As I have a background in neural networks (I completed my PhD at the Neural Systems Engineering Lab at Imperial College London), I have for some time known that neural networks could be used to make sense of consumer data and give rise to predictions and insights that have been so far unavailable.
Artificial neural networks and implicit reaction time tests are based on the very same assumptions about how the brain processes information – that many of these processes are associative. For consumer scientists this means that perception of a product or service is a composite of every- thing that we have come to associate with it, from adverts to word-of-mouth, to direct experience with consuming the product or using the service, and that such memories are activated in the brain, though most often do not reach conscious registration. It is not a coincidence that the same principles that underlie deep neural networks are the same that underlie implicit response testing.
At Split Second Research we have created EmNet a neural network platform that enables us to dive deeper into market research data. The platform enables us to understand purchase drivers more deeply by mapping brand values (in the form of reaction times or explicit choices) to behavioural ‘outcomes’ towards the brand, (such as purchase intent, willingness to recommend, and so on) as well as explore the relationships between brand values in order to understand different kinds of purchasing decisions.
We simply upload the responses from a survey into the platform, make a few selections based on the kind of network we want to produce, and within just a minute or so it produces outputs that show relations between the concepts we are testing in the survey and salient features in the data. We are currently offering this service free with every survey we are commissioned to undertake. We also offer this service to other agencies who have data they want to analyse using a machine learning approach.
We already have some interesting case studies which we have put together with a description of EmNet (with a brief introduction to neural networks). This six-page report can be obtained on request via the
