Understanding your customers is key to growing any business. For businesses looking to improve customer retention and engagement, cohort analysis offers a powerful way to track and optimize Customer Lifetime Value (LTV).
In this article, we'll explore how you can use cohort analysis to calculate Customer Lifetime Value effectively, providing actionable insights to keep your customers engaged and loyal. Let’s dive in.
What Is Cohort Analysis and Why It Matters?
Cohort analysis might sound technical, but it's simply a way of grouping customers based on shared characteristics—like the month they signed up, their first purchase, or how they interacted with your product. By analysing these groups (or cohorts), you can uncover patterns in customer behaviour over time.
For example, let’s say you’re running a mid-sized fashion brand in India, similar to Bewakoof. By using cohort analysis, you can track how customers from different sign-up months behave, which can help you identify when they’re most likely to make a repeat purchase or when they tend to drop off. This insight is invaluable because it allows you to tailor your engagement strategies and increase your Customer Lifetime Value (LTV).
Choosing the Right Time Frame for Cohort Grouping: Day, Week, Month, or Year?
When setting up a cohort analysis, one of the most crucial decisions you'll make is selecting the right time frame for grouping your customers. Let's have a look at it:
Understanding Customer Lifetime Value (LTV)
Customer Lifetime Value (LTV) is a metric that every business should be tracking. Simply put, LTV is the total revenue you can expect from a customer over the entire duration of their relationship with your business. The higher the LTV, the more profitable your business will be in the long run.
Let’s consider an example from an Indian subscription-based service like BookMyShow Stream. If they know that their average customer spends ₹500 annually and typically stays subscribed for three years, their LTV would be ₹1,500. But how do you know if that’s good or if there’s room for improvement? That’s where cohort analysis comes in. By breaking down your customers into cohorts, you can see which groups have the highest LTV and what strategies are driving that value.
At Nudge, we empower businesses to understand and enhance their LTV through intuitive tools that don’t require any coding skills. Whether it’s through gamified loyalty programs or personalized in-app messages, we provide the solutions you need to increase the value each customer brings to your business.
How to Prepare Your Data for Cohort Analysis
Before diving into cohort analysis, having well-prepared data is crucial. This preparation ensures your analysis is accurate and gives you actionable insights to drive your customer engagement strategy. Here’s how to get started:
- Collect Relevant Data: Start by gathering the necessary data points. This includes customer sign-up dates, purchase histories, and any interaction data that could influence customer behavior. For example, if you’re managing an e-commerce platform like Nykaa, you’d want data on first-time purchases, browsing behaviour, and customer feedback.
Related blogs to read: What is behavioral data? Importance & types
- Clean Your Data: Cleaning your data involves removing duplicates, correcting inconsistencies, and handling any missing information. A clean dataset ensures your cohort analysis provides reliable insights. Nudge can assist here with features like surveys and in-app messages to automatically collect and clean customer feedback and interaction data, ensuring you have high-quality information for your analysis.
- Segment Your Customers: Once your data is clean, you’ll need to segment your customers into cohorts based on shared characteristics—such as their first purchase date or the month they signed up. For instance, a streaming service like Hotstar might create cohorts based on the month customers subscribed and track their viewing habits.
- Choose Metrics to Track: Decide which key metrics you want to monitor for each cohort. These might include repeat purchase rates, churn rates, or average order values. For example, BigBasket might track how often a cohort of customers reorders within a specific time frame. Nudge's gamification features, such as loyalty programs and challenges, can be used to encourage repeat purchases, and the impact of these can be tracked in your cohort analysis.
- Visualise Your Data: Finally, visualising your data helps you identify trends and patterns more easily. Tools like Excel or Google Sheets can create charts that illustrate cohort behaviour over time.
How to Build a Cohort Analysis in Excel
Building a cohort analysis in Excel is straightforward when broken down into clear steps. This method allows you to track customer behavior over time and draw meaningful insights to enhance your Customer Lifetime Value (LTV). Here’s a step-by-step guide, along with a sample table to help you visualize the process.
1. Organize Your Data
Begin by setting up your Excel sheet with essential data columns, such as:
- Customer ID
- Sign-Up Date
- Purchase Dates
- Purchase Amounts
This organized structure will be the foundation of your cohort analysis.
2. Create Your Cohorts
Next, you’ll segment your customers into cohorts based on their sign-up dates. In Excel, you can use the “=TEXT(SignUpDate, "YYYY-MM")” function to extract the month and year of the sign-up date. This allows you to group customers who signed up in the same period.
3. Track Behaviour Over Time
With your cohorts in place, track their behaviour by recording metrics such as monthly purchase activity. Use a pivot table to summarise the number of customers who made a purchase in subsequent months.
This table shows how each cohort's purchasing activity evolves over time.
4. Calculate Retention Rates
To calculate retention rates, divide the number of active customers in each month by the total number of customers in the cohort. Use Excel’s “=COUNTIF()” function for counting and “=MonthN/TotalCohortSize” for calculating retention rates.
This table helps you see how retention rates change over time, giving you insight into customer loyalty.
5. Analyse and Take Action
Finally, interpret the data to uncover trends and areas for improvement. If you notice a drop in retention after a particular month, consider strategies like introducing Nudge’s in-app messages or walkthroughs to re-engage those customers.
