Customer Lifetime Value Prediction

DMSRetail uses sophisticated predictive analytics to help retailers build Lifetime Value (LTV). Attrition and Retention scores for their customers, products or segments. Strategic deployment of these results are proven to reduce churn and increase marketing ROI across the most profitable customer segments.

In mathematical terms, Lifetime Value calculates the present value of the profit realized on each customer for a given period of time. It is important to note that "lifetime" does not designate a person's whole life, but rather the amount of time that he or she remains a customer.

In business terms, Lifetime Value modeling attempts to answer the question "What is the value of a given customer over time?" LTV does this by building the holistic view of the customer that marketers can leverage to direct product development, service bundles and the creation of the optimal marketing strategy.

By synthesizing market information, transactional behavior, promotional history, time, money spent and attrition survival probabilities into a single

metric, predictions can be made about the current dollar value of a customer or a segment as well as the expected value (either by client, portfolio, segment or location). This holistic approach is the primary driver of Lifetime Value calculations.

 

Attrition and Retention Scoring

Another important aspect of Lifetime Value modeling is the Attrition and Retention scores that can be built from the results.

These scores assists retailers in identifying the customers who are most likely to leave and join a competitor (attrition), when they are likely to leave and how much it will cost to keep them (retention). In this way retailers can calculate ROI and develop strategies to retain the most valuable customer segments.

In addition, metrics like probability of attrition allows retailers to re-position resources and messaging to target the customer segments with either the greatest possibility of attrition or the best return on a retention strategy.

 

Lifetime Value Modeling

Just because a new customer makes a large purchase does not mean they are a profitable acquisition.

Lifetime Value modeling applies advanced algorithms to historical data to determine a customer's value over time, predict which customers are at risk of attrition, when they are likely to attrite and which customers threaten retailer's profitability. Once a retail business understands the value of a particular customer for a specific period of time, they can proactively direct resources and strategy to support profitable Customer Relationship Management (CRM).

Lifetime Value modeling builds a single metric for customer, based on:

	External Data
	Transactional Behavior
	Market Conditions
	Promotional History
	Time
	Attrition / Survival Probabilities
	Spending