It appears that package design research and cancer research share more than we might have suspected. And that for both, family history may be as good a predictive tool as any.
Package designers, and the consumer product marketers that we serve, continue to look for the “holy grail” when it comes to the right combination of design research tools. It now appears, in spite of the promise of the human genome project and the breakthrough opportunities it offers, that medical research is no different.
Package design research is an evolving combination of art and science, the qualitative and the quantitative. . . really not so different from the design process itself. And most consumer product marketers use a mix of qualitative tools like interviews, focus groups and ethnography, combined with various quantitative tools.
A recent study conducted by Brigham and Women’s Hospital in Boston, as reported in the NY Times, suggests that a combination of quantitative and qualitative research tools will continue to be useful in medical research for some time to come.
As the article mentions, “a medical team . . . collected 101 genetic variants that had been statistically linked to heart disease in various genome-scanning studies. But the variants turned out to have no value in forecasting disease among 19,000 women who had been followed for 12 years. The old-fashioned method of taking a family history was a better guide, Dr. Paynter reported this February in The Journal of the American Medical Association.
There has also been research on the “family history” or the multi-generational usage of brands. One such study by Barbara Olsen, of SUNY, for the Association of Consumer Research suggested that while brand loyalty is certainly multi-generational, and an important long-term influencer of purchase decisions, it varies by category. For instance toothpaste is transferred from generation to generation more often than bath soap.
So it would appear that family history continues to be a simple and fairly predictive guide for both medical research and predicting brand preference.
