In the marketing world, understanding the long-term value of a customer is crucial to the success of any business strategy. Lifetime Value (LTV) represents the total value that a customer brings to a company over the entire period that he or she remains active. Not only does this indicator allow companies to assess the profitability of their customer acquisition strategies, but it also provides insight into consumer behavior and preferences.
Calculating LTV helps identify the most valuable customers, allowing companies to focus their resources on market segments that provide the greatest return on investment.
Using machine learning models and predictive analytics, companies can estimate future customer value by leveraging a wide range of behavioral and transactional data. This approach allows them to anticipate consumer behavior, optimize marketing strategies, and make informed decisions.
Definition and Formulas for Calculating Customer Lifetime Value
Customer Lifetime Value is defined as the total net revenue that a company can expect from an individual customer over the lifetime of their business relationship. It is essential to clarify that cLTV is not a predictive value, but a value calculated from historical data on customer buying behavior from the first purchase to the present.
It is common to use the terms “lifetime value” and “customer lifetime value” interchangeably, but there are significant differences. Lifetime value is calculated at the aggregate level for all of a company’s customers, while customer lifetime value refers specifically to the value generated by an individual customer. Calculating LTV at the aggregate level provides important information at the strategic level, but it can also introduce significant distortions, since purchasing behaviors vary widely among different customers. Therefore, it is preferable to calculate cLTV at the individual consumer level to obtain an accurate estimate. When this is not possible, customer segmentation can be performed by calculating an LTV for each of the customer segments.
Calculation of Customer Lifetime Value
There is no single formula for calculating cLTV, but rather several methodologies that can be applied depending on the context and available data. Below, two commonly used formulas are outlined:
- Simple Formula:
cLTV= (Average transaction value) × (Average purchase frequency) × (Average customer lifetime)
This formula takes into account the average transaction value and average purchase frequency, multiplied by the average length of customer relationship.
- Advanced Formula:
cLTV= (Average transaction value) × (Average purchase frequency) × (Margin) × (Churn Rate)
This more sophisticated version also includes margin and churn rate, which represents the probability that the customer will continue to be active for the company.
Prediction of Customer Lifetime Value
Regardless of the final formula that will be used, to calculate the predictive cLTV certainly will require the prediction of its main components:
- The average monetary value of the individual customer;
- The number of products purchased in the next n months;
- The probability of the customer’s survival in the next n months.
Prediction of cLTV cannot be made indefinitely into the future; it must be limited to a reasonable period based on available data, such as n days, n weeks, n months, or a year. Once these components are estimated, they can be combined to obtain a good prediction of customer lifetime value.
Approaches for Calculating Customer Lifetime Value
Just as there is no single definition of cLTV there is also no single approach for its prediction, as reported by several authoritative studies including Kumar and Reinartz (2018).
The probabilistic approach is commonly recognized as one of the most effective methods for calculating cLTV. To implement such an approach, it is essential to have transactional data, which must include:
- A user ID linking all transactions made with the company;
- A transaction ID;
- The date of each transaction;
- The amount of each transaction.
This data is the minimum set needed to begin both cLTV calculation and cLTV predictive analysis. From the transactional dataset, three key variables are derived:
- Recency: recency of the last purchase;
- Frequency: frequency of purchases;
- Monetary: average value of purchases.
Probabilistic analysis of these three variables individually is relatively straightforward; however, the challenge lies in modeling beyond the three variables:
- Customer Heterogeneity: a model that captures the different buying habits of customers is needed;
- The Probability of Survival: estimating the probability that a customer will remain active or abandon after each purchase.
A significant limitation of the probabilistic approach is that it can only be applied to returning customers, that is, those who have made at least two purchases. For new customers, alternative solutions must be adopted.
Solution 1: Probabilistic Approach and Clustering
A first solution is to use the probabilistic approach for returning customers and then apply clustering algorithms. In this way, new customers are assigned to clusters of existing customers with similar behaviors, allowing their cLTV to be estimated based on the cluster they belong to. This methodology, although approximate, showed optimal results.
Solution 2: Machine Learning Approach
A more complex but potentially more accurate approach is to abandon the probabilistic method in favor of machine learning algorithms. This approach uses classification algorithms to estimate the survival probability of customers and regression algorithms to estimate the monetary value of future transactions. However, estimating survival probability can be particularly complex, especially in noncontractual businesses where the definition of an “active” customer can be subjective.
Regardless of the approach chosen, it is essential to have more than transactional historical data for model training and validation. Only through careful training and validation can reliable predictions of future customer behavior be obtained.
What data to use to calculate Customer Lifetime Value
To calculate Customer Lifetime Value, it is essential to have the main transactional variables: recency, monetary and frequency. These variables form the indispensable basis for initiating any kind of analysis. However, to apply both advanced clustering models (first solution) and Machine Learning predictive models (second solution), it is necessary to integrate additional variables such as:
- Transactional variables: Shopping cart composition;
- Behavioral variables on the site: Pages visited, acquisition channels, events recorded during navigation;
- CRM variables: Demographic information, history of customer service interactions, market segments they belong to;
- Computed variables: Interests, membership clusters, complex behavior variables, purchase preferences.
When integrating data from multiple sources, it is crucial to ensure data quality. Inaccurate or incomplete data can compromise the validity of the most sophisticated models.
Common problems in data management
The most frequent problems encountered in relation to data quality are:
- Limited lookback period: Too short a lookback period makes long-term predictions difficult and inaccurate;
- Data in silos: CRM and behavioral data often reside in separate systems, preventing integrated analysis. This problem limits the amount of information available for models, reducing the effectiveness of predictions;
- Data quality and availability: Unreliable or unavailable data for all potential clients reduce model performance.
To address these issues, it is essential to adopt data integration strategies that unify different data sources and implement rigorous quality controls.
Information Obtainable with Predictive Customer Lifetime Value
The application of predictive customer lifetime value offers a wide range of valuable information beyond the pCLTV itself. In fact, already at the data preparation stage, details such as user-level recency, frequency, and monetary value can be obtained, which are useful for significantly enriching the customer profile.
This information can be further processed through RFM analysis, client clustering, and identification of top clients. Even without the use of predictive models, these data provide a solid basis for improving segmentation and understanding of customer behavior.
The implementation of predictive models, on the other hand, not only enables estimation of future customer value, but also enriches the company database with detailed and useful information for segmentation and customization of marketing strategies, such as:
- Forecast of Products Purchased: Estimation of the number of products the customer is likely to purchase in the future;
- Customer Survival: Analysis of the likelihood that a customer will continue to make purchases over time;
- Value of Future Transactions: Estimation of the value of future transactions, based on historical customer behavior.
Using these approaches, companies can transform raw data into strategic insights, thereby improving their decision-making and competitive capabilities.
