ESRC KEO: e-Resilient Retail

Retail Research and Big Data Meeting – 07/10/2014

Transcript

1. ! www.alex-singleton.com! @alexsingleton! ! Consumer Data Research Centre An ESRC Data Investment L LDC Alex Singleton, Barnes Oswald, Les Dolega, Michail Pavlis, Dean Riddlesden ! Department of Geography and Planning / Local Data Company ESRC KEO: e-Resilient Retail
2. L LDC L LDC
3. L LDC
4. L LDC University Partnerships (Current/Previous) • LDC’s strategy is to partner with leading academic institutions to build new indices and insight which have credibility and academic rigour: • UCL (CLG boundaries – update) • Oxford Institute of Retail Management (Diversity Indices) • Liverpool (e-Resilience & Business Rates) • Stirling – (Top 100 Towns, Booze/Charity/Money/Gambling Index) • Loughborough (Consumer Journeys) • Manchester (Local Shopping Provision) • Cardiff (Role of Town Centres in Large Urban Areas) • Cambridge (Undergraduate & Post Graduate Work) • Over 15 undergraduates supported through licences to LDC’s analytics and consultations with management
5. L LDC Retail Summits • 14 to date • Attract over 200 people to each summit • Attendees include leading retailers, financial institutions, press, academics, government • Receives national coverage
6. L LDC University Partnerships (Future) • Town Centre Boundaries – dynamic boundaries updated every 6-months • Catchment Areas – fit for purpose for current retailing trends • Typology of Places • Investment Dynamics (role of an asset and the surrounding centre and competing centres)
7. L LDC A Research Problem • To what extent are retail centres exposed to populations with variable engagement in online retail
8. L LDC A Business Need • A way of estimating the catchment area for retail centres • A better understanding of the drivers of vacancy rates • Of which use of the Internet engagement is widely attributed
9. L LDC
10. L LDC
11. L LDC What do you need to know? • Estimate of those people likely to visit a retail centre • Influences on the level and type of engagement of such populations
12. L LDC Catchment Estimates LSOA (i) A - attractiveness D - distance Retail Centre (j) L LDC Pij = A↵ j D sj ij Pn j=1 A↵ j D sj ij Large, Medium, Small (s)
14. 70%
15. L LDC
16. L LDC
17. L LDC Online&sales& Supply&factors& Demand&factors& Retail/Service& Offer& Catchments& &Demographics& Retail& e<Resilience& Vulnerability/adapta?on& Connec?vity& Consumer&Behaviour& retail/service+mix+ +++a.rac/veness++ +++++shopping+convenience++ + + socio5economic+status+ age+ + ++infrastructure+ +++++++speed+ rurality+ Engagement+with+ICT+ ++++++Shopping+online+ +
18. Distance to telephone exchange
19. Distance to mobile mast
20. Download Speeds
21. % households with Internet connection
22. % of people who mostly use mobile phone for internet access
23. % Students
24. L LDC From Attributes to Contexts Variable 1 Variable 2 Cluster 1 Cluster 2 Cluster 3
25. Internet User Classification
27. L LDC Work in Progress • Catchment model refinement • Population • Geodemographics • Day V Night • Retail hierarchy • Sensitivity to e-Resilience • Retail centres…
28. L LDC Work in Progress
29. L LDC Work in Progress