The relationship between high street footfall, attraction and conversion

The three critical measures of retail performance are often suggested to be “location, location, location”. However this generalisation, like many others in the literature, is of little practical use without underpinning empirical evidence. At a time when high streets find themselves under pressure from emerging omni-channel shopping behaviours, now more than ever retail managers need reliable performance benchmarks and comparators. We report findings from a mass observation of high street shopping behaviour in a single category, covering four brands, competing in four location types over two days. Our aim was to identify predictable behavioural norms between the key retail metrics; footfall density, shopper attraction and shopper conversion. Such evidence-based relationships, empirical generalisations, would then imply that the one number that matters is high street footfall, tentatively quantifying the retailers’ mantra. Although shopper marketing (conversion) is much studied (Sorensen, 2005; Underhill, 2009; Desforge & Anthony, 2013) footfall density has to date been the preserve of the urban planning rather than the marketing literatures (exceptions are Denison, 2005; Kirkup, 1999; Yiu and Ng 2010) and we found no studies linking all three metrics. Our observations revealed systematic regularities. Despite great differences in multiple conditions (base footfall, timings, brands, trading locations, frontage) attraction rates remained close to 4% for each competing outlet with an average conversion rate of 43%. Attraction patterns conformed to a well-known mass observation phenomenon, the Law of Double Jeopardy (McPhee, 1963; Ehrenberg, Goodhardt & Barwise, 1990), such that higher share brands attracted slightly more shoppers into store. Double Jeopardy was not so clear in the conversion ratio. Results therefore suggest a law-like relationship between footfall and retail volumes that might be usefully applied in a wide range of circumstances, for example to negotiate rent, forecast sales and to evaluate performance variances between competing brands or between outlets within a retail brand.