HOW TO MEASURE FOOTFALL – ACROSS THE ENTIRE TOWN CENTRE

26th November 2020

Government funded programmes, which in include the High Street Heritage Action Zones, the Future High Street Initiative Fund and European Regional Development Funding (ERDF) all have a requirement to monitor footfall levels as this data is often, if not always considered as an important part of monitoring the success of future regeneration initiatives in Town Centres.

One street is not the town centre

Unfortunately the traditional methods used to count and monitor footfall use hardware (cameras or wifi) which are costly to deploy and maintain and involve time consuming and difficult implementation programmes.

Often to minimise these of issues, town centres are forced to restrict or prioritising where to measure footfall and end up excluding many locations from their analysis, particularly those that are under represented in the provision of retail and leisure businesses or sites which are open spaces such as Urban Parks, as the installation of hardware is impossible.

Restricted sites for counting due to harware costs, availabilty of WiFi and/or conservation area constraints

Machine Learning, AI and GPS

So could the latest Machining Learning and AI solutions help solve the problem?

Firstly, the use of the latest machine learning and Artificial Intelligence (AI) techniques combined with GDPR compliant GPS data and polyons (digital shapes) remove all of the constraints and costs of tradional camera and wifi counting solutions.

Secondly machine learning and AI can now accurately predict how people are going to behave (the same way Netflix accurately recommends your next TV series to binge watch!) by analysing the behaviour movement ‘patterns’ of real people and accuratly predicting where they will move to in the future – known as predictive analytics. These real behaviour predictions are then extropolated (in the same way for COVID 19 the Office for National Statistics uses a sample of the UK population to predict actual UK infection rates) to calculate the actual volume of visitors in a location at any time.

Thirdly, by using polygons shapes, entire town centres can now be measured for footfall, easily and at a fraction of the cost of deploying hardware . The example ‘geographic area’ below, is for the High Street Heritage Action Zone for Hexham. Footfall is required to be measured across the entirety of the Heritage Action Zone and across multiple streets

High Street Heritage Action Zone – Hexham

Below is the digital polygon which exactly traces the required area where footfall is need to be calculated. This would be financially prohibitive and practically impossible using cameras or wifi methods.

Street Level analysis

Sub behaviour polygons can then be created in seconds for any street, open space or urban park area, providing the footfall counting and insight which truely reflects how visitors are using the town centre.

Lower Cost, More Locations – What’s not to like?

Machine Learning/AI combined with GPS location data really does solve the problems of having to limit the size of the geographic area where you want to capture footfall behaviours due to limited budgets and the difficulty of installing counting hardware.

TownCentre.AI is a UK wide footfall and visitor behaviour platform using machine learning and AI predictive behaviour technologies to accurately calculate the footfall for over 1,950 town centres across the UK.

Register at TownCentre.AI for a free 7 day access pass, to footfall and visitor behaviour for your town centre today!

  • 1 billion data points

    Every month we analyse 1,000,000,000 continuous location data points across the UK

    1,900+ towns

    From Wick to Penzance, Aberystwyth to Hull, we know where and how people shop
  • 1,350+ Retail Parks

    We measure shopper visits, 24/7 for every tenant occupier on 1350+ retail parks

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    We track, measure and rank performance for over 1,400+ retail and leisure UK operators

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    No ad exchange data for us (it is only 1% useful for in-store visit measurement) - ours is all 1st party and continuous!
  • 1.4 Million GDPR compliant phones

    All our data is GDPR compliant and contains no personal identifiable information

    Geo Fenced Accuracy

    Every location feature is manually geofenced, delivering the most accurate store visit measurement in the UK

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