Every business should put effort towards understanding its customers better. The best way to understand customers is by analyzing customer data. This can give a business some insights that can spearhead its growth. Such insights help a business know where it’s doing well as well as where it needs to make an improvement.
Businesses can also use such insights to come up with successful growth strategies. They can learn more about their Customer Retention Rate as well as the Average Lifetime Value (LTV) for their customers. That is exactly what the Cohort Analysis does.
It helps businesses and organizations to know their customers better and make sound decisions. In this article, you will be learning about Cohort Analysis and the steps for setting up Cohort Analysis Excel.
Table of Contents
Understanding Cohort Analysis
Image Source: https://mode.com/blog/cohort-analysis-helps-look-ahead/
Cohort Analysis is the process of analyzing the behavior of a group of customers over time. Cohorts are simply nonchanging groups, for example, customers cannot move from one Cohort to another and no new customers can join a Cohort once it has been formed.
The most popular type of Cohort is a group of people who became customers within a certain time frame, for example, the fourth quarter of the year, or the second week of March. Cohort Analysis is also known as “Statistic Pool Analysis” and it determines how these specific, fixed customer groups behave over time as well as their movement along the Customer Lifecycle Curve.
So, Cohort Analysis takes data from a web application or an eCommerce platform and instead of looking at all users as one unit, it breaks them into a number of related groups for analysis. The groups or the Cohorts normally share common experiences or characteristics within a defined time span. Due to this, Cohort Analysis can be seen as a tool for measuring user engagement over time.
Cohort Analysis helps marketers and businesses to separate growth metrics from engagement metrics since it’s easy for growth to mask engagement problems. The lack of activity of old users can be hidden by the high number of new users.
Hevo Data, a No-code Data Pipeline helps to transfer data from 100+ sources to a Data Warehouse/Destination of your choice to visualize it in your desired BI tool. Hevo is fully-managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
Get Started with Hevo for Free
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!
Pre-Requisites
This is what you need for this article:
- Working knowledge of Microsoft Excel.
Steps to Set up Cohort Analysis in Excel
In this section, you will be learning how to build a Cohort Analysis and calculate the Average Lifetime Value (LTV) of users in Excel.
The following are the general Cohort Analysis steps:
Cohort Analysis Excel Step 1: Understand and Clean the Data Set
Before doing anything with the dataset, make sure that you understand it. Look for any errors and abnormalities in the dataset and deal with them. A good example of an abnormality, in this case, is “cancel dates” that begin earlier than the start dates. You should identify and look for ways of handling them.
The dataset to be used shows user details like their id, the starting date of their plan, the date they cancelled their plan, their monthly payment, and their plan Id.
Cohort Analysis Excel Step 2: Add New Columns to the Data
The current data gives you the foundation for Cohort Analysis. To perform Cohort Analysis on the data, you should first add new columns to help you calculate new information.
Examples of such information include Cohorts, number of active months, and customer LTV. There are different ways of defining and calculating Cohorts in Excel. In this case, a Cohort will represent the month in which a customer was acquired.
The format should be the same for all customers in a Cohort and it can be calculated using the “End of Month” function that finds the end of the previous month and adds 1 to get the start of the current Cohort as shown below:
=EOMONTH(Start Month,-1)+1
After getting values for the Cohort column, you can proceed to the number of active months. The number of active months is the Average Lifetime Value (LTV) of a customer, that is, from when the customer was acquired to when he stopped using the product or service.
You can calculate this using the DATEIF function, which determines the number of days, months, or years between two dates. The active months are inclusive since customers pay during their cancel months. The values for this column can be calculated using the following formula:
=DATEIF(Start Date,End Date,"m")+1
There are different ways of calculating the Average Lifetime Value (LTV) of a customer. The simplest approach is by taking the monthly payment of a customer and multiplying it by the total number of active months to get the total revenue.
It will be good for you to understand the syntax and the logic of each function before trying it. You can press the F1 key (PC) to access the Excel Help menu and get information about each function.
Cohort Analysis Excel Step 3: Data Visualization
You can now use your expanded data set to group your individual customer data into Cohorts.
Image Source: https://chartio.com/blog/cohort-analysis/
You can then start to generate charts that visualize your data and aid in the Data Analysis process.
More Information about generating charts in Microsoft Excel can be found here.
Cohort Analysis Excel Step 4: Perform Cohort Churn Analysis
A Cohort Churn Analysis determines how well you’ve retained customers over the lifetime of each Cohort. You can perform this Analysis using the COUNTIFS function, which counts the number of cells in a particular range that meet a particular criterion.
The function takes the following syntax:
=COUNTIFS(criteria_range1, criteria1, [criteria_range2, criteria2]...)
In Cohort Analysis, this function can be used to count the number of active users per Cohort. The current active customers can be divided by the total number of users in the Cohort so as to get the percentage of active customers per month.
=COUNTIFS('MY Data'!$F$2:$F$515,"="&J$1,'MY Data'!$C$2:$C$515,">"&EOMONTH(J$1,$B22))/J$33
Note, that you must format the cells correctly so as to get a percentage. The final product should be a visualized analysis of Customer Churn for every Cohort, which can help one understand the Retention strategies which were not effective and the ones that should be replicated in the future. More information regarding Churn Analysis
Limitations of Cohort Analysis
The following are the drawbacks of Cohort Analysis:
- Cohort Analysis requires you to keep a sizeable and detailed dataset within your business, which makes it costly and time-consuming.
- It is subject to bias by the person performing the analysis. This can result in useless results.
Conclusion
In this article, you learned in detail about the process of Cohort Analysis and the steps that need to be followed for setting up Cohort Analysis Excel. Automated integration with your Data Warehouses/multiple data sources and the analytics database can make your choice much simpler as a lot of necessary features can be integrated readily. Integrating and analyzing data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture.
Visit our Website to Explore Hevo
Hevo Data, a No-code Data Pipeline helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. Hevo with its strong integration with 100+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
Want to take Hevo for a spin?Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!
Share your experience of learning about Cohort Analysis Excel! Let us know in the comments section below!
Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.