analysis, you will likely notice one or several products that are linked to high customer retention rates. In many cases, these products are also frequently repurchased.
When both retention and repurchase rates related to a product are high, it’s clear that the product is a valuable tool for driving revenue. This is the type of data you can use to keep your customers coming back for more.
The Right Cohort Analysis Tools for Your Ecommerce Site
Now that you know how cohort analysis works and why it’s valuable, consider getting the right tools for the job. Peel has the most powerful data analysis software available — which allows you to study data across acquisition, activation, retention, and referral metrics with ease.
By collecting customer retention, average lifetime revenue, total lifetime revenue, and repurchase rate cohort data all in one place, you can get direct insight into how your customers engage with your brand. The result is a simpler, clearer decision-making process for fixing problem areas, spotting opportunities, and improving customer loyalty.
Sign up for Peel today and explore all the ways that data automation and analytics software can help your business grow.