Location analytics in retail

Whilst much of the volatility surrounding consumer behaviour in the last two years was forced by government rules associated with the global pandemic, personal choice has also shifted seismically once restrictions relaxed. 

Not only have customers quickly adapted 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 don’t 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? 90% of companies believe that location data is crucial to unlocking the answers to increase their success.

Location analytics is the process of using data generated by actual customer interactions with a retailer’s physical spaces to gain insights into consumer behaviour, preferences, and purchase patterns.

Tracking actual consumer movement over time with location analytics involves using permissioned data from consumers’ mobile devices or other location-tracking technology to understand how they move through physical spaces such as stores, malls, or entire cities. This type of analysis can provide valuable insights into consumer behaviour, including how frequently they visit certain locations, how long they stay there, and what routes they take.

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 3 metres, 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 the 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.

Additionally retailers see how customers move through stores and interact with products, which can be used to optimise store layouts, improve product placement, and enhance the overall shopping experience.

Here are the common steps involved in tracking actual consumer movement over time with location analytics:

1. Collecting location data: to begin tracking consumer movement, data is collected from a source such as GPS-enabled smartphones or location beacons installed in physical spaces. This data can include latitude and longitude coordinates, time stamps, and other contextual information.
2. Processing and analysing the data: once collected, data must be processed and analysed to extract meaningful insights. This may involve cleaning and normalising the data, identifying patterns and trends, and using statistical techniques to uncover correlations or causations. Blending this data with other data sources also enables greater understanding of customer behaviour between physical and digital spaces.
3. Visualising the data: after analysing the data, visualisations are created to help interpret the findings. This may include heat maps, flow maps, or other visual representations of consumer movement and behaviour.

Location analytics can bring benefits to every element of retail store strategy, including:

The valuable insights provided through location analytics can be used to inform strategic decision-making in any business that has physical locations. 

• Market share growth: Location analytics can not only show customer behaviour in and around your own stores, but can also unlock insights into which of your direct competitors they visit and where else they shop. From this you can also identify which customers only shop with your competitors and not with you. Once you know this, you can deploy tactics that target them directly to gain a competitive advantage and grow market share.
• New locations: When it comes to selecting new store locations, retailers can utilise location analytics to assess the potential market demand and customer demographics in different areas. By analysing data such as population density, income levels, and competitors’ locations, retailers can identify prime locations with high growth potential. 
• Optimising store performance: Additionally, location analytics can assist in optimising store networks by identifying underperforming locations, optimising store layouts, and re-evaluating the distribution of products and services. By harnessing the power of location analytics in retail development, businesses can make informed decisions that lead to successful expansions, improved customer accessibility, and enhanced profitability.

When working through a location analytics project, various types of data can be taken into consideration to make informed decisions. For example, our Location Optimiser product uses fully permissioned data from consumers’ mobile devices to understand more about their interactions with stores, this data can then be overlaid with many other sources to provide insights into the market potential, customer behaviour, and competitive landscape of specific locations. Here are some of the key data sources that are likely to be considered considered:

• Demographic data: This includes information about the population residing in a particular area, such as age, gender, income levels, education, and household size. This data helps retailers understand their target customer base and determine if a specific location aligns with their target market.
• Socioeconomic data: This provides insights into the economic conditions and purchasing power of the population in a given area. Factors like average income, employment rates, and consumer spending patterns can help retailers gauge the potential demand for their products or services.
• Competitor analysis: Analysing the presence and performance of competitors is crucial. This data includes the location, size, market share, and offerings of competing businesses in the target area. Understanding the competitive landscape helps retailers identify gaps in the market and differentiate their offerings.
• Foot traffic and dwell time: Collecting data on foot traffic and dwell time within specific areas is essential to evaluate the potential customer flow in different locations. This information helps retailers understand the level of customer engagement and can influence decisions on store placement, layout, and staffing requirements.
• Consumer behaviour data: This provides insights into customer preferences, purchasing patterns, and shopping habits. This data can be collected through loyalty programmes, transaction records, online surveys, or mobile apps. Understanding consumer behaviour helps retailers tailor their offerings and marketing strategies to meet customer expectations.
• Market trends and insights: Analysing market trends, such as emerging neighbourhoods, changing demographics, or shifts in consumer preferences, helps retailers stay ahead of the curve. This data helps identify growth opportunities and anticipate potential challenges in specific locations.
• Sales data: Retailers can analyse their own historical sales and performance data to identify successful store models and understand factors that contribute to profitability. This data can be used to develop benchmarks and compare potential locations against past performance.
• Geospatial data: Geospatial data, including maps, satellite imagery, and geolocation information, provides a visual representation of the physical environment. It helps retailers analyse factors like transportation infrastructure, proximity to residential areas, visibility, and accessibility of potential locations.

By leveraging these diverse data sources, retailers can conduct comprehensive location analytics to evaluate the suitability and potential of specific areas for retail development. This data-driven approach allows retailers to make informed decisions, minimise risks, and maximise the chances of success when expanding their business or opening new stores, or implementing new strategies.

Of course, the quality and depth of the data analysed will depend on the analytics provider used. For example, Ikano Insight’s services provide a significantly higher level of data analysis. To highlight this, the case study for IKEA in Singapore goes beyond the general data sources mentioned above to also provide insights on:

• Audience visitation patterns, motivations and outcomes
• Taking into account existing loyalty scheme data
• Overlap with existing stores
• Where are customers visiting from? (Their journey routes to and from stores)
  • Customer motivations
• Tracking competitor visitation

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 store assistants or online which 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.

All sales and marketing is ultimately about connecting people with products or services in space. Whether sitting 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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