The 39 steps – to understanding High Street performance – Part 1

The 39 steps – to understanding High Street performance – Part 1

Infographic-website_900x900This month our new Innovate project started. The project will bring big data to town and city centre decision makers, enabling them to optimise footfall whilst also improving the experience of centre users. The first stage of the project (running from now until Spring 2017) is very research focused.  Because we have over 9 years of hourly footfall data, courtesy of the project lead Springboard, the research team at the Institute of Place Management (Manchester Metropolitan University) and the University of Cardiff can really start to work out how and why town and city centres perform as they do.  Our findings will then be incorporated into a place management information system and a serious of dashboard products, built by our technology partners MyKnowledgeMap.

These new products will support decision making in towns and cities, by making important data more readily available and more easily accessible to the wide range of stakeholders who need to collaborate to build strong centres.

One of the challenges with big data is what to do with it.  It may seem an obvious starting point, but first the research team have had to identify a definitive list of research questions that we want the data to answer. The Principal Investigators for both the IPM/MMU team and the Cardiff team (Cathy Parker and Christine Mumford)  met in August to compile such a list of research questions (39!) that we will be answering over the next few months and we are sharing these here*.  As always, any comments, observations or feedback is most welcome:

RQ1: Are the distinct town types (comparison, specialty, convenience/community) recognisable in a bigger data set?

RQ2: Are there other signature types present in the data?

RQ3: How are signature types defined?

RQ4: How many UK retail centres have (or have had) a recognisable monthly signature type?

RQ5: How do the monthly signature types differ by week, day of the week and hour?

RQ6: How well does our original HSUK2020 model predict footfall?

RQ7: Can we refine original HSUK2020 model to improve forecasting ability?

RQ8: Can we (should we) develop a more accurate catchment predictor?

RQ9: What is the relationship between the amount of footfall and town types?

RQ10: If we build a hierarchy of towns by size of footfall how does this compare to existing hierarchies?

RQ11: What is the influence of location?

RQ12: Which of the 25 priorities for improving the vitality and viability of high streets can be operationalised through the use of existing secondary data?

RQ13: Which of the 201 factors can be operationalised through the use of secondary data?

RQ14: Which of the 25 priorities that CAN’T be operationalised through secondary data do we want to collect primary data for?

RQ15: Which of the 201 factors that CAN’T be operationalised through secondary data do we want to collect primary data for (e.g. those in the ‘top 20’)?

RQ16: What are the best ways to visualise significant relationships both academically and practically for towns and other partners to engage and understand.

RQ17: What other (non-footfall) measures of town centre performance can be identified?

RQ18: Can we build a model of town centre performance?

RQ19: What is the baseline performance of pilot towns (footfall, retail sales and customer experience)?

RQ20: Is there a relationship between baseline performance and model of town centre performance?

RQ21: How do we classify and measure collaboration activities?

RQ22: What are the relationships between collaboration activities and performance (individual trader and collective town) in pilot towns?

RQ23: What collaboration activities are associated with higher levels of performance?

RQ24: Can we identify distinct movement signatures in tracking data?

RQ25: What performance indicators can we deduce from the tracking data (e.g. dwell time)

RQ26: Is there a relationship between movement signatures and footfall signatures?

RQ27: Do we need to establish a composite signature based on footfall and movement?

RQ28: How do we best visualise performance (footfall, sales, customer experience, dwell and any other performance indicators)?

RQ29: Can we identify towns that show unusual or inconsistent performance behaviour (such as sudden drops or rises in footfall)?

RQ30: Can we identify towns that show unusual or inconsistent performance trends (such as much weaker or stronger performance over time)?

RQ31: Can we explain unusual or inconsistent performance trends and/or, where appropriate, develop hypotheses to explore these further?

RQ32: Can we decompose data set into similar groups (based on footfall, based on retail sales based on retail sales per footfall and based on customer experience and based on customer experience per footfall). Establish relationships between other performance indicators and footfall.

RQ33: What parameters will we use to establish optimisation of performance (e.g. town type, town size?

RQ34: Can we identify top 50 towns that optimise performance?

RQ35: What additional resources will be needed to commercialise the data analysis we are prototyping?

RQ36: How does the weather influence footfall?

RQ37: Is post-Brexit footfall lower, allowing for the weather conditions?

RQ38: Can town types be identified through ‘partial’ footfall data?

RQ39: Can a measuring methodology be developed so that towns know when to count (hour/date etc.) to identify their town type?

Note: Outside of the 7 pilot locations (Ayr, Ballymena, Bristol, Congleton, Holmfirth,Morley and Wrexham) no towns will be identified by name when we disseminate the research findings from the project.


*We will publish more details on the research questions next week.

This entry first appeared on Prof Cathy Parker’s blog.

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