Location intelligence is how we describe the process of analysing data about a location to make impactful, strategic business decisions. But it’s not some sort of obscure, niche skill that only big corporations with big budgets can afford to invest in. It’s also the layperson looking at a shop and wondering if there’s enough foot traffic in the area to support their business. It’s the hopeful business owner looking to open a car detailing business and wondering if there are enough households with cars in the neighbourhood to even justify committing to the idea. It’s wanting to know if there are too many competing businesses close by, or if there’s an actual, noticeable gap in the market. Location intelligence is more than just a cursory consideration: oftentimes, it’s the core question that needs to be answered before signing that lease.
Let’s explore some of the different ways location intelligence can be factored into your decision-making, particularly when it comes to big-picture decisions, such as where to set up your first shop. We’ll work off dummy data in these examples:
- Idea: Laundromat
- Competitors: 2
- Typical opening hours: 9am – 5pm
- Median age: 33
Nearby competitors
The most basic level. One single layer of data which can influence where you set up your business. If you want to open a laundromat, you wouldn’t want to open up too close to existing laundromats. Instead, you’re better off finding a geographical gap in the market and establishing a laundromat in that gap instead. Simple, and straightforward.
Competitors + opening hours
Now we have two layers of data, and this gives us some more targeted information. At first, we simply ruled out locations with competitors nearby. Now we can see that, of the two competitors nearby, both of them close at 5pm. Suddenly, something that was completely ruled out now becomes an opportunity: an after hours laundromat could still perform well there, because it’s a gap that hasn’t been addressed by existing businesses despite their presence.
Competitors + opening hours + median age
With three layers of data applied one on top of the other, we get a very granular, very interesting snapshot of our example location. We see that the median age of the population in our example area is 33: in other words, people in the early-middle part of their career. These people likely work full time, which means doing chores during business hours is simply not an option for them. Looking back at our competitors, we can now infer that none of them adequately serve the needs of their target market. If they’re only open 9 – 5, then how can someone who works full time possibly make it in during the week to get their washing done? That means they have to defer their washing until the weekend, resulting in unavailable machines, queues, and all the frustrations they come with.
Now, instead of our business idea being a simple laundromat, thanks to our application of location intelligence we know that an after hours laundromat, specialising in managing weekend and late night visits has a solid chance of performing well. Maybe add in a caffeine-stocked vending machine or two to really help out those sleep-deprived young professionals (or young families!)