Key Insights

• The strongest location intelligence examples put performance in context. A site’s sales mean far more when compared with its expected performance, local demand and role within the wider network.
• Start with the decision, not the analysis. Whether the question is where to open, close or relocate, the insight should make the consequences of each option easy to understand.
• A good business location analysis example makes the trade-off visible. Showing how a new site could increase coverage while affecting an existing location gives decision-makers something concrete to weigh up.
• Visuals should simplify the finding, not reproduce the model. Maps, scenario outputs and metrics such as footfall data work best when they make the opportunity or network impact immediately clear.

When good analysis gets lost in translation

A retailer has one store that’s been underperforming for years, and nobody can quite work out why. On paper it looks like a decent location: reasonable footfall, a healthy catchment and no obvious red flags. Then the analysis runs, and a pattern that nobody had put into words before suddenly clicks into place.

That moment when a long-standing puzzle finally makes sense is what good location and customer insight should deliver. But there's a gap between finding it and turning that insight into a decision.

The people in the room when that decision gets made - senior leaders, local teams, whoever's signing off the budget - usually aren’t analysts, and if the insight doesn't survive translation, it doesn't survive at all. 

So once you know what the analysis is telling you, how do you turn it into something a wider team can understand, act on and use to make a decision? That's what we're exploring here: how to make location insight useful beyond the people who produced it, with location intelligence examples of what tends to work in the room.

What the insight needs to survive contact with a room

Not every finding is equally useful to say out loud. The location intelligence examples that consistently land with non-specialist audiences tend to be the ones that are simple to picture:

• Whether a site is performing better or worse than expected, rather than just its raw sales figure. A site selling 100 units can still be underperforming if the model says it should be selling 150.
• Whether a site is performing against its local market potential, rather than simply comparing it with the rest of the network. A site might have lower sales than other locations but still be performing strongly for the level of demand available in its catchment.
• What you’d actually lose if a site closed, rather than what it currently earns. Some lower-performing sites play an important role in the wider network by serving customers that other locations can’t easily reach. We’ve explored this in why your ‘worst’ sites might be the most valuable.
• What a site does for the business beyond its own numbers, whether that’s brand visibility, service revenue or online sales in the surrounding area.

The trick is to make the finding tangible. “This site has low sales” doesn’t tell a room very much. “This site has low sales, but closing it would leave a large part of its customer base without a nearby alternative” does. The second gives people something they can immediately understand and use in a decision.

Bring the different pieces together

Location and customer data become more useful when the different parts are considered together. Site performance might tell you where something is happening, while customer and market data can help explain why.

A site could be underperforming against the rest of the estate, for example, but that finding means something different if it sits in a market with limited demand. Likewise, a location with strong local demand might look like an obvious growth opportunity until you factor in the existing network and how much of that demand is already being served.

This is where combining different location intelligence techniques can add real value. The strongest recommendations usually come from looking at the network, the market and the customer together, rather than treating each analysis as a separate finding.

How GMAP presents its findings

The way you present an insight can be just as important as the insight itself. We tend to structure findings around five simple questions:

• 
  Where is the demand?
• Where does the network fall short?
• What could change?
• What would that change be worth?
• What should happen first?

It gives the analysis a natural flow: here’s the situation, here’s the gap, here are the options, here’s the value, and here’s where to start. You don’t need to understand the model to follow the logic.

The same order works for most location intelligence examples we present, whatever the sector, and it also helps that the output doesn’t stop at a report or presentation. Our tools let teams explore the findings after the initial analysis: MVPLUS shows the network as it stands today, while IMPACT lets teams test different scenarios and see what happens when the network changes. That gives people something to work with, rather than leaving them with a slide deck and a set of notes.

The clearest way to explain a finding, though, is often through the trade-off it creates. A good business location analysis example of this is a network where the data points towards a new site in a particular town, but there’s already a profitable location nearby. Opening the new site could extend the network’s reach, but it could also affect sales at the existing location. 

That gives the decision-makers a much more useful question: is the additional coverage worth the impact on the site we already have? 

That’s a conversation a board can have without needing to understand the modelling behind it. The data provides the evidence; the decision still belongs to the people running the business.

The audience matters too. A manufacturer might want to start with the overall shape of the network and where it needs to go next. A dealer group may want to get straight into which sites need to close, move or open. The underlying analysis stays the same; but the way you present it needs to reflect what that audience actually needs to decide.

Making the insight stick

The best analysis in the world won’t change a decision if nobody knows what to do with it. A few simple things can make the difference between an insight that gets discussed and one that actually gets used.

Start with the decision

Start with what the business needs to decide, then work back to the analysis that can help answer it. If the question is whether to close a site, for example, the useful starting point is what that site contributes, what happens to its customers if it closes and how the wider network would respond.

The same applies to growth decisions. If the question is where to open next, start with the opportunity: where is there enough demand, where does the current network fall short and what could a new site add?

Give the finding some context

A number rarely tells the full story. £5m in sales sounds impressive until you know the site was expected to generate £8m. Equally, a site generating £2m might look weak against the rest of the estate but be performing strongly for the market around it.

That’s why we look at site performance alongside the market around it. RetailVision gives retailers a view of the wider retail landscape, including things like footfall, competition and estimated revenue, helping put individual locations into context.

The same applies in leisure. LeisureVision helps businesses understand the market around a location, from the existing leisure offer to the factors that influence demand. That context can change how you interpret the performance or potential of a site.

Make the trade-off visible

Location decisions often involve a trade-off. Opening a new site could improve coverage while affecting an existing location. Closing a weaker site could reduce costs while creating a gap elsewhere in the network.

Showing those consequences clearly can make a complex recommendation much easier to understand. A simple before-and-after view of the network, for example, can show what changes when a site opens, closes or moves - giving people a real sense of the impact before they make the decision.

Make the visual do some work

Maps, charts and scenario outputs can quickly become overwhelming when they’re trying to show everything the analysis contains. Make sure each visual is doing something useful. If you’re showing a network gap, make the gap obvious. If you’re showing the impact of a proposed site, show what changes as a result.

And don’t assume everyone knows the terminology - even a stakeholder could ask, “What is footfall data?” If a metric matters, explain it before relying on it.

Show where the recommendation comes from

A recommendation carries more weight when people can see the evidence behind it. If the analysis points towards closing a site, for example, show the factors that led you there and how the wider network would respond.

This gives people a way to follow the logic without getting into the technical detail of the model. They can see what is driving the recommendation and understand how the different pieces of analysis fit together.

For a location decision, that might mean bringing together site performance, local demand and network coverage to show why a particular course of action makes sense.

Turning insight into action

A recommendation carries more weight when people can see the evidence behind it. If the analysis points towards closing a site, for example, show the factors that led you there and how the wider network would respond.

This gives people a way to follow the logic without getting into the technical detail of the model. They can see what is driving the recommendation and understand how the different pieces of analysis fit together.

For a location decision, that might mean bringing together site performance, local demand and network coverage to show why a particular course of action makes sense.