The lie of the land: Using Big Data to optimise location planning

Selecting and setting up in the right location has always been one of the most important decisions a ‘store’ brand can make. Getting this fundamental decision wrong, by opening in a sub-optimal location which fails to meet the needs of customers, can be a very expensive and high-profile mistake. That is why location planning is crucial.

The history of retail is littered with examples of brands that failed to master their geographical footprint, blindly over-extending chains, or missing market opportunities, through a lack of market insight. Knowing your market is the foundation of a successful business – just as in military strategy, if you can’t master your geography, then all else will fail.

The science and art of location planning remains vitally important to the success of many businesses. A long-established form of customer analytics, it provides the means to understand your market, know where your customers are, balance the risks of competition, and proactively optimise property, marketing and operational budgets.

However, like many areas of business, new forms of data are opening opportunities for deeper and faster insights and ultimately, better location decisions. We can explore this evolution in location planning through a Good, Better, Best scenario.

Good (for a while, at least) – instinct

You are in the process of finding a new store location for your company and want to locate several options for a new site. To do so, you use a mixture of property knowledge, possibly from a trusted agent, and your own instinct to assess the potential of a location. You might assess the local areas, look at footfall, competition, complementary stores, and get a general feel for ‘supply and demand.’

Your friendly agent may even have pulled some basic market data together for you, from one of the many ‘off-the-shelf’ sources, using simple static consumer demographics. This helps compare your candidate locations, although little more than that.

Are these good locations? Which is the best one? Which will offer your greatest return? You balance the pros and cons and select one. This is how retail locations have been selected in the past, and some brands still take this ‘hunch-driven’ approach to location planning. It can work in the short-term but will fail as soon as you run out of obvious locations, and new opportunities, as they always do, will become ever more marginal.

Better – customer-first prioritisation

You realise that to avoid costly mistakes, you need to take a more analytical approach. This method prioritises understanding the customer as the key to finding the right locations. Many brands, especially large retailers, operate at this level.

You decide you need to optimise your location decision making, to ensure you only invest in places likely to deliver the greatest return. This requires a clearer understanding of your market, who your customers are, where they live, and how they behave.

You start to map and predict your potential store catchment areas and produce a clearer view of the ‘size of the customer prize’ offered by new locations. Much of the insight needed to do this is either found in your own transactional data or through location datasets, such as geodemographic segmentations, available from a range of data suppliers.

This approach allows you to better prioritise opportunities. A better understanding of the type of people in your store catchments also has secondary benefits, such as optimising store ranging and guiding local marketing initiatives.

This is certainly a better approach. However, your decisions will be based on static and often out-of-date data, and you are likely to be working with the same ‘off-the-shelf’ geodemographic insights that your competitors are also using.

Best – dynamic customer decision making

The emergence of big data is changing the game for customer analytics in general, not least the venerable practice of location planning. Innovations in location data offer significant potential to greatly improve the accuracy, relevance and timeliness of market initiatives.

You now enhance your location decision-making through a dynamic understanding of consumer lifestyles: this is a capability that allows you to see how your customers’ passions, interests and influences are changing over time. Starcount’s Audience data*, derived from big social sources, and mapped to postcode level, for example, provide brands with an unprecedented level of insight needed to take fast location decisions – whether new stores, pop-ups, events or local target marketing. This kind of dynamic customer location data is reducing the risk associated with geographical decisions

The future

Of course, with new technology and evolving data sets, you will soon be able to go much further than this in location planning. Mobile location data will allow you to understand where people are concentrated most at a specific time of the day. Transport data will allow you to understand the flows of consumers, whilst private data from apps like Strava can help to pinpoint how consumers go about their daily lives.

But, again, this only reveals certain details of who your customers are. Enriching these data sets with social data will give you the ability to know not only where these people are and how they go about their daily routine, but also what motivates them, what they are passionate about and how they will interact with your company.

That is perfection.

*To learn more about Starcount Audiences, please get in touch here.

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