Customer lifetime value

We will provide an overview of how this model is built, its data requirements, and the predictions it can make. We will also provide details of use cases showing how this model can be integrated into your personalization efforts.

In particular, we will cover:

The CLV model is a custom-built machine learning model that predicts the future spend for each customer–a person that has converted at least once–over a specified time horizon. Also, it estimates the churn probability, future purchase frequency, and future Average Order Value (AOV) for each customer.

The features used to train this model are constructed from a visitor’s previous transaction history and can be provided from a number of sources, including QProtocol or your own CRM/CDP system.

The following diagram provides a high-level overview of how this model functions:

The model needs three data points for each historic transaction:

All three of these are required for the model to function and can be provided from a number of sources, including QProtocol (QP), GA360, or your own CRM or CDP system.

The following table summarizes the data requirements for each data point:

The CLV model uses a Bayesian hierarchical framework to infer three parameters:

Mean transaction value predicts how much each customer will spend, on average, in each future transaction. Churn probability and effective frequency are strongly related and can be illustrated by the following example.

Because churn is not observed in non-contractual settings, the model must be realistic enough to infer this latent variable by teasing apart churn and effective frequency.

Bayesian hierarchical modeling is a method of statistical modeling composed of multiple levels (hierarchies) that uses Bayes’s rule to estimate probability distributions using a combination of prior knowledge and observed data. Our CLV model has been crafted by our expert team of data scientists to ensure the best possible performance.

Our models produce high predictive accuracy relative to the domain. We measure predictive performance using a metric known as mean absolute error. Our models typically achieve a mean absolute error around 50x smaller than a benchmark model that takes the amount spent by customers in the past and extrapolates this into the future.

For example, the mean absolute error of the model could be around USD 92, with an average observed yearly transaction value of about USD 36. That error is significant compared with the actual average, but it’s still much better than simple extrapolation. Moreover, the error is mainly due to one-time purchasers that never buy again; for customers with even moderate loyalty, the model is far more accurate.

Each customer’s CLV is calculated daily or weekly by a batch job. This job outputs timestamped estimates of CLV and its component parameters into a table in the client’s BigQuery project, for example,clientName__v2.clv_output_user_email.

In addition to CLV, several other (derived) quantities are calculated:

For each of these, alongside the prediction, two percentile columns are provided to indicate the degree of uncertainty in the predictions. Finally, we show the data that were used to train the model as well.

The following table provides a detailed overview of the output in the client project. This output can be actioned by using a Derived Dataset (DD).

In the following image, the predictions, clv_pred and realizations–what has actually been observed, are plotted for a client with a period of 12 months and user_email as primary key. The model is able to capture the fact that there are two peaks in the distribution, corresponding to one-time purchasers and the average, loyal customer.

There are many use cases for CLV, including segmentation, investment decisions, and optimization.

To start using this model in Qubit experiences, contact your Customer Success Manager and ask for the early access signup form. Submit this form and enter CLV as the project name you are interested in.