Customer Retention Analytics Guide for 2023
Here we discuss customer retention, what it means, and how to gather data to analyze customer retention.
What are customer retention analytics?
Customer retention analytics is the study and interpretation of customer data to understand why people stay with your business or decide to churn. With these insights you can:
Identify high-value customers and their characteristics, which can be used to set up lookalike audiences and target similar prospects.
Learn which customer engagement practices are most effective, and invest more time and resources in them.
Discover why customers leave, and use those lessons to prevent other customers from churning.
Customer retention analytics types
You can perform different types of customer retention analytics to unearth different customer insights, like the following:
Descriptive analytics shows you trends and patterns in historical data. For example, periodic analytics measures customer retention and churn over a given period of time (e.g., month, quarter, year).
Diagnostic or retrospective analytics helps you understand why an incident happened – like why a customer churned. You observe data surrounding a past churn event and study customers’ exit survey responses.
Predictive analytics forecasts which customers are likely to stay and which ones will soon churn. Using an uplift model, you can also segment customers based on whether marketing campaigns will make them more likely to stay or leave.
Prescriptive analytics recommends the best action to take to achieve your desired outcome. For instance, you use propensity models to identify the product recommendation or the type of promotion that would most likely convince an e-commerce customer to shop again.
Customer retention KPIs
With clean customer data, you can calculate customer retention KPIs to get an accurate picture of retention trends. For each KPI, you want to watch for sudden dips and spikes, seasonal highs and lows, and trends by customer segment and cohort. You measure each KPI over months, quarters, or years.
Retention rate tells you how many customers stay with your business over a given time period. You want to improve your retention rate over time (or at least maintain it if you have a strong retention baseline).
Customer Retention Rate = ((Number of customers at the end of a given period - New customers acquired in that period) / Customers at the start of the period)) x 100
Example: You started January with 100 customers, gained 5 new ones, and ended the month with 88.
Calculation for customer retention rate:
Step 1: (100 - 5) / 88 = 0.83
Step 2: 0.83 x 100 = 83%
Not counting your newly acquired customers, only 83 of the original 100 customers stuck with you throughout the month. You end January with 83% of the customers you had when it started.
Churn rate tells you how many customers leave your business over a given time period.
Customer Churn Rate = ((Customers at the start of a given period - Customers at the end of that given period) / Customers at the start of the period)) x 100
Example: You started 2022 with 9,000 customers and ended it with 8,460.
Calculation for customer retention rate:
Step 1: (9,000 - 8,460) / 9,000 = 0.06
Step 2: 0.06 x 100 = 6% You had 540 fewer customers at the end of 2022 than you did when the year started. Your churn rate for that year is 6%.
Customer lifetime value
Customer lifetime value (LTV) is the amount of money an existing customer will spend on your business over their lifetime. You use LTV to identify your highest-value customers. As a general rule, a customer’s LTV should be 3x to 5x the amount you spent to acquire them (also known as the LTV:CAC ratio). With highly scalable products, such as consumer apps, the ratio can be higher.
There are various ways to calculate LTV based on your business model. Here’s one of the simpler formulas.
Customer Lifetime Value = ((Average purchase value x Average # of purchases in a year) x Customer lifespan in years))
Example: A customer spends $50 on average per purchase, and shops 16 times a year. You estimate that this customer will be your customer for five years (based on historical data for similar customers, competitor/industry benchmarks, or predictive modeling).
Calculation for customer lifetime value: (($50 x 16) x 5) = $4,000
The customer will likely spend $4,000 on your business over their customer lifespan.
Net Promoter Score®
Net Promoter Score® (NPS) measures how likely customers are to recommend your company to someone else. You ask customers that question (“How likely are you to recommend us to a friend/colleague?”) and ask them to choose a numerical answer from a scale of 1 to 10. Customers who answer 9 or 10 are Promoters, while those give you a score of 6 or below are Detractors.
Net Promoter Score = % of Promoters - % of Detractors
Example: Of all your NPS survey respondents last month, you had 30% Promoters (9 or 10 score), 30% Passives (7 or 8), and 40% Detractors (6 to 0).
Calculation for Net Promoter Score®: 30% - 40% = -10%
You have a negative NPS – a real emergency! Investigate internal and external causes, and observe how NPS correlates with your retention and churn rate. If you asked a follow-up question (“Why?”) in your NPS surveys, dig into the responses to discover the reasons for low customer satisfaction.
How to measure customer retention
The following are broad steps for implementing customer retention analytics. For a deeper dive into data analytics, you may want to try free online courses like those offered in Segment’s Analytics Academy.
Choose your metrics
Identify customer retention metrics relevant to your business model. A SaaS business needs to measure factors like monthly recurring revenue and DAU to MAU rate (daily active users to monthly active users). An e-commerce business looks at product return rate and time between purchases, among other metrics.
Choose metrics that let you gauge how well you’re succeeding in achieving particular goals. Start with the goals your business leaders have set out for the year, then identify how your team can help achieve them. For example, if your company aims to reduce expenses, your team might aim to lower costs associated with product returns. To know how well you’re achieving that goal, you track changes in product return rate month over month.
Compare your customer retention rate with your own performance by establishing a baseline. That means examining historical data to know your “typical” customer retention rate, as well as retention and churn patterns.
You might find, for instance, that 20% to 30% of your free trial users typically churn when the trial period ends. With this knowledge, you avoid needless panic when 20 out of a cohort of 100 free trial users don’t convert into paying customers. And if 40 people churn in the next cohort of 100 free trial users, you know you need to investigate.
Compare your baseline with industry benchmarks (and, if data is available, with that of competitors). You want to identify and fix causes of churn if your baseline retention rate is lower than the industry average.
Segment your customers
You gain useful, actionable insights in customer retention analytics when you analyze customers by segment and cohort. Segments are groups of customers who share characteristics, like demographics and shopping habits. Cohorts are groups of customers who performed an action in the same time period, such as people who downloaded your app in January.
When you analyze customer retention and churn by segment or cohort, you avoid “apples to oranges” comparisons. Free trial users, for instance, have different churn reasons than long-term customers. You also identify more effective customer retention strategies – for example, you offer an exclusive loyalty program to high-LTV customers, show new users content designed to get them to perform valuable product actions, and create various onboarding processes tailored to different personas.
Get more ideas in the ebook, 9 Retention Strategies Unlocked by Customer Data.
How to turn your customer data into action
Customer retention analytics begins with collecting clean customer data and putting it all in a centralized database, where you can create comprehensive customer profiles and build detailed customer segments. You can do that (and more) with Twilio Segment, a customer data platform that performs automatic data quality checks, identity resolution, and customer segmentation.
With accurate customer profiles and segments, you can glean useful insights from customer retention analytics. You discover unmet customer needs, opportunities for personalization, and ways to improve the customer journey to boost revenue and ROI. You can run data-driven campaigns to prevent churn and get customers to stick with your business.
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