Mass marketers link up

Direct marketers have long touted the fact they can calculate the cost-effectiveness of every aspect of their business, from list selection, copy and creative, through measurement of the effects of different price levels and seasonality on response rates.However, for mass marketers...

Direct marketers have long touted the fact they can calculate the cost-effectiveness of every aspect of their business, from list selection, copy and creative, through measurement of the effects of different price levels and seasonality on response rates.

However, for mass marketers the use of this kind of precision marketing seemed, well, inappropriate.

‘Unsuitable’

They dismissed direct marketing with the complaint that it was unsuitable for mass-distributed products since it could not be used to move products in sufficient volume.

Moreover, since they distributed their products through retailers, a database marketing program that could not be related in some way or other to the retail structure and their own sales force was useless.

But we all know there is hardly a mass marketer or retailer today who is not experimenting in some form or other with database and/or target marketing.

Most marketers, even mass marketers, believe that if they are going to be successful in the 1990s, they will have to isolate their key market segments and do everything they can to reach those segments with as much selling impact as possible.

Homgeneous group

(In the parlance of database marketers, a segment can be an individual, a household, a lifestyle cluster, a brand buying group, or any homogeneous group of consumers that can be measured and targetted).

The reasons behind this sea change in the perspectives and business practices of mass marketers and retailers have been covered in numerous articles and speeches.

They include: the increasing fragmentation of mass media; an avalanche of new products in every category; an increasing interest in accountable or measurable advertising (a recession can focus the mind wonderfully), and the growing appreciation for the lifetime value of a customer, even for a marketer of a low-cost food product such as peanut butter.

If someone is going to buy 200 jars over the next five years or so at $5 per jar, then it seems sensible to know where that person lives and to establish on-going communication with that customer.

Only looks expensive

At $5 per jar, database marketing looks expensive, but at $1,000 per household over a period of years, the cost benefit calculations change considerably.

However, just as important as any of these explanations for the growing popularity of database or response-based marketing has been the rapid advances in technology.

Marketers and retailers can now cost-effectively acquire, store and manipulate individual level data on millions of consumers, much in the same manner as any traditional direct marketer.

Dominion’s Priority Plus program, which has been extensively covered in the press, is only one of dozens of recent examples of how a mass retailer can acquire individual level data through normal commercial transactions, and then use the name, address and consumption data collected to communicate with individuals on a one-to-one basis.

Advances in pc storage and statistical analysis allow an almost unlimited amount of data to be stored on a marketer’s desktop.

A few years ago in the u.s., several companies (Spectra Marketing and Donnelly Marketing, to name two) recognized that not all marketers and retailers would or could make the commitment to collect or build individual-level consumer databases. They also recognized that, all other considerations aside, most mass marketers would not be interested in any program that did not link up with their retail distribution structure.

Donnelly and Spectra’s solution was to build databases that contained the name, address and the approximate geographic coverage of every grocery and convenience store’s trading area.

Why?

Well, simply knowing the geographical area from which every store pulled most of its customers meant that marketers could correlate the demographic and lifestyle characteristics of a store’s trading area to the known (based on surveys or other consumer research) demographic characteristics of a product’s primary consumers.

As a result, marketers and retailers could use these databases to target stores and chains for selective in-store test marketing (for example, promoting to a selection of stores that had the ‘right’ profile, as well as to stores that had the ‘wrong’ profile, in order to measure the product’s inherent appeal to one consumer group over another, a common ‘split half’ test used by direct marketers.)

They could also use these databases to justify an increase in shelf space for stores in high potential areas, and to target high potential neighborhoods around each store with door-to-door promotional material or even addressed mail for selective sample distribution.

Applications

The list of applications is beyond the space available here.

A similar database, called StoreBase, was recently developed in Canada by Compusearch in conjunction with Canadian Grocer.

Interestingly enough, one of the most popular but unanticipated uses of these databases has been the calculation of redemption rates of non-targetted, non-addressed coupons.

Simple process

For example, if the store or chain code is put on the coupon when it is redeemed (usually a simple procedure for one of the fulfilment houses), then it is a simple process of dividing the number of coupons returned into the number of households contained in each store’s trading area.

Think of it as a ‘response’ bdi (brand development index) for each store and then compare this response rate to the overall response rate, i