XYZ has good data on customer purchase history. They used this data to code all customers by RFM. Then, they used this coding in a very interesting way. The goals of the analysis were to:

• Identify patterns of migration from RFM cell to cell over time.

• Determine the extent to which customer migration patterns fell into distinct clusters

• Identify investment and marketing strategies appropriate to each migration cluster.

• Assess the effectiveness of RFM migration vs. other segmentation and targeting strategies available for marketing promotions.

Migration means, of course, that some customers improve their performance over time. They move to a higher ranking RFM cell. Other customers regress to lower ranking RFM cells. Profitable marketing comes from anticipating the migration of groups of customers so that the marketing and service investment is spent on higher value customers who will, in return, improve their spending and retention habits. Marketing spend is thus not wasted on lower value customers who are less likely to migrate up.

The customers for this exercise at XYZ were selected based on whether they had purchased any one of a particular "family" of services in a two year period. Purchase transactions were summarised at a half year level for each of the four services. The analysis file included the summary transaction data along with other demographic or behavioural data such as SIC code, company size, age of account, and discount status.

The first step was to determine which products to use in the migration analysis. Migration takes time. The goal was to select customers who used the service over both of the two years. The following chart shows the result of the preliminary analysis:

Activity Timeframe

For the two years analysed, Products C and D had only a small percentage of users in both years. For this reason, these products were eliminated from the migration analysis. The analysis, therefore, was based on three groupings: Product A, Product B and Total Usage. The purchase patterns of the customers were studied over the four half year periods.

The RFM score for each half year period was defined as follows:

• Recency = 1 if the most recent purchase was in the first quarter of the half year period and 2 if the most recent purchase was in the second quarter.

• Frequency = Total shipments during each half year period.

• Monetary = Total monetary value during each period.

The RFM score was then determined by multiplying each of the above scores for each individual customer. This definition tends to dilute the impact of Recency on the RFM score, since 1 or 2 are much smaller numbers than the typical number of shipments or monetary value. Why was this done? Because XYZ was primarily interested in the monetary value and frequency of the customers for studying migration, rather than Recency of purchase.

Customers were then categorised into deciles based on their Period 1 RFM score. Deciles rather than quintiles were used to identify the relatively fine movements that were expected to appear during the balance of the analysis. Period 1 deciles (10 = best decile, 1 = worst decile) were used as the benchmark for monitoring the subsequent migration.

The third step was to complete the actual migration analysis. Migration patterns were identified using the statistical technique called Cluster Analysis. Seven clusters with common behaviour patterns were identified. They were:

Stable customers – top 10%	Top10
Lapsed 6 month customers – Medium Value	LapMV
Seasonal Shippers – Low Value	SeasLV
Growing Shippers – High Value	GrowHV
Stable customers – Medium Value	StayMV
Lapsed 6 month customers – Low Value	LapLV
Reactivated Low Value Customers	ReacLV

The object of the analysis was to determine how each of these groups migrated from RFM decile to decile during the two years. Once this was known, the goal was to use this knowledge to develop an appropriate marketing program for each group.

The cluster analysis clearly identified particular customer types. The Stable, Top 10% cluster were the best and most valuable customers. They had nearly three times the lifetime value of the average customer in the study, and were remarkably consistent in their behaviour over time. They did not migrate up or down, but remained in the top 10%.

What should the marketing strategy be for these good customers? XYZ should work to retain them. They should invest sufficiently in services which will protect them against the possibility of defection.

The Growing, High Value cluster increased their monetary value by 1,500% over the two year period. Although this cluster was only one third as valuable as the Stable, Top 10% cluster, their migration pattern shows that they are clearly worth serious marketing attention. Few clusters could promise as good a return on marketing investment.

On the other hand, the Lapsed, Medium Value cluster experienced a 90% loss in average monetary value from Period 1 to Period 4. What went wrong with these customers? Did they go to competitors, or was their business declining for other reasons. Surveys and market research were appropriate for these customers. There is often more to be learned from failure than there is from success.

A very valuable part of the analysis resulted from identifying the Seasonal, Low Value cluster. These people only ship at certain parts of the year. Spending a lot of money to get them to ship at other periods would be a waste of marketing pounds. The marketing program should be timed to adjust to their schedules. That way, the marketing spend would be far better used.

As a result of identifying these clusters, therefore, XYZ was able to channel its marketing pounds where they would do the most good. Beside brainstorming possible marketing and investment strategies for each migration cluster, a comprehensive profiling exercise was conducted of each cluster. A number of clear characteristics were evident, including key differences in the frequency of sales contact rates, discount status, SIC classifications, etc. As a follow on to the findings of the migration study, a series of predictive models were carried out to identify customers in low value clusters who "looked" as if they should be in higher value clusters.

For example, modelling the customer in the "Growing, High Value" cluster against a look alike model built from the "Stable, Top 10%" cluster allowed XYZ to separate growing customers with additional upside potential from those who had reached the limit of their growth. The key differences between the two clusters, such as sales contact rates, automation status, discounts levels, etc. could then be worked into promotional programs designed to continue to grow these customers with untapped upside potential.

This kind of RFM Migration Analysis can easily be duplicated by any business engaged in database marketing. The benefits are:

• You can identify changes in RFM behaviour patterns that would be invisible with the relatively static traditional application of RFM as a response improving technique

• RFM migration can be a valuable segmentation tool alongside your traditional segmentation approaches

• To the extent that you are able to identify or model key differences between the RFM clusters, the output can provide a clear course of action for the marketer.

• Investment strategies and marketing pro-forma are much easier to produce with the wealth of customer behaviour and value information generated by this kind of study.

If you are considering RFM migration analysis, what are the points you should keep in mind?

• Have a set of business objectives and be prepared to modify your methodology as new information becomes available

• Before your original definition of the RFM score is precise enough to identify fine changes in behaviour. For this analysis, deciles and better than quintiles. Don’t however, make your definitions so fine as to prevent action later. The typical 125 cell approach used in RFM response analysis scoring is too fine to yield sufficiently distinct clusters. Recency, which is usually the most powerful factor in normal RFM analysis is less important in Migration Analysis than frequency or monetary behaviour.

• Marketing analysts and marketers must work closely together during migration analysis. Why? Because the analysis must be focused on how to use the output later in a way that is meaningful to the customer. Database marketing only works if the customer benefits from it. It is useless to identify a cluster unless you then use that knowledge to adjust your marketing investments up or down and in creative ways that will be meaningful to your valuable customers.

Using RFM Migration analysis, you will be able to identify opportunities to create marketing messages that are relevant to your customers. You will be rewarded with increased business and improved customer satisfaction.