By Professor Cathy Parker, Institute of Place Management, Manchester Metropolitan University @PlaceManagement
Internet shopping, which has been hailed as the ultimate level of retail decentralisation, along with the continued aftershocks of the economic recession of 2008 and temporally concentrated lease expiry dates, have created the “perfect storm” for town centre restructuring. Consequently, more city and town centres are changing, adapting to the new technology demands of today’s omni-channel consumer. However, as was the case with out-of-town developments a couple of decades ago, not all centres are able to adapt to this change. Medium-sized traditional town centres in particular are struggling and continue to under-perform, enduring high vacancy rates, victims of out-of-town developments, Internet retailing, economic problems, policy restrictions, poor retail diversity, and outdated infrastructures (like the windowless shopping mall) that are unpopular with today’s consumers. What is also evident is the continuous polarisation between north and south. Many smaller centres, outside of London and the South East are providing a very poor environment for shoppers. And improving the customer experience in these type of locations is the fundamental problem a new project, led by Springboard, with partners PinPointer, is addressing.
Part of the ‘Bringing Big Data to Small Users’ Innovate UK project, the Institute of Place Management at Manchester Metropolitan University and data scientists from Cardiff University are analysing billions of shopper movements, going back over 9 years, across 100 retail centres in the UK, to understand exactly how the High Street is changing. Just as economic geography principles of the 1930s reflected the widespread adoption of the car, now current understanding of how consumers make location decisions must reflect the Internet, as customers search for more convenience and speciality from physical environments. This requires the retailers and other operators to cooperate in a way not previously necessary, when comparison shopping was the main attraction in locations. The use of big data and big data analysis techniques to bring insight to this problem, at a national scale, is novel and the team are making some groundbreaking discoveries.
First, contrary to popular belief, the activity patterns in towns do not just differ by volume. By using K Means clustering techniques with over 17,000,000 hourly footfall counts we have identified four distinct signature types. The footfall data clearly demonstrated that towns had one of four activity patterns which represented the overall function or offer of the location (comparison shopping, specialty, holiday, convenience/community). Whereas the function of towns has, historically, been identified through planning and policy, this analysis was showing a dynamic, or ‘real-time’ classification, based on actual usage and activity. The identification of these new signatures has led to valuable policy recommendations, of interest to any politician, public servant or concerned citizen wanting to combat the decline of the High Street.
To enhance the overall customer experience, within one of these signature types, retailers need to cooperate to strengthen the attractiveness of the places in which they are located (markets, the High Streets, shopping centres etc). Retailers located in places that attract more footfall perform better; there is a strong correlation between spend and footfall. To date, many firms have not seen a need to cooperate in specific locations as each firm just follows its own strategy and does not see itself as part of a larger offer. This is in stark contrast to the consumer’s perspective – attracted to the collective offer of the location, for linked trips or a whole bundle of benefits.
In order to bring about the behavioural change needed to turn town centres back into the bustling heart of communities, the project is sharing the footfall data and other insight directly with retailers and other high street stakeholders, in 7 locations (Ayr, Ballymena, Bristol, Congleton, Holmfirth, Morley, Wrexham). This data will help them understand more about their customers and the locations they trade in, and adjust their individual operations accordingly. Sophisticated new technology, that can not only count consumers, but calculate dwell times and recognise repeat visits, is being developed, tested and installed by Springboard, to monitor the customer experience and count footfall. Springboard are also measuring turnover for the retailers.
For the first time, the project will not only facilitate but be able to quantify the value of collaboration, demonstrating the relationship between collective customer experience and individual retail performance. Project partners MyKnowledgeMap are bringing all this insight (the big data) to the town centre stakeholders (the small users) through a new, easy-to use, simulated interface (a footfall optimiser), that brings information on town type, customer experience, customer demographics, 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 are being used to develop software that will predict and monitor the impact of interventions (such as changes to car-parking charges, opening hours, or gowth/decline in resident population, etc.) on customer experience levels. This project is bringing complicated data analysis techniques to all town centre stakeholders, so that they can collectively decide strategies and interventions to optimise performance. The participative way in which local decisions will be made, and facilitated through technology, with a clear evidence-based ‘feedback loop’ has never actually been comprehensively tested before. The simulation develped in this project allows individual stakeholders, as well as other important stakeholders, such as the Local Authority, to collectively decide upon collaboration interventions, operationalise these and then monitor their impact on both individual and collective sales, footfall and customer experience ratings.
The project team believe that the transfer of data science directly to place decision-makers is exactly the type of innovation that will bring about the type of transformative change needed to reinvent High Streets and town centre’s so they remain relevant to future generations.
You can find out more about the ‘Bringing big data to small users’ project at http://www.placemanagement.org/BDSU