Deep Data Analytics for New Product Development by Paczkowski Walter R.;

Author:Paczkowski, Walter R.;
Language: eng
Format: epub
Publisher: Taylor & Francis Group
Published: 2020-04-15T00:00:00+00:00

4.4 TURF analysis

In the previous sections, I focused on newer methods for testing new products in the marketplace. This was predicated on there being just one version of a product. But suppose there are several. An older methodology for determining which of several versions of a product will sell is based on finding the best combination of those products with the notion that the combination will sell the best. It may be that each product alone does not garner enough market share to meet business objectives, but in combination with one or two others they could produce total sales to meet the objectives. A good example is ice cream. Offering one flavor (e.g., chocolate) may not attract enough customers to be profitable. Several flavors, however, could attract customers because of the wider selection even though they would still buy only one; they would buy at least one of the offered flavors. Offering a huge selection of flavors may not be practical because of the cost of producing, maintaining, and marketing them all. An optimal subset, a small combination, may be more profitable. Also, too many options may stifle purchases because customers could be overwhelmed and therefore just not make a purchase. There is a paradox of choice that involves creating purchase anxiety which customers may not be able to resolve and overcome, so they just do not purchase. See Schwartz [2004] for the classic discussion of this interesting paradox.

A market research methodology named TURF, an acronym for “Total Unduplicated Reach and Frequency,” was developed to handle situations such as this. It has its origins in advertisement research and is quite old and simplistic, but still used, especially beyond its original intent. I will briefly discuss TURF in this section for product research. MaxDiff is a modern approach to testing claims and messages, as well as different versions of a product. I will discuss this approach in Chapter 5 and then show how it can be combined with TURF.

TURF was developed to determine the best or optimal set of magazines (or newspapers or TV shows) to use to promote a product. If there are five magazines, it may not be cost effective to promote in all five, not just because of the costs of placing an ad, but because customers may only buy, say, two of the five so placing an ad in two will have the best exposure; it is not necessary to have all five. The percent exposure in the set of magazines is the set’s reach. The set is the combination of magazines. If there are n = 5 magazines, then the number of combinations of size two is

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