Consumer location analytics in retail

How to forecast return on investment from physical stores using consumer location analytics

6 minute read

April 27, 2022

Last Updated: December 22, 2022

Opening a new store anywhere in the world nowadays is fraught with risk, such has been the volatility of consumer behaviour in the last 2 years. Whilst much of that was forced by government rules associated with the global pandemic, personal choice has also shifted seismically.

Not only did customers quickly adapt and become accustomed to sourcing their requirements through other means, mostly online but also more locally to their homes, they began to trust and enjoy their new shopping experiences.

We are only now beginning to see the long-term effects of this behavioural change, and much data is yet to be produced post-pandemic (perhaps) on whether permanent new norms have been established, and what the financial implications are for retail in particular.

Are physical stores still sustainable in the long term, or in the volumes they previously existed? The visible gaps in high streets would suggest that many retailers do not think so, and yet at the same time studies tell us that consumers themselves still very much desire in-store experiences.

Perhaps a better question would be whether the new levels of experience expected by consumers can be profitably provided by retailers. That means goods bought, returned and exchanged interchangeably and seamlessly online or in-store, where the store often becomes a showroom or a collection point.

To answer this question, retailers must understand what motivates consumers to leave their homes and travel somewhere. Where do they go, what need does it fulfil, and how does that relate directly to retailer revenue generation? How are journeys now combining leisure, shopping, exercise, social events, and even remote working? And how do retailers insert themselves profitably into those journeys?

What is consumer location analytics and how can it help?

Location analytics is now a well-established tool, with specialists gathering and processing huge volumes of raw data from public and private sources to enable geo-location mapping of individual consumers at scale.

Essentially data is collected by smartphone apps, where users have opted into location sharing, and with over 3 billion smartphones and 2 million apps, human movement is a highly reliable analysis opportunity. With GPS accuracy to within 12 inches, it is now possible to map where people go, at what times, and for how long. Apply contextual location and activity data to this, and it is easy to see how retailers can access consumer behaviour insights previously only ever dreamed about.

Knowing where consumers go allows understanding of potential motivations and preferences, and creates the opportunity for retailers to be more relevant in their locations, their experiences offered, and ultimately to achieve competitive advantage.

Being able to pinpoint consumer movement quantitatively at scale with such accuracy removes traditional subjectivity from decision making, and enables data-driven, fact-based insights to steer retailers directly down a course that will meet consumer need and expectation.

Of course, the data is still ‘sampled’ even at such scale, and care must be taken to understand true validity, but where this is high, the value of insight gained is unparalleled.

Retailers now have the opportunity to see which competitors their customers also visit, and at what scale. They see customer travel patterns, shopping journey profiles, common daytime and evening locations, and combined with demographic and owned store data, this can build a comprehensive picture of their market opportunities.

Watch a 10 minute video demo of location analytics in action

Watch consumer location analytics demo

A retail success story utilising consumer location analytics

IKEA in Southeast Asia are just one retailer to take advantage of this insight tool. On the launch of their first small store concept in the region, IKEA used Location Optimiser from Ikano Insight to understand shopper movement patterns around the new store in Singapore, and compare directly with their two previously established large format stores nearby.

The new concept store offers a different shopping experience for the busy, modern, city-based consumer. Rather than traditional browsing and instant purchase from a range of many thousands, IKEA is offering a new alternative.

Bringing IKEA closer to where people, live, work and socialise, customers are able to get home furnishing inspiration from over 12 room settings, 16 vignettes and 2 homes, depicting life at home living situations in Singapore.

Customers make purchases through roving store assistants or online. Orders are then delivered straight to the customer’s home. Shoppers can use self-pay checkout options and enlist planning assistance from IKEA staff to map out their dream kitchens, living rooms, or bedrooms.

Understanding customer reaction to this new offering is key, and location analytics provided IKEA with an understanding of how their concept store influenced customer catchment areas, travel distances, visiting times and durations, all compared with nearby traditional store formats.

Crucially IKEA were also able to identify comparative demographic groups attracted to different store formats, which consequently led to more relevant, personalised marketing and communications.

Read the full IKEA case story here

Read IKEA location analytics case study

In conclusion

All sales and marketing is ultimately about connecting people with products or services in space. Whether sat in their own homes online, or motivated to travel to a retail destination, it is paramount to understand what choices consumers make and to unearth insights into the drivers and motivations for their choices.

Consumer location analytics offers an unrivalled opportunity to observe anonymised real-life behaviours scaled up from individual data points, which can then inform strategic business decisions.

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Posted by Simon Taylor



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Consumer location analytics in retail