In the dynamic realm of FMCG (Fast-Moving Consumer Goods) markets, businesses constantly face critical decisions that can make or break their success. Key strategic questions arise, such as:
- Can growth be unlocked by altering our pack price hierarchy?
- What impact would reducing pack sizes and optimising shelf prices have on sales?
- Is our current pricing strategy optimised for maximum profitability?
- Could we stretch our underlying price and leverage existing promotional mechanics more effectively?
- Can we refine our approach to promotions, potentially increasing our market position through multi-buy offers?
- Is our product assortment optimal? Could we unlock value by restructuring our good/better/best offering?
Traditionally, choice modelling has been the go-to method for answering these crucial questions. However, this approach falls short when applied to dynamic FMCG markets.
The Dynamic Market Problem: Unveiling Hidden Interactions
To illustrate this dynamic market problem, consider the in-market sales of a supermarket brand in an ambient drink category over a 26-week period. The data reveals that:
- Sales surge during promotional periods, reaching multiples of median sales.
- Promotional effectiveness is not constant, as evident in the difference between Weeks 9 and 22. The same promotion yielded vastly different sales outcomes.
The data masks a critical factor: In Week 22, the brand faced a deep promotion from its main competitor. This interaction significantly reduced the promotional effectiveness in that specific week.
Key Implication: Beyond Simple Price Elasticity Curves
The impact of pricing activity is contingent on the unique dynamics unfolding in each individual week. This renders FMCG markets too complex to be accurately represented by simple price elasticity curves.
Nature's Solution: A Monte Carlo Overlay
To address the limitations of traditional choice modelling in FMCG markets, Nature has developed a revolutionary approach that incorporates the dimension of time into choice modelling. Through a Monte Carlo overlay, our choice model results are extended to represent a full promotional calendar (encompassing 52 individual weeks).
This approach ensures a stable projection of volume and value for each brand/product, accounting for the actual promotional calendar and the impact of promotional interactions between brands.
A Revolutionary Advance in Choice Modelling
Nature's groundbreaking Monte Carlo overlay approach represents a significant leap forward in the field of marketing analytics. Our work utilising this methodology has gained recognition at The Research Society's Awards, a testament to its potential to revolutionise FMCG decision-making.
Research Effectiveness Awards 2023:
- Winner of Consumer Insight category
- Winner of Technology & Innovation category
Research Effectiveness Awards 2021:
- Winner of Technology and Innovation category
Delve Deeper: Access Our Whitepaper
To gain further insights on this approach, leave your details below to request access to our comprehensive whitepaper, which includes detailed explanations of the methodology, along with case studies showcasing the application of the Monte Carlo approach to choice modelling.
In the dynamic world of FMCG markets, Nature's groundbreaking approach to choice modelling empowers businesses to make informed decisions that drive growth and profitability.
Work on Nature’s Monte Carlo overlay has been developed by Nature Senior Partner Peter Stuchbery and Partner Emma Tommasini.
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