It's a Choice, Real Numbers or Estimates

As an industry we still find that nine out of ten new products we introduce into the market fail within the first twelve months.  this is a gross waste of investors' capital as well as Company time and effort.  From my perspective, there are two main reasons for this high failure rate.  One is our rush to market as covered in my Views of September 21, 1996  The second reason appears to be the way in which we evaluate new opportunities.  Some companies project market potential from responses to concepts.  Others give a person a product and then ask how well they like the product and how likely they are to but it.  Little thought is given to the biases introduced through the selection of the panelists and the protocol of the study.  These studies are great for the development of a product but leave a lot to be desired when assessing the market potential of the same product.

I frequently hear the argument that the researcher knows not to take the data from these studies as literal truths.  You should compare the data with our historical database.  Maybe that is our real problem, historically we are wrong nine out of ten times.  We should expect that when markets do not react normally, forecasting based on norms will yield misleading results.

What is the solution?  My grandfather had a saying that I think fits this problem very well.  He would say that if you wanted to know how good anything was, try to sell it and see what you get in return.  I guess if you want to know if your product will sell, try to sell it.  It sounds easy and it is.  There are companies out there that do this for a living.  You can choose a very simple design or a complex one and many in between.

For instance, you can use a very simple approach whereby the product is placed in two stores for a period of six weeks.  Category sales data (by week) are collected for four weeks prior to the study and then for the six week test period, yielding real sales data relative to the category.  The cost of this study would be approximately $10,000.

On the opposite end of the complexity scale would be a study that was conducted for six months, yielding sales data, projection analysis, reasons for purchasing, reasons for not purchasing and most importantly reason for not re-purchasing along with a complete diagnostic evaluation of who purchases, when, frequency, etc.  The cost of a study of this nature is in the area of $180,000, complete with promotion and advertising capabilities.

I have conducted a considerable number of studies of both types of research, utilizing minimal product produced in a pilot plant to experimental plant produced product.  I have used as few as two stores in a test to as many as thirty-two.  My experience with field services in this type of research has included a number of companies, but I found only two acceptable.  They are Sorensen Associates Inc and Elrick & Lavidge.

A parting thought, it may seem that this type of research is expensive but it is not if you compare it to the cost of a failure in the market.