Using simulators to find E-LTV

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.

For example, any business with brick-and-mortar retail outlets or an e-commerce site will always have some degree of “walk-in traffic.” With retail, and sometimes online, this constitutes the majority of activity. Rigorous analysis might uncover that:

  • Only 15% of in-store customer purchases that occur immediately after a direct mail promotion are the result of that promotion.
  • Only 25% of customer purchases subsequent to an e-mail blast are the result of that blast.

All baseline purchase volume — 85% in the first example, and 75% in the second — should be properly attributed to previous efforts to capture demand and build brand awareness. This in turn is driven by historical CRM, and — when applicable — mass marketing.

Finally, understand that you must use longer-term, time-based measurements to quantify true incremental effects. For example, you have to plan, manage, and track ongoing cycles of well-designed longitudinal treatment panels. “One/off” tests often are misleading, because their apparent findings are contaminated by the interaction of prior and subsequent marketing contacts.

Building a business simulator

A business simulator is ready for deployment once the corresponding atomic-level data that captures the dynamics of the firm are ready for input. Keep in mind, however, that an inaccurate business simulator is worse than no simulator at all.

Creating a robust and accurate simulator is a significant task, especially for businesses that deal with substantial numbers of customer, inquirer, and prospect segments, and a large variety of promotions across multiple channels. You must estimate the incremental effectiveness of each contact, and quantify the degree of cannibalization across contacts and channels.

But a robust simulator generates realistic customer, inquirer, and prospect transitions from segment to segment, in a process that assumes an ongoing dynamic of its own. When combined with corresponding revenue and costs, the result is monthly profit flows, the analysis of which can answer important strategic and tactical questions such as:

  • Has E-LTV been increasing, decreasing or staying roughly the same in recent years?
  • What would happen if you could lower your cost of goods sold by one percentage point?
  • Would a short-term reduction in profitability from a more aggressive new customer acquisition strategy pay for itself with incremental long-run profits? For example, what if your acceptable acquisition cost were increased by 5%, 10%, or even 15%?
  • What can you do to turn things around in the upcoming year? And from a longer-term perspective, will it adversely affect E-LTV?
  • How long will a positive event “echo” in your business (increase E-LTV) or a negative development depress future results (decrease E-LTV)?

As a dynamic extension of best-practices marketing database content, a business simulator lets you take full advantage of atomic-level data to arrive at important strategic and tactical insights. With a robust business simulator, you can drive significant increases in revenue and profit, and a corresponding dramatic increase in E-LTV.

Jim Wheaton is cofounder of Daystar Wheaton Group, a Chicago-based consultancy that focuses on data strategies and CRM.

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