How to win back fast-food customers after years of rising prices
With rising prices over the past few years, how can fast-food restaurants regain customers in a price-sensitive market?
What it does for you
The impact of price changes on buying behavior is often captured by scanner data, where price changes are monitored over time and the influence on volume changes measured. This data may give insights in how competition organized their pricing strategy in the past, and how this has influenced your own portfolio. However, it does not give sufficient information to what extent competition can impact your business in the future. Especially for pricing scenarios that have not happened in the past, scanner data cannot give information on how competition could potentially steal market share and revenue.
Helping you answer questions like
- How should we optimize our line up/pricing strategy to be able to compete with the new price changes of our biggest competitors?
- How would a competitor price move impact our market share and revenue?
- How is portfolio revenue and share impacted when competition follows our new pricing strategy?
- What pricing strategy are my competitors most likely to adopt and how price sensitive are my customers towards the brands and products of my competitors?
How it works
SKIM’s decision behavior platform builds on over 30 years of experience in practicing and advancing choice modeling techniques. We provide an adaptive approach to each project with the appropriate blend of methodologies to fit the project and market needs. Our approach is based on survey testing with the relevant customer base away from your store or website.
Through “virtual shopping trips” we can replicate store shelves or websites and include the offering of competition, which enables us to not only identify pricing opportunities for your own business, but also identify the opportunities for you competitors and give clear insights to how they could impact your business .
SKIM uses conjoint methodologies to test new market scenarios in a survey environment providing all the required insights. Choice-based conjoint allows you to test different feature and price bundles as concrete products and test which product mix works best to maximize share and revenue.
