The Customer Lifetime Value (CLV) Model will give you predictive KPIs that allow you to create audiences with attributes like high future value, high risk of churning, high historic value and a lot more. It will help you dig deep into the behavior of your users and focus on the customers who are most valuable to your business. The CLV Model must be configured, validated and trained to learn from your data and the behavior of your customers. Once activated, you will find a number of attributes in the Audience Builder that can be used to define your audiences.
The CLV Model setup is available for customers with a Customer Data Platform, in the Raptor Control Panel, under the AI Models headline.
🔍 Note: The model will typically be most effective when utilized on your buy data, but it can be used on any of your data schemes e.g., visits, add to basket, downloads etc. In such cases, the Attributes listed below will have rather different meanings - for more details, see the Non-Monetary Calculations Guide.
Buy-events or Aggregated Orders?
For the purposes of creating a CLV, there is very little difference between using Buy-Event data and Aggregated Orders, and you can largely use whichever you have available. The only difference is that a few of the stats below may vary slightly, if some customers have made multiple orders on the same day.
List of Customer Lifetime Value attributes
As soon as the CLV model is activated, you will find the following list of attributes when you select the have-filter in the CDP's Audience Builder.
- Repurchase probability (%): Repurchase probability represents the probability of the customer placing a new order at any time in the future. It is the opposite of churn, but churn can be calculated on the basis of repurchase probability with this formula: Churn = 100 - 'repurchase probability'
repurchase probability is a prediction by the AI model. It is based on the number of orders (Frequency), days since last order (Recency), days since first order (Time), the personal "average days between orders" for the customer and the drop out rate for the shop. The drop out rate is an internal value predicted by the model. It is shop specific and represents the ability to keep customers coming back.
For example: If a customer has a repurchase probability percentage of 75 %, her churn risk is 25 %. - Historic value last 365 days: The sum of the value of all orders by the customer during the last 365 days.
- Historic value all time: The sum of the value of all orders by the customer
- Predicted future value next 365 days: The predicted value of the customer the next 365 days. It is based on predicted number of orders next 365 days and average order value.
- Predicted Customer Lifetime Value: The sum of historic value and future value the next 365 days for the customer.
- Predicted number of orders next 365 days: This is a prediction by the AI model and tells you how many times a customer will place an order within the next 365 days. It is based on the customer's buy frequency and the predicted alive score.
- Days since first order: Number of days since the first order by the customer.
- Days since last order: Also known as recency. Number of days since the last order by the customer.
- Number of orders: Also known as frequency. The number of orders a customer has placed. Multiple items bought on the same day are aggregated into one order.
- Average order value: Also known as monetary value. The average value of the basket. It is equal to the total historic value for the customer divided by the number of orders the customer has placed. Multiple items bought on the same day are aggregated into one order.
- Average days between orders: Is the number of days between the first and last order divided by the number purchases minus one.
For example: 3 orders in 100 days (first order on day 0, last order on day 100) equals 50 days between orders on average. - Inactivity score: Days since the customer placed her last order divided by the average days between orders for that customer. On the day a customer places an order, this score will be 0. Until the same customer buys again and has not reached her personal buying average, the inactivity score will be between 0 and 100. If she exceeds her personal buying average, the number will be more than 100.
For example: If a customer places an order every 10 days in average, but today it is 15 days since she placed her last order, the inactivity score will be 150.
Customers must have placed at least two orders to get an inactivity score.
How to setup a Customer Lifetime Value Model
You will find the CLV Model setup from the menu under the headline AI Models. Go to the CLV model and click + Create new model to go to the setup page.
1. General Information
Give your model a name and description so it is easy for you to recognize it. The name (suffixed by 'CLV') will be displayed as a source in the Audience Builder and on a card on the overview page. 
2. Select schema & map data
To create a data foundation for the calculations and predictions of the CLV model, you need to let the system know what data the model should be based on.
First, you must select a schema. Schemas come from the Data Manager and represent how your data is mapped into Raptor's system.
Click the '+ Create mapping'-button to open the mapping pop-up.
In step 1, you see a dropdown with a list of eligible schemas. Select the desired schema (most often, this will be a buy schema).
The CLV model is applicable for all types of schemas that have been created and populated via the Data Manager.
Click Continue.
In step 2, you need to map your data to a CLV model schema. You have three options for doing so: 
- Price & Quantity: Select this schema if your data contains both a value (most often this will be the price of a product) and a quantity (most often this will be the amount of the same product the customer bought e.g., five identical t-shirts or three packs of diapers)
- Subtotal: Select this schema if your data only contains a value (most often this will be a subtotal on your buy events)
- Other events: Select this schema if one row of your data equals one value e.g., a pageviews or visits
Select the suitable schema and click Continue, which takes you to Step 3: Map data
In step 3, you select the source of the fields you wish to map, and the field(s) that correspond to the fields of the CLV schema on the right (Value and/or Quantity).