By following these steps and utilising the tables, you’ll be well-equipped to build a cohort analysis in Excel that can drive actionable insights and enhance your Customer Lifetime Value (LTV).
How to Calculate Customer Lifetime Value (LTV) Using Cohort Analysis
Understanding how to calculate Customer Lifetime Value (LTV) through cohort analysis is crucial for making informed business decisions. Here’s a step-by-step guide that will help you calculate LTV in a way that’s clear and actionable, with examples relevant to an Indian context.
1. Calculate the Average Revenue Per Customer
- Formula: Average Revenue Per Customer=Total Revenue of the Cohort/Number of Customers in the Cohort
- Example: If 100 customers generate ₹1,00,000 in revenue, the average revenue per customer is: ₹1,00,000÷100=₹1,000
Now that you’ve calculated this, the next step is to understand customer retention.
2. Determine Retention Rates
- Formula: Retention Rate=Number of Active Customers in a Period/Number of Customers in the Cohort
- Example: If 60 out of 100 customers remain active after the first month, the retention rate is: 60100=60%
With retention rates in hand, you can now estimate the average customer lifetime.
3. Estimate the Customer Lifetime
- Process:
Sum retention rates across periods to estimate how long customers stay active. - Example: If the sum of retention rates equals 1.8, the customer lifetime is about 1.8 periods (or 18 months).
Finally, you can calculate the total value each customer brings to your business.
4. Calculate the Customer Lifetime Value (LTV)
- Formula: LTV=Average Revenue Per Customer×Customer Lifetime
- Example: If the average revenue per customer is ₹1,000 and the customer lifetime is 1.8 periods, the LTV would be: ₹1,000×1.8=₹1,800
By following these simplified steps, you can accurately calculate Customer Lifetime Value using cohort analysis, giving you the insights needed to grow your business efficiently.
Advanced Cohort Analysis Techniques to Enhance Customer Lifetime Value
Once you’ve mastered the basics of cohort analysis, there are advanced techniques that can take your insights—and your Customer Lifetime Value (LTV)—to the next level. These techniques allow you to dive deeper into your data, uncovering trends and patterns that aren’t immediately visible with a standard cohort analysis.
1. Behavioural Cohort Analysis
Instead of just grouping customers by when they signed up or made their first purchase, behavioural cohort analysis groups customers based on how they interact with your product or service. For example, you can create cohorts based on specific actions, such as customers who completed a purchase after viewing a certain number of product pages or those who responded to a specific marketing campaign.
This method helps you understand the behaviours that lead to higher LTV, allowing you to refine your customer engagement strategies. At Nudge, our in-app messages and walkthroughs can be used to trigger specific actions and behaviours that are proven to increase customer value, which you can then track and analyse using this technique.
2. Predictive Cohort Analysis
Predictive cohort analysis involves using historical data to forecast future behaviour. By identifying patterns in your existing cohorts, you can predict how new customers might behave and adjust your strategies accordingly. This technique is particularly useful for anticipating churn and taking proactive steps to retain high-value customers.
Nudge’s surveys and nudges can be tailored to gather data that feeds into your predictive models, helping you anticipate customer needs and improve retention rates before any signs of churn appear.
3. Cohort Analysis by Customer Segment
Segmenting your customer base into different groups (e.g., by demographics, location, or product preference) and then performing cohort analysis within each segment can provide more granular insights. This approach allows you to tailor your strategies to the unique needs of each segment, further boosting your LTV.
For instance, Nudge’s gamification features can be customized for different customer segments, offering personalized rewards or challenges that resonate with specific groups, thereby enhancing engagement and loyalty.
4. Multi-Cohort Comparison
This technique involves comparing multiple cohorts side by side to identify trends and anomalies. By analysing how different cohorts perform under various conditions—such as during a promotional period or after a product launch—you can fine-tune your strategies for maximum impact.
Nudge makes it easy to set up and monitor these comparisons through our data insights, allowing you to quickly adapt to what works best for different customer groups.
By implementing these advanced techniques, you can gain a deeper understanding of your customers, refine your strategies, and ultimately increase your Customer Lifetime Value.
Conclusion
Cohort analysis is a game-changer for understanding your customers and increasing their lifetime value. With Nudge, you can harness this power without the need for heavy engineering. Our tools make it easy for your product and marketing teams to drive engagement and retention, turning insights into action.
Ready to see the difference? Book a demo today and discover how effortlessly Nudge can integrate with your existing systems, helping you grow your business smarter.
FAQs
1. How can a young company predict lifetime customer value?
Young companies can predict LTV by using early customer data to spot trends in spending and retention through cohort analysis. This helps estimate future revenue from similar customer groups.
2. What is the best web analytics tool for cohort analysis?
Google Analytics is a popular choice for basic cohort analysis. For more advanced features, tools like Mixpanel and Amplitude are excellent options.
3. What is the best way to calculate LTV, cohort analysis or rolling retention?
Cohort analysis is best for tracking specific customer groups over time, while rolling retention is ideal for understanding long-term customer loyalty. Choose based on your business model.
4. How does cohort analysis help in calculating LTV?
Cohort analysis helps calculate LTV by tracking customer behaviour over time, showing how long they stay active and how much they spend.
5. Can small businesses benefit from cohort analysis?
Yes, small businesses can gain valuable insights from cohort analysis, helping them improve customer retention and boost profitability.