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	Remember the ‘profile’ examples we discussed earlier? These are based upon averages across your customer base. The vast majority of organizations address their customer base by taking arbitrary slices across this base — perhaps subdivided by some external convenience such as sales region, geography, store, etc. But any targeting or selection process that occurs is based upon that underlying average of the customer universe. Now let’s bring the 80/20 Rule into the mix. Knowing what we now know we come to the frightening realization that these 'averages' we’ve been using are mostly (80%) derived from the less significant (and less valuable) part of our customer base. Talk about off-target! Understanding and implementing this cluster-based management of the customer universe is important when working with established customers but critical when attempting to prospect for more in the population at-large. We need to know how to differentiate customers from non-customers and ideally better customers from the rest. We could contact all our customers/prospects over and over again — Sort of like singing all the notes in the scale to make sure we hit the right one. This approach would, however, incur a great deal of unnecessary expense from all those extra contacts (thereby significantly lowering our profitability). Also, we would likely alienate our customers by contacting them too frequently. As a solution, some organizations employ variations of R-F-M (Recency-Frequency-Monetary value) targeting. The thinking here is simply to look to the customers spending the most, most recently and most often to be your best sales prospects. This approach may fall under the ‘it’s better to do something than nothing’ heading. Once again, customers are being incorrectly lumped into a single group and the ‘top’ skimmed off. <back> <next>

Remember the ‘profile’ examples we discussed earlier? These are based upon averages across your customer base. The vast majority of organizations address their customer base by taking arbitrary slices across this base — perhaps subdivided by some external convenience such as sales region, geography, store, etc. But any targeting or selection process that occurs is based upon that underlying average of the customer universe.

Now let’s bring the 80/20 Rule into the mix. Knowing what we now know we come to the frightening realization that these 'averages' we’ve been using are mostly (80%) derived from the less significant (and less valuable) part of our customer base. Talk about off-target!

Understanding and implementing this cluster-based management of the customer universe is important when working with established customers but critical when attempting to prospect for more in the population at-large. We need to know how to differentiate customers from non-customers and ideally better customers from the rest.

We could contact all our customers/prospects over and over again — Sort of like singing all the notes in the scale to make sure we hit the right one. This approach would, however, incur a great deal of unnecessary expense from all those extra contacts (thereby significantly lowering our profitability). Also, we would likely alienate our customers by contacting them too frequently.

As a solution, some organizations employ variations of R-F-M (Recency-Frequency-Monetary value) targeting. The thinking here is simply to look to the customers spending the most, most recently and most often to be your best sales prospects. This approach may fall under the ‘it’s better to do something than nothing’ heading. Once again, customers are being incorrectly lumped into a single group and the ‘top’ skimmed off.

<back> <next>