Using simulators to find E-LTV Nov 1, 2007 12:00 PM
, JIM WHEATON
JobZone
Search and post jobs for the Multichannel Merchant. Including jobs for brand & agency marketers, e-commerce, catalog marketers, ops & fulfillment, direct marketing and more.
You know that best-practices marketing database content is essential to effective data mining. It works in tandem with longitudinal (over time) test panels and business simulators to identify and leverage a given company's dynamics to provide important strategic and tactical insights.
More specific, best-practices marketing database content is an atomic-level compendium of historical customer events such as product purchases, associated post-demand transactions (returns, cancels, exchanges, etc.), and multichannel promotion history.
A business simulator estimates future customer events by extrapolating, under different scenarios of customer relationship management efforts and expenditures, the past trends inherent in these historical events.
The business plans derived from the simulations serve as a benchmark to judge future CRM efforts and expenditures.
Longitudinal test panels and best-practices marketing database content function as a window into previous reality, and a business simulator as a view to the likely future state of reality.
A business simulator's projections allow you to calculate total estimated ongoing profitability with a metric known as enterprise-wide long-term value. E-LTV is the sum of the estimated long-term values for all customers; that is, the discounted sum of all future profit flows. It represents the net present value of the firm, given the input provided to the simulator.
An interactive tool
Working with a business simulator, you can generate significant increases in E-LTV by:
Moving away from intuitive, rules-based, “historical-tallying” customer segmentation approaches, such as recency/frequency/monetary, to “future-estimating” techniques such as statistics-based predictive models
Developing longitudinal contact scenarios
Running each scenario through the simulator and selecting those that are the most promising for live longitudinal testing — those with the highest expected E-LTV
Establishing the most successful contact scenario as the new marketing standard
Executing this cycle repeatedly to achieve continuous improvement, with the ultimate goal of CRM optimization that 1) maximizes current cash flow, and 2) systematically allocates cash flow across investments in customer acquisition and cultivation.
As you develop and test different contact scenarios, you may discover that increased marketing intensity among certain customer segments represents a major growth opportunity; for example:
Increased page counts and additional drops
The judicious use of more expensive postage and shipping options
Special 800-numbers for ordering and customer service
Loyalty and points-based programs
In business-to-business, the field and phone sales force can make additional efforts, including concentrated attempts to track ever-changing individuals at client companies
“Priming” contacts with e-mails, postcards or phone calls in advance of — or sending reminders subsequent to — major direct mail campaigns or field sales force visits
Simulators allow the investigation of E-LTV in an environment that transcends the limitations of past marketing decisions. But there are four important ground rules for such work.
First, do not confuse cause with effect. For example, the state of being a multibuyer is the effect of a customer's loyalty rather than the cause of the loyalty. So using “give-away” tactics to encourage conversion from single to multibuyer status is not likely to increase loyalty.
Second, realize that observed customer behaviors such as attrition are a mix of what you can truly influence vs. mere self-selection. Customers vary in terms of their intrinsic quality. Even best-practices marketing database content presents only a partial view into this intrinsic quality.
For example, consider a woman who has to replace her entire wardrobe because of a fire: Under normal circumstances, she has never been a heavy apparel buyer; her intrinsic quality as a long-term clothing customer is limited. Since she is not likely to sustain her current rate of clothing purchases, attempts to counteract this transition will be unsuccessful.
A marketing database will reflect a flurry of post-fire purchase activity for this customer. Unfortunately, it will reveal nothing about the motivation behind it, so the mirage of a long-term heavy clothing buyer will have been created.
Third, recognize that behavior subsequent to a marketing effort is not necessarily the result of that effort. You must isolate and quantify the portion of subsequent behavior that actually resulted from that marketing effort.
