What is a Cohort Analysis?
Cohort analysis identifies trends in the behaviour of your consumers by dividing them into groups based on similar features. Data gleaned from them may be put to various uses. Cohort analyses, on the other hand, are most typically utilised in the B2B and SaaS sectors to aid enhancing retention efforts.
The cohorts may be divided into two groups for the purposes of analysis depending on two criteria: acquisition time or behaviour.
Acquisition Time Cohorts
How many clients you keep month after month when you run a cohort analysis based on acquisition time is what you’re trying to figure out.
Your company’s offering, contract duration, and the spacing of major client milestones will all influence how long it takes to acquire a new customer. Because freemium SaaS renewal dates are determined by the start date of each customer’s subscription, it’s possible to segment cohorts by the day or week they began using your service if it is a monthly retainer.
During the process of analysing month-to-month retention statistics, you should keep an eye out for the most common drop-off points. Drop-off points might highlight where typical friction points are or where the value promised throughout the marketing and sales process isn’t being realised, assuming nothing has changed with your product or service.
Ask yourself
- What’s going on in the customer experience that’s causing this decline in sales?
- We need to know what’s causing this drop-off in consumer satisfaction.
- Is this point of departure the same for all groups of customers, or is it exclusive to one or a few?
As an example, you could see a large drop-off point three months from the start date of a client. You’ll want to find out what’s creating churn at that point since you know it’s the conclusion of your onboarding process.
Some consumers acquired in August or later showed a significant drop-off point at the two-month mark that was not evident in earlier cohorts of customers. A new sales incentive plan was implemented by your organisation in August, thus it’s possible that a change in selling approach is to blame for the turnover.
Behavioral Cohorts
Instead of segmenting based on when a consumer joined your product or service, you’re segmenting based on how they’re using it.
Some of these cohorts may be based on the product line that they use, the product tier that they paid, the feature or set of features that they utilise.
According to HubSpot’s marketing, sales, and service hub retention data sets, the company might do a behavioural cohort analysis to better understand customer retention patterns. It’s also possible to evaluate whether there’s a difference in retention trends between consumers who use their email marketing tools, those who use social media and those who use conversational marketing tools, for example.
Consider the following questions while examining behavioural cohorts:
- Which product line or tier has the highest and lowest customer retention rates?
- Is there a correlation between the employment of certain characteristics and better or worse retention?
- What is the most effective mix of features?
Using Acquisition Time and Behavioral Analysis Together
An acquisition time cohort analysis seeks to determine the root causes of customer attrition. Some of the reasons for people’s ongoing employment with your organisation may be revealed via behavioural analysis of the acquisition time cohorts that were kept.
It’s possible that the clients you keep after six months all have a high utilisation rate of a feature and most of the customers that churned didn’t use that function. You may attempt to promote the use of such features early in the customer’s lifetime to enhance retention.
How to run a cohort analysis report in Google Analytics
1. Cohort Type
The dimension that serves as the foundation for the cohorts is the cohort type. Google Analytics presently only supports the acquisition date version. The acquisition date is the first time a user is identified as engaging with your content; that is, when users begin their first sessions.
There are of course limitations to Google Analytics cohort analysis reports:
- The first is, as previously stated, the ability to establish cohorts solely based on acquisition dates.
- The second is that monitoring returning sessions with multiple devices is inaccurate in Google Analytics; therefore, if a person views your website from their laptop and the second session is done using a mobile device, that session may be included twice in that cohort monitored separately. The same is true if the visitor visits your website using a different browser or clears cookies.
2. Size of the Cohort
The time span determines the size of each cohort. Google Analytics allows you to see data by day, week, or month.
What exactly does this mean? A cohort size by day report will show you all of the users you’ve recruited throughout the specified time period (in this example, the past 7 days) by day. The size of the cohort group for each date is shown in the example below inside their corresponding rows.
3. Metrics
This is where you may choose the metric to measure for each cohort. You may examine three types of metrics:
Metrics for each user
All of these measurements are cohort indicators when split by the overall cohort size. Goal completions, page views, revenue, session length, sessions, and transactions per user are all metrics.
In the following example, we’re evaluating daily cohorts of visitors based on page visits. On March 25, 134 people produced 677 page views, 106 page views the next day, and 85 page views the next day.
Why is this information interesting? It displays the interactions of people with your website after you have obtained them. To fully evaluate this data, you must have a marketing calendar to monitor your actions and identify which campaigns or efforts are causing a shift in the consumers’ behaviour.
Total
Total metrics provide the aggregate indication for the whole cohort.
Retention
User retention is one of the most critical metrics in the cohort analysis report, if not the most essential. It displays the number of users in the cohort who returned to your website during a specified time period, divided by the total number of visitors in the cohort.
PRO TIP: Determine which cohorts have higher user retention rates than others and investigate the reasons why. Have you started a retargeting campaign? What did you do at that period that had an influence on retention and might be used as a valuable lesson learnt in the future?
4. Time frame
The date range is the time limit that defines which data is presented on the report. These relate to the columns, and it will display the metrics chosen by date range and cohort size for each cohort size (rows).
5. Data visualisation
Now that you understand the fundamentals, you may experiment with the reports and compare, for example, two distinct cohorts. This is accomplished by choosing them under Acquisition Date. In the following example, I opted to display cohorts by revenue for two distinct cohort groups, depending on purchase date, by day.
In summary, I’m comparing two cohorts of recruited customers on April 6 and April 9, 2020, as well as the income they made in the previous 14 days.
It’s fascinating to watch a rise and how conversions occur on day four, after the user was obtained.
6. Including new portions
Understanding your users and their behaviour requires including various groups in your cohort study.
To begin, click the Add Segment icon in the upper right corner. A list of segment alternatives will be shown. Deactivate all users If you wish to view certain portions, click here.
I’ve chosen Mobile Traffic, Tablet Traffic, and Desktop Traffic for this report.
So, let’s assume I want to understand consumers’ purchase behaviour a few days after they first visit the website, but I’m not sure what the difference is between mobile, tablet, and desktop.
Why would this be of interest to you? Let’s have a look at the stats.
- To begin with, mobile traffic outnumbers desktop and tablet traffic combined.
- Revenue data, on the other hand, are comparable to week 0, when users were gained. This might imply that desktop and tablet users convert at a faster rate.
- Looking at revenue in the next few weeks is where things become interesting. There are desktop and tablet users that convert during weeks 4, 5 and 6 after being acquired.
These insights might be quite useful in shaping your marketing approach. Should you prioritise recruiting new desktop users and increasing desktop traffic? Should you, on the other hand, analyse and improve your mobile conversion path? Should you boost your desktop advertising budget?
When evaluating these numbers, you should obviously consider other variables driving these patterns, and it’s a smart habit to keep pulling these reports over time to determine how marketing efforts may be influencing user behaviour.
Key takeaways
To establish a clear reason of churn, you may have to undertake cohort analysis. The customer’s behaviour may be influenced by a variety of things. If you have a large amount of data, you’ll have a better chance of seeing valuable patterns.
However, the knowledge you receive is just a beginning. Following the discovery of links between behaviour or time and retention, you’ll need to go further into the underlying reasons and test out various remedies to find the most effective approach.
