Creating a Customer Lifetime Value Indicator

The Customer Lifetime Value Model is a predictive analytics tool in the AI Workbench. It uses historical customer data to understand patterns and predict the likelihood of a customer, or a group of customers, improving their engagement with your products or services. The model leverages sophisticated algorithms and allows customized CLV formulas to provide actionable insights on key customers who are highly engaged with your brand.

Predict Customer Lifetime Value probabilities at both profile and group levels.

The model can predict Customer Lifetime Value for individual customers (the profile level), allowing for highly personalized interventions. It can also predict Customer Lifetime Value for a collection of customers (the group level), enabling strategic business decision making.

Use advanced algorithms to train and analyze predictive behaviors.

The Customer Lifetime Value Model utilizes sophisticated machine learning algorithms, for instance Gradient Boosted Trees (GBTs) and Multilayer Perceptron Classifier (MLPC), to identify complex patterns in customer behavior that indicate brand engagement. These algorithms learn from your historical data and continuously improve their accuracy over time.

Enable business-specific customizations like CLV Formula and observation/prediction windows.

You can tailor the CLV Model to your specific business needs by defining what constitutes value for your business (e.g., sum amount of the last orders placed in the 90 days, average orders sums in the past year, etc.). You also control the timeframe for the historical data analysis (observation period) and the timeframe for the CLV prediction (prediction period).

CLV Formula

This defines the projection that indicates the customer value for your business. It's the trigger that tells the model how much a particular customer is engaged. For example, for an e-commerce business, it could be "Sum of purchases in the last 90 days."

Prediction Period

This is the timeframe into the future for which the model predicts CLV. For instance, you might want to predict CLV for the next 30, 60, or 90 days.

Observation Period

This is the timeframe of historical data the model uses to identify CLV patterns. It's the period the algorithm ‘learns’ from. A longer observation period can provide more data for the model to learn from, but it also means older, and thus perhaps less relevant, data might influence predictions.

You can define CLV Formulas using the AI Workbench's flexible editor, choosing from four options:

1. Visual Editor: A user-friendly GUI interface for building formulas without coding.
2. Visual Editor and Text: Combines the visual editor with a read-only text view of the formula.
3. Advanced: Visual Editor and Editable Text: Allows you to edit the formula directly as text while also seeing the visual representation.
4. Advanced: Editable Text: For users comfortable with writing formulas directly as text.

The AI Workbench provides pre-built CLV Formula templates to get you started quickly. These templates cover common CLV scenarios, such as customer order frequency and amounts, and other high engagement patterns.

Example: "Sum Amount for Last 90 Days" Template

This template defines a CLV Formula projecting the sum of all orders amounts for the last 90 days. It looks like this:

sum(Activities.Orders.Amount , inLast(90 day))

You can use this template as is or customize it by modifying the number of days. For instance, changing '90' to 60 would constrict CLV to only include orders in the last 60 days.

This understanding of the CLV Model and its key components will allow you to effectively use the AI Workbench to predict and address how valuable some customers are. In the next section, we'll dive into the practical steps involved in configuring and running a CLV Model.

1. Go to Customers → Indicators from the left-hand side navigation menu.
2. Click Create New Indicator.
3. Choose Customer Lifetime Value under Predictive Indicators on the Create New Indicator popup and choose Create.
4. Choose the prediction time frame, segments, processing purposes, the activity representing the order information, and its attribute representing order amount.

   This is a simplified view of the full CLV model settings. Settings are covered in the next section, which explains how to fine tune the model run in the AI Workbench.

   Once you choose Save, the new indicator will be activated and calculated. You will be able to see the new indicator values on customer profiles or groups when the calculation is completed.

   

1. Access the AI Workbench

   First, choose AI Workbench from the top Entity dropdown. After that, from the AI Workbench side panel, choose the Manage Models icon.

   
2. Choose the CLV Model Type.

   Select Customer Lifetime Value (Profile) to predict CLV at the profile level, or

   Select Customer Lifetime Value (Group) to predict CLV for customer groups.

   
3. Define the Model Details
   • Predictive indicator name: Give the CLV model a name that reflects its purpose (for instance, "VIP Customers Prediction").
   • Provide a Description (optional).
   

   The name is considered to be the name of the predictive indicator, hence the field name. Once you complete the model run, a new predictive indicator will be created in the CDP, although it’s not activated by default.

   
4. Segment and Group Selection (optional)

   Choose the target population or a subset of your analytics data by selecting a segment. For example, a segment could be a set of profiles or groups from a particular city, e.g., "Customers from New York".

   For group-based predictions, specify the relevant group type (for instance, product category or subscription type).

5. Set Processing Purposes (optional)

   Ensure data processing permissions are configured under Processing Purposes.

   

   If you don't choose a processing purpose, the model will run on all profiles or groups and their orders, i.e., no matter whether or which processing purposes are granted.

6. Adjust CLV Formula

   Define a new CLV Formula or choose one from the Templates(for instance, "Sum Amount for Last 90 Days").

   

   The CLV Formula will be used to determine how valuable a profile or group is. You can use built-in templates or create a custom CLV Formula in the editor.

7. Customize Prediction Parameters.
   1. Set Prediction period (for instance, 6 months).

      The window of time for which the model produces the dependent variables or predictions.

   2. Define the Observation period (for instance, 12 months prior)

      The period of time that determines the independent variables of the model.

   3. Configure High/Low percentile thresholds for high/low value categorization.

      Profiles or groups that are predicted to have a high value at or above/below this probability will be labeled high/low value, respectively.

8. Advanced Settings (Optional).

   Activate a specific algorithm, and configure Evaluation metric (e.g., RMSE), Split ratio, and cross validation options.

   

   The advanced section is a powerful tool for data scientists. It allows you to fine-tune your model by selecting different algorithms and evaluation metrics. However, this section is optional and disabled by default. If you don't need to use it, simply move on to run the configured CLV model.

   If you want to explore the advanced section, just toggle it on. Once it's open, you can further customize the algorithm you've chosen.