Week 3 Discussion

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3. cable TV service and extend them? It would help me get ready for their presentation Thursday.” “Several of our divisions work in the women’s clothing markets. As you know, they are all specialized these days, this segment or that segment. It’s getting hard to come up with a new segment, one that has some size and would be responsive. So, when you were talking about morphological matrix, which I liked, I thought about women’s attire. One way to innovate would be to come up with new settings, or occasions, situations where we could devise a whole outfit. Sort of like wedding, or racetrack, or picnic, though we know of them and have clothing for them, of course. Sort of a package of apparel and accessories. But there must be many we don’t think of now. Would that morphological matrix method work on that?”

Case: Rubbermaid17

Newell Rubbermaid is a global company that manufactures and sells a wide range of brands worldwide. Its divisions include: Tools (Lenox, Hilmor), Writing (Sharpie, Waterman, Paper Mate), Baby and Parenting (Graco), Home Solutions (Rubbermaid, Calphalon), Specialty (Mimio, Bulldog Hardware), and Commercial Products sold under the Rubbermaid name. Rubbermaid had been a successful product innovating company for years before its purchase by Newell in 1999, and as many as 200 new products per year are launched under the Rubbermaid name. The success of the Rubbermaid division is based partly on creating and producing high-quality, functional plastic products for anywhere in the house: kitchen, garage, laundry room, and bathroom, as well as closet organizers, car organizers, trash bins, and similar products. In recent years items have ranged from lunch boxes with snack compartments to stacking cereal containers, storage trunks and benches, power scrubbers, and many more. Category brands include TakeAlongs®, Lunch Blox™, Closet Helper™, and others.

The firm makes almost a half-million different items, boasts a 90 percent success rate on new products, and obtains at least 30 percent of its sales each year from products less than five years old.

The firm’s new product strategy is to meet the needs of the consumer. The new product rate is high, and diversification is desired. It is market-driven, not technology-driven, although in recent years the firm has identified such technologies as recycling new plastic parts from old tires for which it is seeking market opportunities. This practice of seeking opportunities for specific technologies will increase as a fallout of the firm’s current use of simultaneous product development.

For idea generation, Rubbermaid depends on finding customer problems that can be built into the strategic planning process. Problems are sought in several ways, the principal one of which is focus groups. It also uses comments and complaints from customers, an example of which came when then-CEO Stanley C. Gault heard a Manhattan doorman complaining as he swept dirt into a Rubbermaid dustpan. Inquiry determined that the doorman wanted a thinner lip on the pan, so less dirt would remain on the walk. He got it.

Each complaint is documented by marketing people, and executives are encouraged to read the complaints. One complaint by customers in small households, who found the traditional rack-and-mat too bulky to store, led to a compact, one-piece dish drainer. The firm generally finds its problems by using problem analysis in focus groups and solves them internally. They occasionally use scenario analysis to spot a problem. But scenario analysis is much less useful than problem analysis because the lead times are so short; their new product cycles make them concentrate mainly on already existing problems. The organization is kept conducive to newly created ideas by promoting cross- functional association among workers. Problem-find-solve is encouraged at all levels.

Some other new items have been:

Bouncer drinkware was created for people who fear using glassware around their swimming pools. A lazy susan condiment tray and other patio furniture products came from studies of lifestyle changes.

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People working at home told of problems that led to a line of home office accessories, including an “auto-office,” a portable device that straps onto a car seat and holds pens and other office articles.

Generally speaking, Rubbermaid does not make much use of attribute listing and other fortuitous scan methods of ideation, including the various mapping approaches. It does find that product life- cycle models can be useful, and it closely tracks competitive new product introductions.

Rubbermaid is, however, always looking for new ways by which it can come up with good new product concepts. They know from experience, for example, that there will be new ways by which problem-find-solve techniques can be used. And perhaps the fortuitous scan methods can be of greater use than now perceived.

What would you recommend to Rubbermaid management? Should they use any of the concept generation techniques discussed in this chapter, in addition to the methods they traditionally use? Which ones, and why?

