Long term success of a business depends on retaining those customers and providing them with customized experiences catered to their requirements. Due to the large amounts of data that are being generated due to operations and sales of the companies, there has been a rise in the practice of implementation of Business Analytics to get useful insights from this Data to improve the profits and the customer experience. This is where Cohort Analysis fits in and helps provide a targeted experience to the customers.
In this article, you will learn in-depth about Cohort Analysis, its importance, and the general methodology that is to be followed for its implementation.
What is Cohort Analysis?
Cohort Analysis is a type of Behavioral Analytics in which large volumes of complex Data is broken into related groups to perform Analysis. These smaller groups or Cohorts tend to share common characteristics or experiences within a specific time span. This process enables an organization to recognize important trends and patterns across the life cycle of a user/customer through a Cohort rather than accessing each individual customer.
An organization can then strategize and optimize its techniques and tactics to target specific Cohorts. Cohort Analysis should not be confused with Cohort study in which Data is broken down into similar groups, whereas in Cohort Analysis, the Analysis is performed in regards to Big Data and Business Analytics.
Cohort Analysis is also used as a tool to measure user engagement over a period of time. Data from a given eCommerce platform, web application, or online game is used for Analysis and rather than looking at all users as a unit, it breaks them down into related groups.
Deep Actionable Cohort Analysis
The technique of Cohort Analysis is helpful in dynamically developing plans and strategies as it provides in-depth and actionable information rather than just being a vanity metric to document the current state of business to be included in the reports. Since just having the information about the number of users at a platform can prove to be useless, Cohort Analysis can provide actionable insights across different customer demographics to cater to their needs in a targeted manner.
Understanding Cohort Analysis with Example
Techniques like Cohort Analysis help in deriving actionable insights. A social media platform may have a large undifferentiated dataset of all the users on the platform. This technique can help differentiate users across multiple user types depending upon the Time, Engagement, and various other trigger events.
Like customers who log on to the platform on a daily basis can be put into one Cohort, users which use the platform only once a month can be put into another Cohort, users subscribing for ad-free service can be put into one Cohort.
These different Cohorts can help the organization target the requirements of these users better and strategize their services and products accordingly. The analysts of the social media platform would be catered with a visualization of these different groups, helping them to track the behavior of these users in a targeted manner.
Steps to Perform Cohort Analysis
There are 5 general steps that are required to be performed under Cohort Analysis:
Step 1: Determining the Right Set of Queries to Ask
There needs to be some structure to the Analysis to come up with information that is relevant to improving the product or service being offered by the organization. Therefore the right set of questions need to be asked and evaluated to ensure that.
Example: For a content platform, the right set of questions could revolve around the subject of increasing user engagement duration.
Step 2: Defining the Metrics
The Analysts need to define the metrics on which the Data needs to be evaluated, it also includes identification of relevant events that can occur and need to be tracked
Example: user engagement, free trial activation, etc. Correlation between different events and metrics is also used to track certain trends.
Example: Increased user spending due to updated promotional offers.
Step 3: Defining the Specific Cohorts
There is a requirement for analyzing all users or performing attribute contribution in order to find the relevant differences between them and creating the Cohorts in the process.
These Cohorts could be defined based on the above-defined metrics and a tier system could be introduced to target these customers better.
Example: Regular users of the platform are provided with gold tier services whereas one-time users are only given base level benefits on a platform.
Step 4: Performing Cohort Analysis
Finally, perform the Cohort Analysis using Data visualization and identify trends and patterns across different Cohorts (usually differentiated using different colors).
This method can help target different types of customers and help provide an optimized experience depending upon the preferences of that Cohort.
Step 5: Evaluating Test Results
The test results are analyzed and strategies based on the insights obtained using this approach are implemented.
The effectiveness of those strategies needs to be evaluated on a regular basis as the Cohort and demographics are dynamic and change over a period of time. Hence, the identification of more relevant metrics can prove to be useful in developing better strategies.
Cohort Analysis with Retention Table
Retentional tables can be used to perform Cohort Analysis or Retentional Analysis helps visualize the Data on whether the user carried out a specific action. It can further be leveraged to represent the retention of the user over a period of time.
For example, the table above depicts a group of users who initiated the“App Launch” event and their retention over a period of seven days. Dates of Initiation / Initial launch such as “4/02” and “4/03” are grouped together as Cohorts and their Retention Rate across time is observed.
Understanding Types of Cohort Analysis
Cohort Analysis can be divided into two types:
- Acquisition Cohorts
- Behavioural Cohorts
1. Acquisition Cohorts
This type of Cohort Analysis segments the user base on the basis of the acquisition date and time of the service or when the user signed up for a product. This tracking can be performed with different frequencies relevant to the product like daily, weekly, monthly, etc.
Example: A Food delivery application can analyze an Acquisition Cohort on a daily basis, on the other hand, a B2B service app can perform the Acquisition Cohort on a monthly basis.
2. Behavioral Cohorts
This type of Cohort Analysis segments the user base on the basis of the actions they undertake while using the application. Special event triggers can be tracked to understand the behavior of demographically different users.
Example: Such triggers on a Food Delivery app could be the choice of restaurants and frequency with which customers order food, and for a social media platform, it could be the pages a user follows or the posts they like.
Understanding the Importance of Cohort Analysis
The majority of organizations use Cohort Analysis to track and improve the Retention Rate and reduce the Churn Rate of the users of their services or products. But this technique has wider applications to improve the business.
Example: An online Fashion Store can use this analysis to track the style and size preferences of different demographics of users and build the inventory accordingly to maximize sales. Similarly, it can also help identify targeted recommendations and promotional offers that can be sent to improve customer trust and build loyalty. In a broader sense, it can identify the correct path for feature adoption and upgrades across various online platforms.
