Chris has been conducting research into the nature of consumers for over 20 years in both corporate and consulting roles. He has a well-developed understanding of marketing, communications and business issues and is an expert in analytic methods to support managerial decision making in these areas.
Chris has a PhD from ANU in quantitative social science and has held fellowships at the University of Wisconsin and Utrecht University, The Netherlands. He has published an academic book and several articles in refereed journals, and has presented at conferences in Cape Town, Vermont, Stockholm, The Netherlands, Australia, and Singapore.
Chris has been a member of the Victorian AMSRS committee and is a regular contributor to AMSRS Professional Development Programs and AFA Strategic Planning training programs. He is a member of AMSRS.
Co-Presenters: Peter Stuchbery
The in-store environment is becoming more and more cut-throat, with pricing strategies pivotal to the success and failure of brands.
Researchers traditionally use techniques such as Choice Modelling to provide insight into the effect of pricing in markets. However, the traditional way in which these techniques are generally applied do not accurately or adequately account for market dynamics and are difficult to interpret in markets in which brands don’t have a single price, but instead in which a pricing strategy is made up of a spread of different pricing mechanics and levels that play out over a time period. Good examples can be found in any supermarket aisle – in which most brands and categories are on different levels and types of promotion at different points in time across the weeks of a month, across the calendar year.
This market reality presents a very real challenge to marketing researchers seeking to understand or predict the impact of changes to the price (regular or promotional) or pack size, on the share, volume, revenue and profit of a brand across a year.
Our research, however, shows that a “Monte Carlo simulation overlay” to a Choice Model can address these issues and create a dynamic market model that accounts for over-time pricing strategies.
This paper explores the shortcomings of traditional Choice Models in FMCG research and outlines how a Monte Carlo simulation method can be used to great effect to increase the ability of Market Research experiments to inform in-market pricing strategies.
Big Data held by corporate organisations presents both a considerable opportunity and equally a considerable threat to the research industry. Big Data, by its very nature is often far from strategic, being highly fragmented, and transaction-level. However, with clear planning and augmenting with customer thoughts/views, it can be used as both a tactical and strategic tool.