Peter has been working in research and finance roles for 15 years and is known by clients as having the rare trait of possessing a strong understanding of business issues coupled with outstanding conceptual and technical analytic skills. A natural problem solver, he has a unique ability to create clarity out of complexity.
He has had exposure to business, marketing and communications matters across most industries, in particular finance, telecommunications, FMCG, public transport, insurance and energy.
Prior to Nature, Peter headed up the Customer Analytics department at Roy Morgan Research, before which he was an Actuary for Mercer in Superannuation.
A graduate of Melbourne University with Honours in Commerce specialising in Actuarial Studies and Finance, Peter is a regular speaker at AMSRS Professional Development Programs, and regularly publishes articles for research and marketing publications.
Peter is on the editorial committee for the Research News monthly magazine, a regular contributor to AMSRS publications and conferences, and qualified as an Associate of the Institute of Actuaries of Australia.
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.