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The project, led by retail intelligence specialists (Springboard), is a partnership between IPM and retail and IS researchers (Manchester Metropolitan University), big data and computing experts (Cardiff University), technology designers and usability experts (MyKnowledgeMap), retail property owners/managers (BCSC, New River Retail and National Association of British Market Authorities), retailers and pop-up (National Market Traders Federation and PinPointer), High Streets (Ayr, Ballymena, Bristol, Congleton, Holmfirth, Morley and Wrexham) and policy experts (Association of Town and City Management).
Recent IPM research used Springboard's footfall data from 70 UK locations to build a new typology of retail centre/towns, based upon the 'signatures' of different types of customer experience. We found a relationship between a distinct town type and constant or increasing footfall. This is customers 'voting with their feet' being attracted to centres that offer them the experience they require. Retailers located in places that attract more footfall perform better; there is a “strong correlation between spend and footfall" (Springboard, 2013). To date, many retailers have not seen a need to cooperate in specific locations. “As each firm follows its own agenda and goals and may not see itself part of a larger, value-added channel” (Van Bruggen et al., 2010). To enhance the overall customer experience, retailers need to cooperate and strengthen the attractiveness of the places in which they are located (markets, the high streets, shopping centres etc).
The project will facilitate and quantify the value of collaboration, demonstrating the relationship between collective customer experience and individual retail performance through a new, easy-to use, simulated interface (a footfall optimiser), that brings information on town type, customer experience, customer demographics, etc. to individual decision makers, from retailers to place managers, enabling them to adjust their operations to meet local preferences, optimising both the customer experience and their own business performance. Algorithms and equations developed in the research stage of the project will be used to develop software that will predict and monitor the impact of interventions (such as changes to car-parking charges, opening hours, or resident population, etc.) on customer experience levels. This will bring complicated data analysis techniques to all town centre stakeholders, so that they can collectively decide strategies and interventions to optimise performance.