Your startup can acquire billions of potential customers but if none of them stick around, it doesn’t matter. That’s why retention hacking is the new growth hacking.
There are all kinds of tactics to boost retention and improving user onboarding is a critical one.
But to get directly at churn reduction, you need to first diagnose your product's/service’s specific problems. Then, make adjustments.
The logical next step is to put on your goggles & swim cap and dive into the numbers. That’s how you’ll find out exactly why users stop using your product/service.
And it all begins with a cohort analysis.
Cohort analysis is a powerful tool that helps businesses to understand the behavior of their customers over time.
Cohort analysis is a method of grouping customers based on their behavior over time. This type of analysis helps businesses to understand the behavior of customers who share similar characteristics, such as age, location, or purchase history. By grouping customers into cohorts, businesses can compare their behavior and identify patterns and trends that may not be immediately apparent from a single data point.
This type of analysis is essential in advertising because it helps businesses to understand how their customers are interacting with their products and services and provides valuable insights into their behavior. In this blog, we will discuss the concept of cohort analysis and how businesses can use it to improve their advertising strategies.
Cohort analysis is an important tool for advertising because it helps businesses to understand how their customers are interacting with their products and services. By grouping customers into cohorts, businesses can compare the behavior of different groups and identify trends and patterns that may not be immediately apparent from a single data point.
For example, a business may find that customers who purchased a product six months ago are more likely to purchase a product again compared to customers who purchased a product a year ago. This information can be used to improve advertising strategies by focusing on the behavior of customers who are more likely to purchase a product again.
Additionally, cohort analysis can help businesses to identify customer segments that are not engaged with their products or services. By understanding the behavior of customers who are not engaged, businesses can improve their advertising strategies to better reach these customers and encourage them to interact with their products and services.
As a business analytics technique, a Cohort Analysis allows you to compare variables and changes between your digital marketing campaigns.
For example, like real brick-and-mortar stores, websites change. If you’re doing it right, they change a lot and often. You can use a cohort analysis to try to isolate the effect of the website modification on user behavior.
Here are some factors that can impact user behavior that you may want to analyze with a Cohort Analysis:
The first step in using cohort analysis in advertising is to define your cohorts. This can be done by grouping customers based on common characteristics, such as age, location, or purchase history. For example, you may create a cohort of customers who purchased a product within the last six months, or customers who have made multiple purchases over a certain period of time.
Once you have defined your cohorts, the next step is to analyze their behavior. This can be done by tracking key metrics such as purchase frequency, average purchase value, and customer lifetime value. By analyzing these metrics, you can identify patterns and trends in customer behavior that may not be immediately apparent from a single data point.
After analyzing customer behavior, the next step is to identify opportunities for improvement. For example, you may find that customers who purchased a product six months ago are more likely to purchase a product again compared to customers who purchased a product a year ago. This information can be used to improve your advertising strategies by focusing on the behavior of customers who are more likely to purchase a product again.
Personalizing your advertising strategy is another way to improve the effectiveness of your advertising efforts. By using the insights gained from cohort analysis, you can create targeted campaigns that resonate with specific customer segments. For example, you may create a targeted email campaign for customers who have not purchased a product in the last six months to encourage them to make a purchase.
Finally, it is important to continuously monitor and update your advertising strategy based on the insights gained from cohort analysis. This will help you to stay ahead of the curve and adapt your strategy as customer behavior changes over time.
Take the example of an e-commerce business that generates massive data on its customers. The data ranges from purchased products, customer spending, click-through rate, product ratings, product returns, and other metrics.
Cohort analysis conducted by ecommerce businesses represents the behavioral patterns in a customer’s life cycle. This, in turn, helps in preparing better strategies to target suitable customers to further boost customer retention and engagement.
Cohort analysis, when defined, sounds similar to segmentation. Though these terms imply dividing customers based on specific criteria, they are still different.
While segmentation deals with classifying consumer groups irrespective of time, cohort analysis deals with classifying consumers into different groups for a defined period. When both segmentation and cohort analysis are applied, businesses get an opportunity to identify friction points within a time frame, which might lead to risk aversion.
A Cohort chart can be a little confusing at first glance. But they’re actually pretty easy to read once you know what you’re looking at.
Here’s a quick overview of what a cohort chart might look like:
First, let’s just look at just one customer cohort.
From left to right, here’s what you’re looking at:
Each column after that shows the percentage remaining customers from that cohort after each month. So under the “1” column, we can see that 88% of the original 24 customers remain after their first month, and so on and so on.
If you don’t like looking at percentages, you can also look at the data in absolute numbers. This will show you the exact number of remaining customers after each month.
This makes reading of the data pretty simple, right?
If you want to see revenue instead of users, below is that data as well.
Having the data and knowing how to read a cohort chart is nice. But unless you turn it into action, what’s the point?
Here is another example.
SOURCE - medium.com
The way to read the above chart is along the left-hand side we have the different cohorts, grouped by the month they purchased. Along the top we have months since purchase (or age of the cohort in months). In the chart itself we list the retention rate. Month 0 is defined as the month they purchased. The chart assumes that your customers pay upfront for the month, so it’s impossible to churn that month. Therefore, the retention rate in month 0 is always listed at 100%.
As an e-commerce company following are the most important KPIs for your business:
It evaluates the averages for all of these KPIs across your business.
Evaluating the above KPIs across the various metrics.
Look to understand the best and worst cohorts, and what are the traits of the best and worst performing cohorts to improve your next steps.
Evaluate the overall average performance of the business for the given metrics over time.
Here is a Scribe document explaining the step-by-step process of creating a GDS for a effective data visualization via a Cohort Analysis
Cohort analysis is an essential tool for businesses looking to improve their advertising strategies. By grouping customers into cohorts and analyzing their behavior over time, businesses can gain valuable insights into customer behavior and identify opportunities for improvement. By using the insights gained from cohort analysis, businesses can create targeted campaigns that resonate with specific customer segments and improve the effectiveness of their advertising efforts.
Takeaways and Next Steps
If you see significant increases or decreases in metrics like user retention for a certain cohort, check your marketing calendar to identify what changes could be driving these movements in your metrics. Turn these cohorts of interest into custom segments to dig deeper into their behavior.
Run new marketing campaigns like experiments. Isolate one variable you want to test with each campaign (e.g. channel, audience, ad content, etc), and stagger campaigns by week or month. Use the cohort analysis report to track the performance of each campaign. Schedule a half-hour one day every week to look at your Cohort Analysis report to check the results of these tests.
To read more about how Cohort Analysis Google Analytics can help you measure lifetime customer value (LCV), check out The Case for Cohort Analysis and Multi-Touch Attribution Analysis by Neil Patel of Uber Suggest.
You can also schedule a Free intro call with us here and we can help you leverage data for your business, better.
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