• Datitude

MARKETING METRICS BLOG SERIES: CUSTOMER LIFETIME VALUE

Welcome to the third part of our blog series helping retail marketers improve their data literacy and get more from their data.


This time we're exploring Customer Lifetime Value (CLV): it's the second of our top five metrics and insights retail marketers should be focusing on to boost customer acquisition, retention and engagement.


If you missed the previous blogs in this series catch-up here:


Part 1: Improving your data literacy

Part 2: Get a single, reliable and accurate view of your customer


What is Customer Lifetime Value?

CLV is a customer’s total worth to you over time.


How do you measure CLV?

It can be calculated in different ways but the simplest, and we like simple, is total revenue from sales orders across all channels, less returns. Some organisations build in acquisition and servicing costs to determine CLV in net profit terms.


CLV can be both historic (using data from past events) and predictive (expected value based on historical data and algorithms to predict future value).


At an organisational level, CLV is the customer value (ie. average order value X average order frequency) multiplied by the average lifespan of a customer.


Why is CLV important?

Customer value icon

CLV is a key growth metric - a higher CLV means buyers are becoming repeat and loyal customers, there's less churn / better retention, lower acquisition costs, and increased profitability. Conversely, a reduction in CLV suggests product issues and attrition.


Knowing CLV aids segmentation analysis and enables customer acquisition and retention strategies to be developed whilst maintaining profit margins. It also helps with budgeting as you can confidently set budgets for customer acquisition and retention costs.


At an individual customer level, CLV can help you decide how much to invest in acquiring new customers and in retaining existing customers.


How to use CLV?

A good customer data platform (CDP) will enable you to compare the lifetime value of different customer segments - essential for drilling down into the data to identify what's driving a higher CLV so you can take action to increase the value from all customers. Some examples of how to use CLV are:


i) Understanding lifetime value by acquisition channel and RFM segment (recency, frequency, monetary value) is incredibly powerful for optimising marketing actions. For example, you can identify, and target, lookalikes based on your most valuable customers and determine how much you can afford to spend by channel.

Image of shop front, sale signs, discounted prices

ii) What’s the promotion / pricing mix of higher CLV customers? For example, what proportion of customers acquired during a sale period are likely to become full-price customers, and when? Or what’s the relationship between price (full, promotion and mark-down) and lifetime value, order count and frequency? Such insights can help you determine when and how to engage with customers, including promotions and loyalty offers.


Some recurring themes we see are customers who purchase a mix of mark-down and full-price products have consistently higher lifetime values than those who always shop full price, and if customers acquired during a sale period place a second order, they’re more likely to purchase at full price.


Datitude platform dashboards and analytics

iii) What’s the link between returns and lifetime value? For retailers selling products with high returns rates (apparel for example), being able to differentiate between truly profitable customers and persistent returners is gold.


iv) Where are your most profitable customers located? This can help in many planning scenarios including store openings and closures, collection points, localising social spend.



That's the magic of CLV in a nutshell. If you have a single customer view and are measuring customer lifetime value, you’ll be in a much better position to calculate the right approach to segmentation. And that’s what we’ll focus on in the next blog in this series! Here's what's coming up in future blogs in this series:


Part 4: Calculating the right approach to segmentation

Part 5: Understanding the relationship between products and customer loyalty

Part 6: Using device usage insights to optimise platforms.


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About Datitude

We do data! Datitude is a fast, robust, customer and retail data platform that helps businesses make sense of their data. Combining data warehouse and data lake, systems integration, and business intelligence, Datitude's platform integrates, processes and unifies your key data from multiple systems and channels, both on and offline, to unleash the most powerful customer and trading insights. Whatever the data source, we can process it.


Business intelligence, reports, and analytics are built-in and automated as standard, including visualisations and sophisticated interactive dashboards. Get a single customer view, lifetime value, segmentation analysis, product and customer loyalty analytics, device insights, and more.


If you need to turn your data into valuable intelligence, insight, and action, let's talk!