If you were unaware Customer Lifetime Value calculations have been digitally disrupted, you might be interested in how AI has changed the game for retailers.

Retailers have long realised it’s typically easier to get an existing customer to make a repeat purchase than to win over a new customer and then get them to buy something.

That appears to be even more the case in 2024, with widespread agreement that customer acquisition costs are increasing. And it’s not like it was cheap in the past – this Harvard
Business Review article from a decade ago was already warning that “acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing one”.

But until a few decades ago, it seems nobody had investigated this phenomenon with much scientific rigour. 

How customer lifetime value all began

They don’t get a lot of respect nowadays, but it was the telemarketers of the 1970s and 1980s who got the Customer Lifetime Value (CLV) ball rolling.  After detailed studies of their customers’ purchasing behaviour, telemarketers developed the RFM method – Recency, Frequency and Monetary value. This ‘method’ claimed that data about when a customer last made a purchase, how many purchases they made, and their average purchase amount are indicators of future purchases.

In the mid-1980s, David Schmittlein, presently a Professor of Marketing at the MIT Sloan School of Management, worked out the arithmetic (the Pareto/NBD framework) to turn the lagging indicator of RFM data into forward-looking estimates of what’s now commonly known as Customer Lifetime Value (CLV).     

Calculations of CLV have become ever more elaborate, mainly due to technological advances. But if you thought sophisticated CRMs and analytics tools had made calculating the lifetime contribution a customer is likely to make to your business’s coffers straightforward, I have some disturbing news for you.

Almost half your CLV attribution could be wrong 

According to Amperity CTO, Derek Slager, retailers often mistakenly calculate CLV by only focusing on transaction data.

“What we found in building predictive customer lifetime value algorithms on top of rich, unified customer data is the correlation between the transaction data and all the other data is a really key signal in making good predictions. The other is that building this without a unified view of the customer is even worth the time. It isn’t. We’ve done the math and we found that 46% of customer lifetime value attribution is completely wrong if it’s not done on a unified data foundation,” he says.

As flagged above, accurate estimations of your customers’ CLV helps with everything from segmentation to deciding what marketing campaigns to create for those segments. This raises the question of whether your business’s CLV calculations are as accurate as you believe them to be.

How AI is changing CLV calculations for retail

Your CLV calculations are unlikely to be wildly off the mark, even if you use an ‘old-school’ CRM or CDP. But neither are those calculations likely to be as accurate as they now could be. I’ll once again quote Derek Slager, to explain why:       

Artificial intelligence (AI) is revolutionising the software solutions that brands use for marketing, particularly around identifying, understanding and connecting with their customers. New advances in AI and machine learning (ML) have unlocked capabilities once thought impossible. 

With a CDP, brands can ingest raw customer data across many sources – from online and in-store interactions to loyalty programs, email engagements and financial systems. Once the CDP has captured that data, it uses ML to resolve identities even when records lack unique identifiers across systems.

AI connects essential customer information, including demographics, loyalty, email engagement, and product purchase data, allowing brands with this software to collect richer, cleaner data. This in turn improves ML modelling performance. As a result, brands can use this insight to understand customer lifetime value, enabling them to make strategic decisions related to marketing, customer acquisition and customer retention.

A practical retail example

For example, say a cosmetic company is interested in calculating the lifetime value for a particular customer. AI algorithms, such as machine learning models, can analyse vast amounts of data to predict customers’ future behaviours.

The software can analyse their past purchases, preferences for online or in-store shopping, frequency of purchases and more, all of which help to more accurately predict their future buying habits. And through these insights, marketers can more effectively reach customers with more relevant ads and product offers, increasing retention and driving sales.

How to collect richer, cleaner data 

To recap, an accurate data foundation is required for accurate CLV calculations. Unfortunately, many retailers are still struggling to unify their offline transactions with digital interactions and most are mis-identifying as many as a quarter of their customers. 

AI-powered models predict customer lifetime value (and order frequency, average order size, risk of churn, and product affinity). The predictions made by these AI-powered models are based on a complete picture of the customer that includes full historical data for both online and offline.

I’ll end by returning to a point I made at the beginning of this article – it’s almost always easier to get an existing customer to make another purchase than to win over a new customer.  And in a down economy, customer retention becomes life-or-death.

For brands and retailers, the ongoing economic crunch and inflation crisis turns up the pressure on every aspect of operations. Businesses are looking for ways to do more with less, cutting costs by any means necessary in order to stay lean during a potential recession.

At the same time, consumers have less money to spend with their favourite brands – let alone with new sellers. In this economic environment, organisations will have to shift their focus from customer acquisition to retention. And while many sales and marketing teams have used the data revolution to inform their customer acquisition strategies – targeting potential customers who share attributes with their best existing customers – first-party data will prove even more important for retention.

Billy Loizou is area vice president of APAC for Amperity.