Nance Olson is a senior market data analyst with St. Paul-based Minnesota Mutual. She is responsible for the tracking of data and analysis of databases to look for marketing opportunities. She received a bachelor of arts degree from Augsburg College in Minnesota and was previously employed at Rockwood Research Corporation.

Three decades ago, Minnesota Mutual's group product databases existed primarily for administrative purposes. Financial institutions across the country supplied their customer lists for use in direct-mail insurance solicitations. Realizing that these names and addresses could be used beyond an administrative function, the scope of the database was expanded. Today, the database has manifested into a comprehensive marketing vehicle.

The group division currently provides $31.4 billion worth of insurance to more than 1.2 million households nationwide, making it the country's number one provider of mortgage life insurance. Still, the company faces fierce competition and cannot be comfortable with the status quo. In addition, today's discerning customers require increasingly sophisticated marketing methods.

Therefore, having names and addresses isn't enough. It's not who you know, but how much you know about your customer base that counts. Minnesota Mutual has long maintained databases of insureds and applicants, clients and prospective clients, but a database without a marketing direction is not being utilized to its full potential. Minnesota Mutual decided to develop a system to reach that potential.

The company needed to add a marketing bent to the group division's most critical database—potential customers. Nothing less than a radical revolution would allow efficient use of the data. The first step was to find out exactly what the database contained that would help increase direct marketing revenue.

The database provided demographic information, but it did not give a profile of the different buyer types. The "real" customers—their values, attitudes and perceptions—were elusive.

The company looked to external sources to help round out the customer profile. Two segmentation systems which classify American consumers into homogeneous groups, PRIZM and VALS, were used to complete this analysis.

PRIZM, a geodemographic system designed by Virginia-based Claritas Corp., is built on the sociological principle that people with similar cultural backgrounds, circumstances and perspectives "cluster" in localities suited to their chosen lifestyles. Using Census data, a two-digit neighborhood lifestyle code is assigned to the smallest geographic areas defined by the Census. A code is subsequently assigned to every level of microgeography, including block groups, Census tracts, postal carrier routes and ZIP codes. A unique nickname is assigned to each lifestyle cluster to capture the essence of the group and facilitate recall.

VALS—Values Attitudes and Lifestyles—was created by Arnold Mitchell of California-based SRI International. Based quite heavily upon Abraham Maslow's Hierarchy of Needs Growth, the VALS typology describes consumers by linking their inner values with their outward lifestyle expression. The prime developmental thrust of the typology goes from classifying people as Need-Drive, Inner-Directed, or Outer-Directed. Each of these classifications contains a number of VALS types—for example, Survivors, Achievers, and Experientials.

It would have been nearly impossible to VALS-type all of the 1.2 million insured households, so data was purchased from National Family Opinion (NFO). NFO provided data on 20,000 members of their national panel who had been both PRIZM- and VALS-typed. This data was cross-tabulated to find out the national norm for VALS type within PRIZM cluster. Since this data was projectable to the group division's database, a PRIZM analysis of the database (which required only addresses) predicted the VALS population for the purchasers of the product.

Combining the PRIZM, VALS and NFO data with the purchasing information already available allowed Minnesota Mutual to create a marketing segmentation system called MAPS—Mortgage Audience Profiling System. MAPS grouped insured and potential insureds into five distinct segments:

The Finer Things

Consisting primarily of families, aged 35 to 44 with upper middle socioeconomic status, they own nice homes, have a lot of discretionary income, and are usually college-educated and career-minded. They tend to be fairly financially sophisticated and skeptical of purchasing insurance through the mail.

Advantaged Advocates

Made up of mostly single professionals with household incomes upwards of $40,000, they believe personal growth comes before getting married and raising a family. They like the convenience of purchasing through the mail, but scrutinize costs and benefits—especially of financial services—carefully.

White Picket Fences

This group enjoys family and home more than just about everything. Predominantly aged 55 to 64 with blue-collar occupations, this segment likes to eliminate the risk of financial loss, and is a good direct response prospect for reputable, established companies.

Rural Route 1

The pace here is slow and easygoing. This is a conservative group, keeping traditional values. They tend to be aged 55 and older, living in small towns and rural areas. Since "the big city" may be miles away, direct mail provides a way to obtain products easily. Financially comfortable, but not wealthy, this group is a good direct-mail insurance prospect, as they tend to make their own purchasing decisions, and may not seek out advice from financial advisors.

Bare Essentials

Taking one day at a time, this group lives from paycheck to paycheck on about $15,000. Most are struggling young parents or couples aged 18 to 24. One parent may work the day shift, the other the night. Like the Rural Route 1 group, financial planning assistance is not sought out. So if a product is something they feel they need, they'll purchase through direct mail. (SRI International recently introduced VALS 2 and Minnesota Mutual will reevaluate the customer segments with this new information, following the same procedure as before.)

The development of MAPS gave the group division a clearer picture of the actual person buying the product, rather than marketing to an anonymous mass. The MAPS information is used in a variety of ways:

  • Product segmentation—deciding which product to offer to which households and the best way to market that product. For example, if the Finer Things segment appears unlikely to buy one product, there may be another product that could have more appeal for them. The right product mix is the ultimate goal.
  • Non-buyer segmentation—some households just aren't worth the sales effort. These households can be deleted from the start, which means eliminating administration costs associated with preparing a mailing, as well as postage costs.
  • Market analysis—keeping up with how our markets and our buyers are changing.
  • New product development—the database work has provided greater insight into the gaps that exist in the product offerings and where new product efforts should be concentrated.

Efficient database management has revolutionized the marketing of group products, but has it increased revenue? A variety of techniques is used to find out.

Both point-of-sale and direct-response activity are continually tracked. Extensive tests also pit products against one another. In general, new strategies are tested against what would have been done without the added MAPS information and are judged on the basis of premium generation. What is being tested and judged is not just the data or the system, it's all of the parts that went into the execution of the particular program—that includes the type of consumer, the product, the timing issues (seasonality)—anything that could have an effect on whether or not a consumer will buy.

The customer profiles developed through our analysis also add value to the service package delivered to the financial institution clients. Many clients have asked that their portfolios be analyzed and marketing recommendations made.

Minnesota Mutual's database marketing expertise in group-related products is the base formula for realizing even greater corporate-wide potential. The analytical methods used for the group data could be applied to other product lines: a larger agency force sells individual insurance, and the company has pension and asset management divisions and a fire and casualty affiliate. The need now is for a corporate-wide Customer Information File (CIF).

Currently, data for all the various product lines is stored in separate databases. First, a CIF would merge data from all the databases and allow standardization of data collection, storage and analysis so that effectiveness of marketing programs can be measured globally. Second, with all product lines' data merged into one database, cross-sell opportunities can be explored, allowing Minnesota Mutual increased market share. And finally, increased information can be made available at point-of-sale. By having various database and segmentation information available via computer, the sales attempt can be that much more effective.

The "revolution" of Minnesota Mutual's databases has now turned into a process of "evolution." The databases must adapt to accommodate an ever-changing marketplace—they cannot remain static. The company must meet the marketing challenge that the ’90s present and continue to provide customers with products that will truly meet their needs. Proper database management will help meet that challenge.