Customer Lifetime Value Prediction

Identify your most valuable customers by using the AI & Analytics Engine to quickly, easily and affordably predict CLV with machine learning.

Use CLV template
Get a demo
CLV Prediction icon

What is customer lifetime value?

Customer lifetime value (CLV) is a measurement of the total revenue a customer generates for a business over their customer lifetime. CLV considers purchase value, purchase frequency and customer lifespan. CLV is a core metric to understand when monitoring the financial health of a business.


Why should you predict CLV?

Every business wants to acquire more customers, but not all customers are equally valuable. The Pareto principle states that roughly 80% of an output is determined by 20% of the input. In other words, a business can expect around 20% of their customer base to deliver around 80% of the revenue.

These 20% are the VIP customers, and every business needs to understand who they are.

This is why predicting CLV is important.


Making use of CLV predictions

Once you’ve got CLV predictions, how do you take advantage of this information? It begins by segmenting customers based on CLV.

Firstly, by properly identifying the high-value customers, you can strategically allocate marketing and sales resources to make sure they don’t churn. This may involve premium perks such unique discounts or programs.

Secondly, you’ll be able to pinpoint the particular characteristics common to high-value customers and use that information to maximize the profitability of future marketing campaigns.


Measuring the ROI of CLV predictions

Predicting CLV clearly has a number of benefits on business revenue and growth, but it’s critical to quantify to understand the effect it’s having.

The first aspect to be measured is the effect that the CLV prediction has had on churn rates of the existing high-value customers. This can be done by splitting them into a control group and a group with high CLV targeted marketing strategies and comparing the churn rates. Another method would be to compare the churn rates with their predicted churn rates.

The second aspect is to measure the effect of targeting customers with a higher predicted CLV. This will take some time to gather data, but a simple way to quantify this is with return on ad spend (ROAS) and customer acquisition cost (CAC). An alternative method would be to measure the average CLV of targeted customers compared to the control.


How to predict CLV?

Predicting CLV for any individual is complex, with average purchase amount, purchase frequency and customer lifespan all needing to be forecast. Small inaccuracies compound, and can result in wildly large ranges for CLV.

Predictive machine learning algorithms can easily analyze large customer information and transaction datasets easily, offering a practical solution that is both time efficient and cost effective for a business.


Predicting CLV with no-code machine learning

The AI & Analytics Engine is an easy-to-use machine learning tool, and contains a templated solution allowing you to predict customer lifetime value and identify your most valuable customers.

Requiring no coding or data science experience, the Engine’s CLV Prediction Template is perfect for business professionals looking to build an end-to-end prediction pipeline.

The Engine's CLV Prediction ML Solution Template

With any CLV prediction tool, it's important that you're able to tailor predictions according to the nature of your business.

The Engine's CLV Prediction app builder pipeline is a simple 4 step process, that provides guidance yet offers flexibility.

To ensure results are meaningful and actionable, the template allows you to choose to predict either customers transaction amount, or number of transactions, and how far in the future you want to predict.

All you need to get started with the CLV Prediction template is;

  • Transaction data (required)
  • Customer information data (optional)

Streamlined machine learning for data-driven professionals