One might think the principles of RFM and LifeCycle modeling would break down in the durable goods area. For example, is a person who bought a refrigerator Recently more likely to buy another one than a person who bought a refrigerator a long time ago? On the surface, the answer would seem to be no. But to ask the question this way is to set up a “trick question” with a self-fulfilling answer. In fact, this question is not the right question to ask.
Let’s look at this from two perspectives – the perspective of the “whole relationship” with the customer and the perspective of a single product replacement cycle.
RFM approaches customer behavior from the perspective of a business and the relationship of the business with a customer, not from a product-driven view. RFM tells you the person who Recently bought a refrigerator is much more likely to buy any product from the business relative to a customer who bought a refrigerator at some date further in the past.
In other words, from the perspective of the business selling the refrigerator, the customer who Recently bought a refrigerator is much more likely to buy a stove, a washer-dryer combo, and so forth than a customer who bought a refrigerator further in the past. This is RFM from the “whole relationship” perspective. It’s about the relative likelihood of customers engaging in purchase behavior with a business, not the absolute likelihood of any customer or non-customer on the planet to buy another refrigerator based on last purchase date.
If you want to stretch RFM into service from the single product perspective (the repeat purchase of another refrigerator), you have to make adjustments to the method. With long purchase cycle products, you have to move your threshold of measurement out in time to account for the long product cycles. In other words, it is Recency of purchase relative to the expected life of the product (or some other variable) that matters, not Recency of purchase from purchase date. To expect a re-purchase of a brand new appliance immediately after initial purchase ignores human behavior, and RFM is about predicting relative human behavior. You have to “normalize” the method to account for long lifecycle products and services.
For example, if the refrigerator has an expected life of 5 years (perhaps the length of the warranty), a logical data point to study would be Recency of purchase relative to warranty expiration. A customer who is only 6 months past warranty expiration is much more likely to purchase another refrigerator from the business who originally sold them one than a customer who is 2 years past warranty expiration. The more time passing into the replacement cycle, the less likely the customer becomes to replace the unit with the business who sold them the first refrigerator.
Here’s an example from the auto industry. In the 60’s, when 2 – 3 year financing was the norm, dealerships had very high repeat customer rates. When payment terms for cars stretched to 5 years in the 70’s, repeat purchase rates at auto dealerships dropped dramatically. Over these very long product cycles, people became less and less loyal to the individual dealership / brand of car they had purchased. Since buying a car is a significant (and often traumatic) event, dealerships benefit greatly from handling customers carefully and creating a pleasant buying experience. But over time, customers forget the details of this experience and become more likely to seek other sources.
With the introduction of a 2 year vehicle lease, repeat rates soared. Early dealership and product line adopters of the 2 year lease experienced rapidly increasing market share wins at the individual dealership level, and these increases affected entire product lines on a national basis, forcing a capitulation by those dealerships at the local level and product lines at the national level not offering short-cycle financing methods to their customers.
It became just plain easier for customers to repeat with a dealer, because the memories of the past transaction were fresh – they had “gone through the motions” of the decision-making process more Recently. Familiarity breeds inertia, especially when making high-ticket purchase decisions. And all this happened despite the fact car leasing is generally an inferior deal relative to purchase from an economic standpoint.
Are their other influences? Sure, just like any other business. At a dealership, service is big business and the way customers are handled with respect to repairs is critical. This leads to a whole other opportunity for behavioral profiling – the impact of Recency or Frequency of service on the new car buying decision. My guess is they are negatively correlated – the higher a customer scores on Recency and Frequency of repairs, the lower their likelihood of a repeat purchase with the dealership. This effect is seen in the service business all the time, where profiling of non-purchase transactions is frequently more predictive than profiling purchase transactions. But just because the metric is inverted does not mean it is not valid; it simply represents an inverse relationship relative to the desired outcome.
Many companies offering long purchase cycle products actively shorten the cycle by employing an inter-purchase contact strategy. By actively contacting the customer between purchases, these companies try to “bridge” the purchase cycle and maintain Recency of contact. This approach can lead to an increase in repeat purchase rate, if handled correctly.
In fact, this approach is not new and has nothing to do with the Internet. State Farm Insurance has for a long time pursued this contact strategy through the mail. Many companies have the means to conduct an inter-purchase communication campaign though the installment loan system, but fail to send the customer anything but a bill and bunch of bill inserts selling unrelated products. On the other hand, Weber-Stephen Products Co., the manufacturer of Weber Barbecue Grills, sends a quarterly magazine full of seasonal cooking tips and accessories to customers who buy high-end grills.
In the durable goods business, it is much more likely the data needed to profile customer behavior has never been collected or cannot be accessed than it is RFM “doesn’t work” for the business. If developing a customer retention measurement and management system is on your “to do” list this year, you might want to pick up a copy of my book.
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