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Redefining Your Best Customers Using Database Techniques

The Recency, Frequency, and Monetary value (RFM) model has long driven the mailing efforts of businesses. Customers who fit the profile of high-frequency buyers or recent buyers received the most communiqués.

The theory: Promotional and mailing dollars were most effectively spent on these “best” customers. You may find that you can get your best customers to buy even more from you, and get less-active buyers to buy more often, by using more customized contact strategies such as targeted offers, specialized versions of communiqués, and specially timed mailings.

Recent improvements in database technology allow you to capture more transactional and promotional history, enabling you to move beyond pure RFM.

By focusing on individual customer characteristics that clearly identify and predict purchase behavior, you can customer email database implement the best contact strategy for individual customers or unique customer groups. The customers’ needs – not those of your own in-house schedules – become the focal point of the contact strategy.

Knowing the value of your customers, along with what, how, and when they like to buy, provides you with powerful and actionable information. When carefully used, this data gives you the foundation to develop a targeted mailing that is closely aligned with actual customer behavior. You can then determine how to create different versions of the communiqué and when is the best time to mail to customers within each segment.

Using such programs, you can mail fewer promotions at less expense and still yield the same revenue because you’re targeting your best customers. In addition, through careful analysis of the purchase patterns and behaviors of “best” customers, you can find those best customer “look-alikes” who, with the right promotional mix, can mimic the buying habits of your best customers.

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To develop a customer-centric contact strategy beyond traditional RFM, you must combine profiling and segmentation with an in-depth analysis of customer profitability and purchase behavior. The following step-by-step approach can help you form the foundation of a customized contact strategy.

Step 1: Understand unique groups. While RFM variables may be part of the overall equation, demographic, behavioral, or lifestyle information, such as leisure interests, can identify customer needs and attitudes.

Overlying this information on your house file lets you develop a clear picture of who your customers are and what they want so that you can group them into segments beyond recency and average order size. For instance, you can also segment customers by method of payment, separating those who place orders by phone from those who order via email.

Step 2: Identify purchase behavior. Next, take a detailed look at purchase behavior on a segment-by-segment basis. It’s important to identify four key variables:

Affinity – the products or product categories that customers usually buy
Product sequencing – the order in which customers typically buy
Seasonality – the time of year in which customers buy
Timing – the amount of time that lapses between orders that aren’t seasonal in nature

More powerful than just recency and frequency, identifying purchase behavior by product affinity, sequencing, seasonality, and timing helps you truly understand and predict what, how, and when customers will buy from you.

Step 3: Use “true” monetary values. Instead of looking just at a customer’s total sales or average order size, you should also view a customer’s profitability based on a combination of factors:

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o net sales less the sum of cost to acquire
o cost of mailings
o cost of special offers (such as free shipping)
o cost of returns
o bad debt

In a natural extension of this analysis, you should look at customer profitability results over time, rather than on a per-order basis, so that you can rate each buyer segment with an overall profitability score. This combination of profiling, segmentation, and complete profitability can give you a new measure of your
“best” customers. Rather than rating your customers by how much they spend or how often they buy, you should look for other patterns, such as what they buy and when they shop for it.

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