Customer Life Time Value (CLTV)
Tags: Rfm Analysis, Crm Analytics, Data Science, Cltv, CRM

In this article, I will discuss the ways to reveal this potential value.

This is the second and the final part of the article series about Customer Value. In the first part, I discussed the core of customer value calculation: RFM metrics and scores.

This final part will be about Customer Life Time Value prediction using BG/NBD and Gamma Gamma Models.

With RFM Analysis, a company can have a vision of its customers’ current behavior by scoring and segmenting them based on their purchasing habits and therefore be able to maintain an effective CRM.

What is CRM?

Customer relationship management (CRM) combines practices, strategies, and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle. It tries to understand the customer profile of the company and to communicate according to these profiles. Analyzes customers, finding audiences belonging to certain behaviors, and organizing campaigns suitable for that audience. Moreover, CRM targets to gain new customers other than existing customers. All in all, CRM offers you the opportunity to improve your profitability, productivity, and efficiency.

What is CLTV?

CLTV, an alias for Customer Life Time Value, is the ultimate metric used to understand and influence the customer’s behavior by predicting each customer’s equity covering the period where he/she is alive. Being alive is a metaphor for “a customer who still purchases from the company.” The existing customers’ purchasing habits will be of great help for creating different patterns and measuring the CLTV of a new customer and the existing ones.

CLTV is based on 4 metrics; let’s examine them one by one:

Recency: The period between the first and the last purchase date, that is the duration where we can observe the behavior of this customer,

T: The age of the customer within the company that is the period starting from the first purchase date until today,

Frequency: Number of purchases the customer has, that is how often he/she purchases,

Monetary: The average of the total prices among all the purchases.

CLTV Calculation compares customer behavior. Each customer is evaluated according to their purchasing status. There are different approaches for calculating CLTV, and I will share the most common one:

CLTV = (Customer Value / Churn Rate) * Profit Margin

Customer Value = Average Order Value * Purchase Frequency

Churn Rate = 1 — Repeat Rate

It is possible to degrade the above equations and reach this one:

CLTV = Expected Number of Transactions * Expected Average Profit

So how would these values be calculated?

Expected Number of Transactions: Calculated using BG/NBD Model

Expected Average Profit: Calculated using Gamma Gamma Model

We are projecting future time using some models(BG-NBD and Gamma-Gamma) with CLTV Prediction. We make predictions about the customers’ process not only now but years later.

BG/NBD Model

Given the Frequency, T, and Recency values of a customer, it is possible to predict the expected number of purchases that he/she would make within a specified time period.

Gamma Gamma Model

It is used to estimate the average monetary value of customer transactions. Given the Frequency and Monetary values of a customer, it is possible to predict the expected average profit of a customer.

Online Retail II dataset will be used for this approach.