Supercharge Your Database Marketing with Psycho-Demographic Segments

Traditional database marketing techniques have been in use for years by companies seeking to target their best customers.

For the uninitiated, database marketing involves analyzing your historical customers in terms of their transaction history with a company. Database marketers will often speak of “RFM”: which together stand for “recency, frequency and monetary value.” By segmenting existing customers by recency, frequency and monetary value, the marketer can:

* isolate those customers who spend the most money with the company, both per transaction and over time
* identify past “best customers” who are on the decline in terms of the frequency or recency of their visits
* separate out which customers should receive certain promotions, coupons or other discounts, and which should not
* and much more

Database marketing works great. But, it’s limited in its scope.

With all of its effectiveness in helping companies market more intelligently, database marketing is lacking in one very important area: it is not able to identify prospects who “look like” your existing customers out in the marketplace. Why? Because all of the information on hand pertain to existing customers; there is no way to generalize this information to would-be customers (i.e., prospects). In short: traditional database marketing works well in marketing to existing customers but doesn’t help you find new ones.

There is a way to overcome this shortcoming of database marketing: by assigning segment types to each individual customer based upon psycho-demographic attributes and behaviors – segment types which can be generalized to the marketplace as a whole. Here’s how it works:

1. Start by assigning a segmentation cluster type to each of your existing customers.
2. Determine which characteristics of your existing customers represent a “best customer” to you, such as high value per transaction, high frequency visits, etc.
3. Calculate which subset of all possible segmentation cluster types represent, as a group, your “best customers.”
4. Go out into the marketplace and find prospects who match the same segmentation cluster type(s) as those assigned to your best customers.

The subtle, but game-changing, benefit of overlaying the psycho-demographic segments onto your RFM data is that you now have a way to find prospects (i.e., those who live near your business but who do not buy from you yet) who match those precise segmentation types. Translation: now, you can find more people “just like” your best customers and market to them. This blows wide open your previously-limited opportunity to market better to your own existing best customers and instead use smart marketing to identify those most likely to buy from you out in the marketplace.