Press 'Create' to save your selections and close the pop-up.
👀 Usecase: Combine CLV predictions from offline and online stores
You have the possibility to create multiple mappings in one CLV model. By clicking the '+ Create mapping'-button, you can add data from different sources to your model. If you operate both an online and a physical store, it is recommended to combine buy events from both stores. This way you can take customers who have low online activity but might buy frequently in your physical store into account and base for instance churn predictions on a full picture of customer engagement.
Here is how it looks when two mappings are combined:
In this case, we recommend you to build three CLV models:
- A model based on your online buy data
- A model based on your offline buy data
- A model that combines online and offline buy data
This way you have the opportunity to build predictive audiences for both your online and offline stores, and for the people who buy from both.
🔍 Note: When combining two data sources, as in this case, the time period of the data should be approximately the same, and users should be recognizable within the CDP across the two sources.
3. Schedule
Set the time you wish to run the CLV model. When it runs it will include any new data ingested into the CDP and recalculate the KPIs for each of your profiles. It will run once a day, however, it is possible to run it manually regardless of the schedule (see under the headline "The overview page" below).
When is a good time to schedule your CLV model to run?
👀 Usecase 1: If you ingest POS data to the CDP once a day at 9 o'clock in the evening and your model include these data, you should schedule the CLV model to run after the POS data has been ingested, e.g. at 11 o'clock. That way your CLV model is calculated on the freshest possible data.
👀 Use case 2: If you use values created by the CLV model to determine which customers to send an e-mail to, you should time the update of the model close to the email send-out time to make sure that the message of the email applies to all recipients. Maybe you want to target customers with an inactivity score of 90 (meaning they are close to reaching their personal re-purchase average) and send an e-mail with recommended next items to purchase. If some customers actually purchase on the day of the e-mail send-out, and the CLV model is not updated accordingly, you could risk mistiming your communication. In this case, you should schedule the CLV model to run a couple of hours prior to your e-mail send-out time.
When done with the setup, you can click Save. This will perform an initial training-run of the model and validate its performance. You will automatically be directed to the overview page, where your newly established model should be visible with the status 'Running'.
The overview page
On the overview page, you get an overview of all your CLV models. Each of them is represented with a card containing a status, name, description, run schedule, time of last finished run and an indication of model precision.
A model can have the following statuses:
- Running: The model is calculating and is bringing new data into its predictions. It should only take a few minutes.
- Finished: The model is active and ready to use
Time of the last finished run will indicate when the model last finished calculating new CLV values taking new data into account. The time should be a few minutes after the run schedule.
Model evaluation and performance
Model precision is an important indication of your model's ability to provide you with precise predictions. Precision can be either high or medium. Behind this simple indication is a complex set of calculations that continuously validate the model's accuracy towards the actual data. As a general rule, the more data that is included in the dataset and thus available for the model to be trained on, the more precise results it will deliver. An example is the ability to predict buy frequency; the more times a customer has placed an order, the more precise the predicted average purchase frequency is.
A model with medium precision is not an obstacle for taking it into use. The cause of medium precision lies at the data set not being sufficiently large to ensure the desired accuracy in predictions. This will improve over time as new data is added to your CDP continuously, which will only make your model more precise every day. For the CLV model to evaluate the first place, it requires a minimum of 120 days of data on the selected schema. The status of the evaluation will in that case be "Invalid".
By hovering the i-icon on the card you can see the result of the last three model trainings. The model is trained once a week during the weekend to evaluate its ability to predict actual customer behavior. By looking at the evaluations you will be able to follow when model precision shifts from medium to high or the other way around.
Manually train and predict
With the buttons "Train & Predict" and "Predict" available on the model cards, you can manually run a CLV model.
Train & Predict: Use this if you have just ingested a larger amount of data and you want as precise CLV scores as possible based on both the new data and the existing data. This will cause the model to train and adjust its settings according to the patterns found in the newly ingested data.
🔍 Note: This can take a couple of hours.
Predict: Use this if you want the model to include data ingested since the last time the model ran. It will use the settings from the last training, which will drastically reduce the run time.
🔍 Note: This can take a couple of minutes
FAQ
Q: Is there anything I need to be aware of if I delete a CLV model?
A: When you delete a CLV model, you should also delete or change audiences that are based on values from the model. Otherwise these audiences will be inaccurate.
Please contact Raptor if you have questions about the CLV model setup.