Data Science Strategy
Vypr helps to improve what ends up on supermarket shelves. They gather opinions from consumers to help retailers and manufacturers to validate and develop products. The platform provides a (disruptively) cheap way to get insights that save waste from launching new products destined for failure and tune innovation to improve profits.
Vypr gathers data from a mobile app that is designed to emulate real-world shopping decisions as closely as possible. This is made possible by the lessons learned from cutting edge behavioural economics research.
Vypr approached Infonomics to help develop some advanced methods for analysing this data.
We developed a Data Science Strategy to support Vypr’s Roadmap. The strategy introduces significance testing, multinominal logistical regression modelling and conjoint-analysis. These techniques will help to inspire confidence with statistical rigour to produce new insights:
- focussing reporting such that only the most scientifically robust results are presented
- providing relevant context to help users take better informed decisions
- exploring the role of psychological, social, and economic factors in consumer behaviour
- predicting the most profitable pricing strategy and product design
- filtering to the most consistent and reliable respondents and
- ensuring representativeness and supporting retention of the panel
- demonstrating impact to clients with impact evaluation
We are now working with VYPR to implement these recommendations.
innovation business-case strategy data-science product-development