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.
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 co-founder and CTO Derek Slager:
What do people get wrong when they're predicting customer lifetime value? This is an important one; I think there are a couple of really key things that people get wrong. One [is] 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.
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-powered CDPs are changing the CLV game
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 from 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.
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
I can't speak for all AI-powered CDPs, but I can explain how Amperity works.
To recap, an accurate data foundation is required for accurate CLV calculations. Unfortunately, many brands are still struggling to unify their offline transactions with digital interactions, and most are mis-identifying as many as a quarter of their customers.
In the case of Amperity's AmpAi, AI and ML are used as follows.
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.
That complete picture is assembled by AI and ML matching algorithms that unify online and offline first-party data with the nuance of a human but at massive speed and scale. (A transparent process highlights how and why the AI merged data, facilitating easy audits and ensuring an accurate data foundation.) Also, sophisticated AI matching keeps profiles up to date by accounting for the frequent changes in customer data, such as a new email address, moving to a new home, shifting engagement patterns and preferences, etc.
What CLV calculations should look like in 2024
To recap, the CLV-calculating challenges your organisation is almost certainly facing are maximising data quality and integration, accurately defining the lifetime period, and keeping pace with evolving customer behaviours.
The MarTech presently best placed to help your organisation address these challenges is an AI-powered CDP. I'm not arguing an AI-powered CDP can solve all your organisation's marketing challenges. However, for the reasons explained above, it's a powerful tool for unifying and cleansing customer data from multiple touchpoints. And that helps combat the inaccurate or fragmented data that hinders CLV calculations.
