The Customer Lifetime Value or Customer Life Cycle is a metric used in create uk phone number to calculate the economic value that a customer represents for the company during a certain period of time. Also known as CLV or CVC, it is used to make decisions that prioritize profitability in actions aimed at attracting new customers, building loyalty and retaining existing users. In our article, we discuss the benefits of using machine learning techniques to estimate and predict CLV effectively.Why is Customer Lifetime Value important in marketing?
Knowing the Customer Lifetime Value is very useful because it allows:Segment the customer base based on the profitability of each cohort of users, identifying their characteristics and determining which group to focus efforts on (in all senses: product development, marketing…).
Set the acquisition cost for each new client and, therefore, establish a reference framework within which to frame the investment for marketing actions.
Create personalized experiences for the most interesting customers for the brand (for example, with special offers or up-selling or cross-selling proposals ) and design a loyalty program according to their profile in order to increase the CLV.
There are different formulas to calculate the Customer Lifetime Value depending on the business model with which the company operates (subscription-based or free) and the type of costs that are considered in the relationship with the customer. The Harvard Business Review publication proposes this .How to estimate the Customer Lifetime Value with machine learning?
The algorithms of machine learning applied to the analysis of the Customer Lifetime Value are based on the historical data to make predictions and infer the behavior of customers, in order to estimate the number of purchases you will make in the future . The strategic advantage of the use of machine learning in this area is the ability to optimize the current marketing strategy with an emphasis on the long term.
If we focus on businesses that are not subscription-based, different models can be applied , including: The classical probabilistic models
In short, they are above gulf email list all the Pareto / NBD model or its evolution, the BG / NBD model . These models assume that, from time to time, the customer buys and after a specific period, the customer stops buying (the relationship with the brand dies ). Its behavior in both cases depends on the transaction history.. Machine learning models with deep neural networks (DNN)
Widely used to work with time series, these models are ideal for finding patterns over time and being able to infer them in the future . It is a very flexible tool that is better adapted both to the data that we currently have, and to the data that will be inferred in the future. By their nature, they need to work with a large amount of data to provide good performance . Therefore, to implement them in the customer life cycle, it is necessary to create data lakes and data warehouses with which to feed them (such as BigQuery ). In addition, it should be mentioned that they are much more computationally intense when compared to traditional algorithms. Machine learning models in production have to find a balance between their goodness, computing time (the more time, the more money we have to invest) and our ability to offer them data with which to feed them, so the choice of model with that work must always be tailored to the needs of the brand .
We can use machine learning models with deep neural networks to predict the Customer Lifetime Value
The starting point for both types of models is common and consists of classifying customers into segments. The quality and quantity of transaction data is essential here , as it is the basis for defining the importance of each customer (who is assigned an individual ID) based on the value they generate. For this classification, it is common in marketing to use RFM models whose acronyms refer to the three metrics on which the creation of clusters is based : recent transactions (recency) , the frequency of purchases (frequency) and their value (monetary value). ) .Choosing one way or another depends mainly on the characteristics of the datasets you have. In general, the classic probabilistic models work well when there is a good number of repeated purchases by each customer, if you do not have access to CRM or web analytics data or when the data history is not very long in time . On the other hand, models based on deep neural networks are suitable if you have a more powerful data history and if you have access to extra tools that can be integrated, such as CRM or the website itself, whose information will serve to feed algorithm. Machine learning for the study of the present and future clientIn times of uncertainty in which the volatility of consumer habits is constant , the massive analysis and treatment of data with machine learning techniques becomes crucial for decision making to adjust to current reality, without harming long-term strategy.Along with advanced attribution models and omnichannel analytics , the Customer Lifetime Value study is another area in which the application of machine learning techniques stands out for its potential to refine the digital marketing strategy and optimize the return on advertising investment. .