8 key metrics for malls to optimise layouts, conversion and advertising

Juha Mattsson

We are seeing retailers and shopping centres moving quickly into a more data-driven approach in the design of their layouts and indoor advertising. This is thanks to the emergence of indoor positioning based solutions which enable malls and other physical sites to accurately measure the behaviour of their visitors. This equivalent to how online stores like Amazon are measuring and optimising everything based on practical, real-time evidence from website visitor behaviour.

Which analytics malls should use when optimising their layouts and store locations?

Traditional people counting enables malls to see the total number of visitors through door counting, but not how they are distributed within the mall or access this data real-time. Beyond showing visitor distribution over mall zones, Wi-Fi based analytics technology can bring so many more advantages to a shopping mall, highlighting the paths people take, where they go, how long they stay, repeat visit patterns, and much more.
It is then possible to compare the popularity and performance of different retail locations within a shopping centre – as well as the impact of layout changes, advertising boards and campaigns. Mall analytics are also useful for understanding the timing of daily campaigns for attracting customers, and improving loyalty patterns. Using path and zone analytics, malls are able to identify low-traffic areas and flow patterns and together with store visits, to understand and design the mall appropriately for optimal patterns.

In general, path & zone analytics is very handy for analysing the optimum position of stores within a shopping centre. By analysing the typical paths taken, malls are able to identify low-traffic areas and modify traffic patterns e.g. by placing high-visit stores to certain locations. Also analysing cross-store visits will help to understand and reorganise for maximising customer distributinos and convenience during their visit (e.g. which pairs of stores attract most visits by same customers).

Here is a list of the most typical Wi-Fi analytics based metrics that are useful for shopping centres in optimising their layouts:

  • heatmaps - understanding hot/cold areas as well as crowding
  • zone analytics - how different sections of the mall are performing against each other
  • conversions - e.g. mall-to-store, store-to-store and zone-to-zone
  • traffic - analyse/compare popularity of different parts & retail locations within the mall
  • attribution - measure the impact of campaigns, displays, physical ads on shopping mall level
  • benchmarking - compare the performance of different stores against each other
  • paths - typical paths at malls, segment-specific paths
  • loyalty - patterns such as repeat visits and cross-shopping 

How malls can use in-store analytics & marketing technology to draw customers in-store?

Already based on passive (i.e. anonymous) people flow analytics, retailers can take several actions to drive customers to stores. By measuring mall-to-store conversion - the share of mall visitors entering each store - retailers can track real-time how campaigns, in-mall ads, window marketing and in-store marketing are affecting. Mall level footfall and pattern analytics is also handy for understanding e.g. the timing of daily campaigns for attracting consumers. Retailers can also observe visitor patterns just outside the store and observe how window marketing is able catch people, i.e. stop and convert to store visitors. Yet another way to improve is to look into repeat visit patterns and understand how loyalty patterns work. Would e.g. Saturday be a good day to do something special to attract repeat visitors.

When it comes to active customer engagement, there’s a whole new dimension of personalised in-store engagement that is possible. For shoppers who have opted in for location-based services by installing a loyalty/mall app, retailers can identify their location and use their shopping profiles to deliver personally tailored services and targeted promotions. For example, shops could use the mall's app to post ads/coupons to people at a suitable spot during their mall visit, to increase the number of visits to their store.

How to use analytics for positioning marketing boards in most effective areas?

Shopping centres have recently realised the value of their public spaces for advertising. Digital advertising boards are also shifting the approach from general brand and product advertising into more dynamic and engaging content to drive store visits and purchases.

But like with all (digital) advertising, measuring attribution is key for optimising location and timings, and picking the right content dynamically. At malls, the ad display attribution works generally in three ways:

  1. Using visit pattern data to determine areas with most visitors per hour, to catch best locations for ads.
  2. Using path analytics to track ad effects: did e.g. a store's ad elsewhere in the mall drive additional visits to the store.
  3. Use anonymous customer profiling to cue the content of digital signage (e.g. prioritise a shoe ad on a digital sign if there’s persons near who’ve repeatedly visited shoe stores).

In the end, the increased measurability gives malls additional tools to design effective layouts and experiences that improves the overall quality and convenience of our stay at shopping malls.

Read more on this topic:
Nordic’s Largest Shopping Centre: Wi-Fi Analytics Will Drive Our Marketing Decisions

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Walkbase provides a retail analytics solution for improving the impact of marketing on physical stores and personalising in-store shopping experience.