Tools in data driven decision Making: Sensory Research

Differentiation is a source of value creation, in a market where customer needs re converging, its vital to understand the attribute which differentiates your products from competition or other related goods. Sensory research is an essential part of research and development, it is used to evaluate consumer products based on the sensory experience (taste, smell, sight, touch). One of the most important tools is Penalty Analysis, its used to identify potential direction for improvement of products, based on survey questions on consumers or users.

Penalty Analysis

Penalty analysis is a workhorse in innovation and product development (generally referred to as sensory research), through it is not formal method for determining drivers of liking, is an effective tool for linking attribute performance to Overall Liking. It gives insights on points of improvement, and specifically the direction of the improvement. The analysis is based on two types of data, preference data and Just About Right (JAR).

Preference Data or simply called liking scores on a 9-point scale, or for characteristics of a product such as speed of a car, comfort, fuel consumption etc. On the other hand, Just About Right uses 5 points scale for one or more characteristics of the product of interest. 1 correspondent to “Not enough at all”, 2 to “Not enough”, 3 to “Just About Right” or “The ideal”, 4 to “Too Much” and 5 to “Far too much”. For example, for a ready to drink juice, one can rate the sweetness, for a car, one can rate the comfort.

The analysis is about variation on the product overall liking due to the product having “Too much” or “too little” of the attribute of interest. One insight which can be derived from penalty analysis is segmentation and so-called “drivers of liking.” About segmentation, the results for a given product may be different for opposing groups. One group may desire more of a given attribute another group preferring less, this gives an opportunity for market segmentation and maybe a line extension.

Another important insight can be derived from attributes that show high penalties. For such attributes, there is a likelihood of a high degree of consumer sensitivity to non-optimal levels, in most cases, these attributes are important in determining consumer overall satisfaction. These characteristics are referred to as “drivers of liking,” or “drivers of disliking” in penalty analysis. Like the case with segmentation, this kind of evidence is not airtight. It simply means that for these products, as currently formulated may need some variation in some intensity-related attribute, it is also an indication of existence of other important attributes which have not been studied.