How can you calculate customer lifetime value with Markov chain models?

1 What are Markov chain models?

A Markov chain model is a type of stochastic process that describes how a system changes over time based on a set of possible states and transition probabilities. In other words, it can tell you the likelihood of moving from one state to another in the next time period, given the current state. For example, you can use a Markov chain model to predict the weather, based on the previous day's weather and the probability of rain or sun. In marketing analytics, you can use a Markov chain model to track how your customers move between different stages of engagement, such as active, inactive, or churned.

4 What are the limitations of using Markov chain models for CLV calculation?

Using Markov chain models for CLV calculation has some limitations that should be taken into account. For example, these models require extensive data and assumptions to estimate the transition and reward matrices, which may not be available or accurate for your business. Additionally, they assume that the transition probabilities are stationary and independent, which may not be true for customers who are influenced by external factors or feedback loops. Moreover, Markov chain models may not capture the complexity and heterogeneity of customer behavior if they have multiple touchpoints with your business, or account for future changes in your market or industry.

5 How can you improve your Markov chain models for CLV calculation?

If you want to improve your Markov chain models for CLV calculation, it's important to take a data-driven and customer-centric approach when defining and segmenting your states. You should also update and validate your transition and reward matrices regularly, based on the latest data and performance metrics. It's also helpful to test and compare different scenarios and sensitivities, such as how changes in the discount rate, transition probabilities, or reward values affect your CLV. Additionally, incorporating additional features and variables, such as customer attributes, preferences, or feedback can help make your Markov chain models more realistic and relevant.