Direct marketing campaigns are truly effective when you precisely target customers likely to buy from you. This is done by Profiling and Modeling prospects and clients.
Dumb mass mailings are replaced with “surgical” campaigns that market to specific customers with accuracy using technology that is now available. Today, it’s possible to collect an enormous amount of information about customers, but to use it effectively you 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 zero-in on existing customers for your mailing. The goal is to predict behavior based on what you know about your customers.
These two methods are not mutually 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 useful snapshot 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.
Obviously, profiling and modeling add to the cost of your mailing project. You may wonder why you shouldn’t just stick to the old method of “recency-frequency-monetary” (RFM) analysis. The reason is that for RFM to work effectively you need data on the client’s purchasing habits, and that’s the rub! It only works for your existing customer and is of no use in finding potential clients.
What makes profiling/modeling cost effective is found in three current trends.
- Rising mailing costs.
- Computers able to compute mountains of data rapidly.
- Higher quality customer data 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 and not unthinkable anymore.
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 customer to product. Knowing what kinds of product a particular customer is buying gives you the ability to build an “affinity matrix” showing what related products would stimulate more sales from him/her.
- Demographic and psychographic data is also used for profiling. Demographics tells you a client is a 29-year-old, unmarried, male who earns $45,000 and drives a 2-year old Lexus. 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, say, the latest cellular phone.
- 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. Remember hearing the terms “Urban Up-and-Comers, Settled In, and White Picket Fence” used to describe market segments? These are known as “clusters”, each given a score according to affluence, social position, activities, and aspirations.
- Survey data – can be used to enhance demographic, lifestyle, and other data to build a profile. This is collected directly from your customers via application forms, surveys, and credit histories. This provides a more personal portrait of the customer than merely census or demographic data.
The Direct Marketer of today has become more of a “surgeon” than a “shotgun hunter”. It’s no longer cost-effective to shoot at 400,000 prospects to get 40,000 clients, and with computers it’s easier to slice-and-dice data today.