Discussion papers

CPM-02-97 - 8 May 2002

Integrating Domain Expertise With Aggregate Data Using Evolutionary Computation

Bruce Edmonds and Scott Moss


In marketing there are (at least) two separate worlds of knowledge: information about consumers (from surveys, panel data, etc.) and information about purchase decisions (from aggregate sales data and similar). Relating these two worlds is difficult because the consumer views cannot be easily traced to a part of the sales data (or vice versa) – there is no “psychological” model that bridges this gap (al-though there have been some attempts at making one). Part of the problem is that the standard method is to take a model structure that is a prior acceptable (to academics and other experts), parameterise it to fit the data and finally present the results to practitioners (those who actually have to try and make sense of the results in terms of costly marketing decisions). The data is thus used to try and adapt a theoretical model to specific market of concern. However this means that the meaning of the model is in the hands of the academic or expert who formulates and manipulates the model – the practitioners are left with a black-box model and (if they are lucky) some results. Unless the model-maker and the person with experience in the market happen to be the same person then, necessarily, the model will have to be applied somewhat ‘blindly’ to the market and its formulation will be unable to make use of the context-dependent domain knowledge of the practitioner.

In contrast to this traditional ‘division of labour’, we propose an approach whereby the final model is constructed, in part, by the practitioner (and optionally a computer) but within a framework devised by academic modellers. Thus we seek to delay some of the model specification. This is done at three different levels: generic framework, market context and preference model.

Accessible as HTML