For the purpose of the analysis of the Retention Rate of the users, the metric used is called Customer Retention Rate which is calculated with the help of the formula given below.
CRR = ((E-N)/S) X 100
Here, these terms mean the following:
- E: Number of customers at the end of the time period.
- N: Number of customers acquired during that period.
- S: Number of customers at the beginning (or start) of the period.
A higher CRR corresponds to robust customer loyalty. Comparison of CRR with Industry average can help provide a clear picture of the scenario and help draft better strategies to implement changes and improvements.
The key advantages of cohort analysis are,
Cohort analysis is an excellent tool for anyone trying to better understand their consumers and why they make specific decisions in their app. Here are some of the advantages of performing cohort analysis:
- Determine firm Health: Growing revenue even when you aren’t attracting new consumers is a good indicator of a thriving firm. This allows you to concentrate on upselling other services or products to them.
- Improve your Understanding of Customers: Cohort analysis allows organizations to better understand their clients by tracking their behavior over time. This might help you find trends and patterns that may not be obvious when looking at vanity stats.
- Enhanced Customer Segmentation: Businesses may establish more focused and effective marketing efforts and provide tailored customer experiences by splitting users into cohorts.
- Increased Customer Retention: Cohort analysis is useful for assessing retention rates and detecting potential churn issues. With this information, you may take proactive efforts to enhance customer experiences.
Key Metrics for Customer Retention
Other than CRR there are other metrics that can be used to analyze the Data. As there usually is too much information in such operations, Cohort Analysis can be used to highlight the important patterns and metrics to reach conclusions and solutions. These metrics to help differentiate Cohorts are stated below:
- Repeat Rate
- Orders Per Customer
- Time Between Orders
- Average Order Value (AOV)
1. Repeat Rate
This metric helps track the users which interact/purchase/transact with the company’s business repeatedly compared to the customers that terminate the relationship with a single interaction with the services/products.
2. Orders Per Customer
As the name suggests, this metric helps track the number of orders placed per customer. An increased Orders per customer metric indicates a strong retention rate for that product/service.
3. Time Between Orders
This metric measures the average duration between the orders placed by a customer. The optimum value of this metric is dependent on the industry, type of product/service and the business model opted for by the company. This metric can help send targeted mail regarding promotions to dormant customers and reactivation / resubscribing reminders to present customers.
4. Average Order Value (AOV)
This metric helps in the identification of high-value spenders and loyal customers and helps in differentiating high-value Cohorts which can be further targeted with marketing campaigns and promotional upgrades to earn customer trust and build brand loyalty.
Types of Cohorts to Analyze
Cohorts can be grouped into the following three categories:
1) Time-based Cohorts
Time-based cohorts are clients that signed up for a service or product during a specific time period. Analyzing these cohorts reveals that consumer behavior is dependent on when they begin utilizing a company’s products or services. Depending on a company’s sales cycle, the time period could be monthly or quarterly.
For instance, if 70% of customers who signed up with the firm in the first quarter stay with the company in the fourth quarter, but only 30% of consumers who signed up in the second quarter stay with the company until the fourth quarter, it indicates that Q2 customers were dissatisfied. The corporation may have overpromised during Q2 marketing, or a competitor may be offering better products or services to the same customers.
2) Segment-based Cohorts
Segment-based cohorts are customers who have previously purchased a specific product or paid for a specific service. Customers who sign up for basic services may have various requirements compared to those who sign up for advanced services. Recognizing the demands of the various cohorts can assist a company in designing tailored services or products for specific segments.
A SaaS company may offer varying tiers of service based on the purchasing capacity of the intended audience. Analyzing each level assists you in understanding which services are best suited to specific categories of your clients.
For example, if the advanced-level clients churn at a considerably greater rate than basic-level services, that is a signal that the advanced services are overpriced or that basic-level services just better meet the demands of most consumers. Understanding what customers want in a package allows the organization to optimize its alerts so that customers only receive relevant push emails that they will open and read.
3) Size-based Cohorts
Size-based cohorts are the different sizes of customers that buy a company’s products or services. Customers may include small and startup firms, medium-sized businesses, and enterprise-level businesses.
When the various consumer categories are compared based on their size, it becomes clear where the largest purchases originate. For the categories with the fewest purchases, the organization can analyze any flaws with the product and service offerings and discuss areas for development to increase sales.
Small and startup businesses typically experience higher attrition rates in a SaaS business model than enterprise-level companies. Small and fledgling enterprises may have a limited budget and are evaluating low-cost products to see what works best for them. Enterprise-level organizations have a higher budget and are more likely to stick with one product for a longer period of time.
Conclusion
In this article, you learned in-depth regarding the Cohort Analysis, its importance, and the general method to perform it in any Data-centric use case. This form of analysis provides the analysts with a method to have a critical view of the Churn and prevents it from getting overlooked due to growth.
Example: There could be a surge in the monthly active users due to a new promotion of launch, but rather than interpreting the raw numbers as growth this method can help you track the Retention Rate of the customers.
For Analyzing the data you can use techniques like Marketing Funnel Analysis and Customer Churn Analysis. Also set up Cohort Analysis in Excel and Tableau.
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. Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. Hevo with its strong integration with 150+ data sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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Abhishek is a data analysis enthusiast with a strong passion for data, software architecture, and crafting technical content. He has strong data analytical skills and practical experience in leveraging tools like WordPress, RankMath, and SEMRush to optimize content strategies while contributing significantly to Hevo Data.
