Direct marketing campaigns are truly effective when you precisely target customers to buy from you. This is done by Profiling and Modeling prospects and clients. The key point is the artificial intelligence technology (AI).

Mass mailings campaigns are replaced with targeted campaigns that market to specific customers using A.I. technology. Today, it’s possible to collect an enormous amount of information about customers, and use it in “profiling” and “modeling”.

Both of these techniques are ways of applying external data to possible clients. They can be used to prospect for business or to existing customers for your mailing. The goal (success) is to predict behavior based on what you know about your customers.

These two methods are not exclusive, and marketers often use them together. The difference is that profiling data is overlaid against an existing client database, and has a long life span. It can be used for several mailings, and in contrast, modeling is used to sharpen the focus of a specific mailing.

In profiling start with the premise that you don’t want to deal with a customer segment, but rather an individual customer. Break up your client segment into clients who share similar tastes and buying habits. Then use demographic and behavioral information to create a user profile of the customer.

Begin to gather this information from your existing customer database noting such things as frequency of purchases, buying habits, responses to marketing offers, and repeat purchases.

Then start with your perceived prospects using alternate sources of data from purchased sources. Use all this data to break your customers into clusters that share purchasing traits.

What makes profiling/modeling cost-effective.

1. Mailing costs.
2. Artificial Intelligence
3. Higher quality customer data are available.

In the past, direct marketers could mail out 400,000 mailings to find a strong market of 40,000 (1 customer out of 10 mailings was average). The dramatic increase in the cost of paper and postage has made this practice prohibitively expensive.

Computers today are capable of doing millions of computations per second. This makes analyzing mountains of data possible with great results!

Higher quality customer data is more available today, and there are more sources available for obtaining it than ever before.
The result is that you can afford to do a lot of number-crunching before you spend a penny on postage. You can also weed out the useless names and mail only to your most likely prospects.

There are 6 factors to consider when building customer profiles:

• Affinity profiling

Analyzes current buying habits to better match customers to the product. Knowing what kinds of products a particular customer is buying gives you the ability to know what related products your customer will buy pretty easy. (You will become a sales prophet!)

• Demographic and psychographic data

Demographics tell you a client is a 29-year-old, unmarried, male who earns $45,000 and drives a 2-year old sports car. Psychographic data suggests that single young men who buy status-symbol cars are excellent prospects for other highly visible status products.
Combining the two types of data yields a customer profile to someone marketing, the latest iPhone or expensive accessories.

• Lifestyle Coding is used to enhance basic demographic information.

Simply put – people in certain demographic categories will likely have similar hobbies and other interests.

• Mapping is another useful tool in building customer profiles.

Census data, topographic information, geographic coordinates, and zip code+4 postal data can be fed into a computer yielding maps that can be color-coded to certain characteristics of consumers in particular neighborhoods.

• Cluster Coding is a popular means of grouping people by lifestyle characteristics.

It’s like the old names of the neighborhoods. These are known as “clusters”, each given a score according to social position, activities, and aspirations.

• Survey data – can be used to enhance demographics, lifestyle, and other data to build a profile.

This is collected directly from your customers via application forms, surveys, etc. This provides a more personal portrait of the customer than the mere census or demographic data.
P.S.

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