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:- Allocate marketing resources efficiently.
- Identify and retain high-value customers.
- Personalize promotions based on expected spending behavior.
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-
Customers
-
customer_id
(Primary Key) -
name
,email
,phone
-
registration_date
-
address
-
-
Orders
-
order_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
product_id
(Foreign Key to Products) -
order_date
,quantity
,price
-
-
Products
-
product_id
(Primary Key) -
product_name
,category
,price
,cost
-
-
Order Events
-
order_id
(Foreign Key to Orders) -
event_type
(payment, delivery status, etc.) -
event_date
,amount
-
-
Customer Interactions
-
interaction_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
interaction_date
,interaction_type
,interaction_details
-
-
Returns
-
return_id
(Primary Key) -
order_id
(Foreign Key to Orders) -
product_id
(Foreign Key to Products) -
return_date
,return_reason
,refund_amount
-
-
Customer Loyalty
-
loyalty_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
loyalty_points
,membership_level
,points_earned
,points_redeemed
-
-
Marketing Campaigns
-
campaign_id
(Primary Key) -
customer_id
(Foreign Key to Customers) -
campaign_type
,campaign_date
,campaign_response
-
Predictive Queries
LTV can be defined in multiple ways, depending on business needs. Common approaches include:- Predicting total spending per customer within a given time frame.
- Forecasting purchase frequency and average order value.
- Integrating customer engagement signals from interactions and campaigns.
-
Predict customer spending in the next 6 months:
-
Predict transaction volume for active customers:
Building model in Kumo SDK
1. Initialize the Kumo SDKDeployment Strategy
Automating LTV Predictions for Business Growth- Predict LTV and churn probabilities for all active customers.
- Store the predictions in the data warehouse.
- Use the scores to prioritize marketing efforts (e.g., personalized discounts for high-value customers at risk of churning).
- Automate these steps using orchestration tools like Airflow or Dagster.
- Combining LTV with churn models for a more holistic view of customer retention.
- Using marketing response data to identify customers most likely to engage with promotions.
- Incorporating external data sources (e.g., economic trends, industry benchmarks) to enhance predictive accuracy.