1For details on usage of the trade-off technique, see Dick R. Wittink and Philippe Cattin, “Com m ercial Use of Conjoint Analysis: An Update,” Journal of Marketing, July 1989, pp. 91–96; Dick R. Wittink, Marco Vriens, and Wim Burhenne, “Com m ercial Use of Conjoint Analysis in Europe: Results and Critical Reflections,” International Journal of Research in Marketing, January 1994, pp. 41–52; and Gary L. Lilien, Arvind Rangaswam y, and Tim othy Matanovich, “The Age of Marketing Engineering,” Marketing Management, Spring 1998, pp. 48–50. 2Som e research suggests that verbal representations were good for facilitating judgm ent, while pictorial representations are good for im proving respondents’ understanding of design attributes. See Marco Vriens, Gerard H. Loosschilder, Edward Rosbergen, and Dick R. Wittink, “Verbal versus Realistic Pictorial Representations in Conjoint Analysis with Design Attributes,” Journal of Product Innovation Management, 15(5), Septem ber 1998, pp. 455–467. 3In addition to ranking, other types of responses can also be gathered. For exam ple, respondents can be presented with pairs of cards and asked to state which they prefer. The different techniques lead to sim ilar results. See Gilbert A. Churchill, Jr., Marketing Research: Methodological Foundations, 6th ed. (Fort Worth, TX: Dryden, 1995). 4These are estim ated by looking at the ranges of utilities of the three attributes (that is, the gap between the highest and lowest utilities). As seen, the range for spiciness is 3.441. The ranges for thickness and color can be calculated as 1.987 and 0.322. Sum m ing the three ranges yields a total of 5.750, and each range is divided by this am ount to get its relative im portance. For spiciness, 3.441/5.750 5 59.84 percent. 5See discussion in David R. Rink, “An Im proved Preference Data Collection Method: Balanced Incom plete Block Designs,” Journal of the Academy of Marketing Science 15, Spring 1987, pp. 54–61; also Joel H. Steckel, Wayne S. DeSarbo, and Vijay Mahajan, “On the Creation of Acceptable Conjoint Analysis Experim ental Designs,” Decision Sciences 22, Spring 1991, pp. 435–442. 6Steve Gaskin, “Navigating the Conjoint Analysis Minefield,” Visions, 37(1), 2013, pp. 22–25. 7Adapted from Robert J. Dolan, Managing the New Product Development Process: Cases and Notes (Reading, MA: Addison-Wesley, 1993), p. 125. 8The techniques and exam ples are adapted from the Sawtooth Software Web site, www.sawtoothsoftware.com . Sawtooth Software is one of the leading providers of conjoint analysis software. 9Fora good, fully worked-out exam ple, see Nelson Whipple, Thom as Adler, and Stephan McCurdy, “Applying Tradeoff Analysis to Get the Most from Custom er Needs,” in A. Griffin and S. M. Som erm eyer, The PDMA Toolbook 3 for New Product Development (New York: John Wiley, 2007), Chapter 3. 10Peter D. Morton and Crispian Tarrant, “A New Dim ension to Financial Product Innovation Research,” Marketing and Research Today, August 1994, pp. 173–179. 11John R. Dickinson and Carolyn P. Wilby, “Concept Testing With and Without Product Trial,” Journal of Product Innovation Management, 14(2), March 1997, pp. 117–125. 12Jan P. L. Schoorm ans, Roland J. Ortt, and Cees J. P. M. de Bont, “Enhancing Concept Test Validity by Using Expert Consum ers,” Journal of Product Innovation Management, 12(2), March 1995, pp. 153–162. 13Ely Dahan and V. Srinivasan, “The Predictive Power of Internet-Based Product Concept Testing Using Visual Depiction and Anim ation,” Journal of Product Innovation Management, 17(2), March 2000, pp. 99–109. 14For detailed inform ation on inform ation acceleration, see Glen L. Urban, Bruce D. Weinberg, and John R. Hauser, “Prem arket Forecasting of Really New Products,” Journal of Marketing, January 1996, pp. 47–60. See also Phillip J. Rosenberger III and Leslie de Chernatony, “Virtual Reality Techniques in NPD Research,” Journal of the Market Research Society, October 1995, pp. 345–355.

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15Reportedly, Caterpillar lets its custom ers virtually test drive tractors under different driving conditions using a sim ilar virtual reality technique. See Brian Silverm an, “Get ’Em While They’re Hot,” Sales and Marketing Management, February 1997, pp. 47–52. 16The scientist used 11 param eters (dim ensions), each of which had between two and four alternatives; that set yielded 36,864 com binations (possible engines). Incidentally, that m atrix also yielded two com binations that becam e the Germ an V-1 and V-2 rockets in World War II. See Fritz Zwicky, Discovery, Invention, Research: Through the Morphological Approach (New York: Macm illan, 1969). 17This case was prepared from rubberm aid.com and m any public inform ation sources.

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FIGURE III.1 Concept/Project Evaluation

   

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