|
TWO THINGS ARE CLEAR FROM OUR WORK WITH BOTH LARGE AND SMALL COMPANIES throughout the US and abroad. First, even though traditional data analysis tools are still useful, innovation has become a constant in a changing world. Second, companies are awash in an increasing flood of data -- and most of it goes largely unused. Companies are overwhelmed and not making good strategic use of their data. New analysis tools are needed. |
More specifically, the traditional RFM (Recency-Frequency-Monetary) analysis approach that every direct marketer knows about is no longer enough. True, it is still a good start because it looks at customer behavior in only three basic dimensions -- Recency, Frequency and Monetary. But in today's multi-dimensional world, it misses some truly important marketing insights. It is too simple to reveal the exciting new opportunities in our multi-dimensional ever-faster-changing world.
THE SOLUTION
MAG has developed two new new multi-dimensional scoring processes that go beyond the limited scope of a traditional RFM analysis. The first new tool is RFA Analysis. Like RFM, it uses Recency and Frequency. However, it uses Average Order instead of Monetary for the third dimension. In many cases, RFA Analysis reveals that are "below the radar" of the RFM technique. (You can learn more about why RFA is often much better than RFM by reviewing Segmentation: Finding Your Best Customers and another of our magazine articles, Show Me The Average.)
The second new multi-dimensional process is called ABT Analysis because it segments customers into three segments: Advocates, Buyers and Tryers. ABT Analysis extends (not replaces) the traditional RFM analysis to provide deeper insights than basic data analysis does. It does so by taking advantage of the additional data that most companies now collect about their customers.
Specifically, ABT Analysis uses four to six or even more dimensions to define each of these customer segments. This means that each segment is defined differently enough from the others to be identifiable even when the segments are small. Further, ABT Analysis defines each segment in ways that are directly actionable. That is, you can use ABT segment descriptions directly to develop a communications strategy, a creative message or a targeted offer.
For example, consider a business-to-business ("B2B") enterprise. RFM can quickly tell us who buys the most, who buys often and who bought recently. But what about who buys from multiple product categories? Or, which client has one versus several contacts that have purchased? What about customers with untapped subsidiaries that have never bought? These questions represent the dimensions that often identify a "best" customer in a B2B market.
AN EXAMPLE
Recently, MAG helped a client struggling with a common marketing question: Where should we deploy our sales force most cost-effectively? An RFA Analysis was done and it quickly debunked the way the company had been viewing their "best customers". It revealed that a higher M (Monetary) score did not equate to a higher A (Average Order).
Further, the RFA Analysis revealed that the frequent low margin purchases cost the company a lot more to fulfill than their less frequent, high margin sales. So, using RFA (instead of RFM) in this situation revealed where to invest expensive sales team efforts. It also showed how to focus upon changing customer behavior with less costly telemarketing and direct marketing efforts with the goal of moving low-margin customers to buy higher-margin products.
Stopping at this point, however, would have let a lot of insight "on the table". There were other customer behaviors that are extremely important but are not part of an RFM analysis. For example, were the companies that purchased cross-category (at least four product lines) more profitable and/or less likely to defect? What about companies that have multiple (at least 2) sales contacts buying versus a single sales contact? How important was "depth of relationship" with the customer? That is, how much of a difference did it make to be talking to the VP of Sales instead of a buyer? Did it make a difference if the customer was using your product in an innovative way versus using it as a commodity? That is, does it make a difference if you are selling a product/service that goes into a manufacturing process where the customer is making something new with it as compared to using your product as a commodity? Each of these questions are part of designing a good marketing strategy.
In this example, MAG's ABT Analysis revealed a real "Ah Ha!" insight. We discovered a "magic success formula": "4-4-2". That is, the ABT Analysis revealed that any customer described by this formula had a defection rate of zero and a very high profitability!
The challenge then was to act upon this discovery in ways that the marketing and sales teams could easily understand and use. Everyone intuitively understood the difference between an Advocate, a Buyer and a Tryer. That part was easy. But MAG went on to perform a firmographic overlay and a simple CHAID analysis. (You can learn more CHAID and other advanced modeling techniques by reviewing Prediction - Finding Gold in the Data.)
Using the 4-4-2 "magic success formula", it was easy to precisely score each customer as an Advocate, a Buyer or a Tryer. What makes an ABT Analysis especially different and useful is that it can use as many or as few dimensions as necessary. Further, the output is exceedingly simple and easy to use (although the scoring process can be extremely complex.) The chart below shows the dramatic differences in Average Order between the Advocates, Buyers and Tryers in this example.
With the Advocates clearly identified, the sales team shifted their efforts to ensure high retention among this group. Previously, many of these customers were not even on the sales team's "radar screen"! Then the company approached the Buyers with a mixture of direct and indirect sales efforts to boost cross-channel sales in this group and move them towards becoming Advocates. Finally, the company contacted the Tryers with the highest potential -- about 30% of them -- with an indirect sales message designed to advance them into being Buyers.
It's still early for a full accounting of the results. Informally though, this dot.com company has survived while many of its peers have not. Since then, MAG has used its ABT Analysis technique at other large B2B firms in a number of different industries with similar success.
CONCLUSION
In the end, the purpose of any analysis is to guide strategies aimed at changing customer behaviors. This means that no matter how complex the analytical process, the results must be easy to understand and easy to act upon. In today's skeptical world where "black boxes" are not trusted and the promises of CRM have been found empty, MAG has invented two techniques that really work, are easy to understand and easy to use. Give RFA and ABT techniques a try and see for yourself! ABT Analysis - The Super Model tells you more.
Questions? Just ask! [Back to How-to Resources] [Top] |