For Richer, for Poorer

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If you've been following my advice about segmenting your databases to find nuggets of valuable customers and prospects, here's another powerful recommendation: brand names and who buys them.

Simmons' spring 2004 survey presents some pretty telling findings:

Additional statistics from Nielsen//NetRatings reveal Internet use by those with household incomes over $150,000 grew by about 20 percent in the last year. Compared to other groups, these high earners spend more time online (76 hours) and view more pages (2,126 pages) per month.

What can we learn from this?

If you sell multiple brands in any category, you can test two things to identify which people are most likely to buy certain brands:

• Overlay your database with household income data so records contain a code that roughly equates to the quintiles above. If your overlay shows 1,209 people in your database make over $100,000 a year, those might be your first quintile. Market higher-end or luxury brands to these customers.
  
  
• Track, if you don't already, which brands customers buy. If someone has purchased Armani, TAG Heuer, Prada, Bose, or other top brands, code them as your first quintile as well.

And that's just the beginning.

Say you sell 50 different brands of apparel and know which customers purchased which brands. You can develop a series of year-round email messages for each brand based on specific trigger events:

• Announce a new line of merchandise has arrived.
  
  
• Run a preferred customer brand-name sale.
  
  
• Liquidate overstock by emailing customers who've purchased that brand and may want to again.

If you sell books or music, you can do the same thing. In this case, the author or artist is the brand. When John Grisham comes to a local store for a book signing, you can send an event notification to people who purchased books by him and people who purchased books by other authors in the same genre.

Brand is just one example of data that may be available in your database. This strategic thought process can be applied to any number of different data variables. The key is to build a model with strong predictability to generate a positive response. Amazon.com has made billions doing exactly what's described above. You should do it, too, and work toward your first billion!

Want more email marketing information? ClickZ E-Mail Reference is an archive of all our email columns, organized by topic.