Customer Lifetime Value Prediction
Solution Background and Business Value
Predicting customer lifetime value (LTV) is essential for businesses to optimize marketing strategies, enhance customer retention, and maximize revenue. By accurately forecasting how much a customer is likely to spend in the future, companies can:
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Allocate marketing resources efficiently.
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Identify and retain high-value customers.
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Personalize promotions based on expected spending behavior.
LTV models can be combined with churn prediction and coupon affinity models to boost retention efforts and maximize profitability.
Data Requirements and Kumo Graph Schema
We start with a core set of tables and extend our model by incorporating more customer behavior signals over time.
Core Tables
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Customers
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customer_id
(Primary Key) -
name
,email
,phone
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registration_date
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address
-
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Orders
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order_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
product_id
(Foreign Key to Products) -
order_date
,quantity
,price
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Additional Tables (Optional Enhancements)
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Products
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product_id
(Primary Key) -
product_name
,category
,price
,cost
-
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Order Events
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order_id
(Foreign Key to Orders) -
event_type
(payment, delivery status, etc.) -
event_date
,amount
-
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Customer Interactions
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interaction_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
interaction_date
,interaction_type
,interaction_details
-
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Returns
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return_id
(Primary Key) -
order_id
(Foreign Key to Orders) -
product_id
(Foreign Key to Products) -
return_date
,return_reason
,refund_amount
-
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Customer Loyalty
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loyalty_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
loyalty_points
,membership_level
,points_earned
,points_redeemed
-
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Marketing Campaigns
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campaign_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
campaign_type
,campaign_date
,campaign_response
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Entity Relationship Diagram
Predictive Queries
LTV can be defined in multiple ways, depending on business needs. Common approaches include:
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Predicting total spending per customer within a given time frame.
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Forecasting purchase frequency and average order value.
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Integrating customer engagement signals from interactions and campaigns.
Here are some example predictive queries:
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Predict customer spending in the next 6 months:
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Predict transaction volume for active customers:
Building model in Kumo SDK
1. Initialize the Kumo SDK
2. Select tables
3. Create graph schema
4. Train the Model
Deployment Strategy
Automating LTV Predictions for Business Growth
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Predict LTV and churn probabilities for all active customers.
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Store the predictions in the data warehouse.
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Use the scores to prioritize marketing efforts (e.g., personalized discounts for high-value customers at risk of churning).
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Automate these steps using orchestration tools like Airflow or Dagster.
To further refine the LTV model, consider:
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Combining LTV with churn models for a more holistic view of customer retention.
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Using marketing response data to identify customers most likely to engage with promotions.
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Incorporating external data sources (e.g., economic trends, industry benchmarks) to enhance predictive accuracy.