An academic study. Why bother? People do not want to go into six different shops for six different articles; they prefer to buy the lot in one shop. The American Grocer, 1892 For better or worse this distributive revolution is carrying us away from shopkeeping to mass distribution McNair, 1931
Online share of home retailing 2014 Centre for Retail Research 2013
Online retailing 16% pa £52 bn in 2015 M-retailing 62% this year £7.92 bn Centre for Retail Research 2013
Town centre share of retail spend Parliament 2014
Vital & Viable Town Centres Planning Policy Guidance/Statements Business Improvement Districts High Street Britain 2015 The Portas Review Understanding High Street Performance Future High Streets Forum The response
And if 166 factors were not enough….. Partner towns identified 50 additional factors that influence the High Street 33 additional studies reviewed 201 factors finally identified, but: –how much influence does each one have? –what should towns be focussing on?
The Delphi Technique The Delphi method is unique in its method of eliciting and refining group judgement as it is based on the notion that a group of experts is better than one expert when exact knowledge is not available. (Paliwoda, 1983).
22 Experts participated PractitionerAcademic Major retailerManchester Metropolitan University Shopping centres ownerUniversity of Leicester Urban consultantUniversity of Dundee Retail letting agencyUniversity of Ulster Urban policy groupOxford University Trade associationUniversity of Manchester Professional bodyUniversity of Liverpool University of Portsmouth University of Loughborough
Consensus reached on 1.How much influence each factor has on the vitality and viability of the High Street 2.How much control a location has over the factor
Data Footfall supplied by Springboard 62 UK towns and cities 30 months of footfall (2012-2014) 563,828,709 people counted!
Spatial factors Location Distance to centre Size/Type of town Spatial structure Towns can’t do anything about these factors! Towns in NW & NE have 10% less footfall than expected
Macro factors Economy Consumer trends Business rates Ageing population Technology Retail planning policy Towns can’t change these on their own 25% decline in footfall in last 3 years (internet shopping and recession) We predict 21% decline by 2020
Meso factors Barriers to entry Competition (other towns) Comparison/Convenience Out of town shopping Tenant variety Vacancy rates Towns interact with these/have some Influence A stronger or OOT centre within 10 miles account for 30% less footfall
Micro factors Cleanliness Visual appearance Networking Opening Hours Attractions Centre Marketing Amenities Car-parking Entertainment Leadership Our model predicts that micro factors explain up to 37% of variation in footfall
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