Location, Location, Location
Updated: Jan 19
The science of launching retail locations has evolved through the years. The first location I ever launched was merely a best-guess, and how much is rent. As my understanding of target markets evolved, so did my understanding of big data and how technology can help REDUCE the risk of failure.
The operative words are: “reduce risk”. Nobody has a crystal ball and if somebody claims to have one, run away as fast as you can. I’ve launched hundreds of stores and “certainty” is an elusive concept. I’ll cover some basic steps I’ve taken to help me filter locations, and ultimately reduce risk.
1. Target and Strategy: This seems like basic Marketing 101 but it’s worth visiting. Have a long look at your business model, products, Customer Avatar and decide on a hard target based on common demographics: age, sex, ethnicity, income, etc. It’s not good enough to say “everybody”. This info will matter not just for location strategy but it will also dictate your marketing and merchandising going forward.
2. Engage Geo Mapping. Sometimes called Geo Information Systems (GIS). There are a lot of Geo Mapping businesses out there, some are self-serve tools and others on a managed services agreement. Have a look at Batch Geo, and Easy Map Maker. I’ve used GIS to create a visual map of where my target customers are, AND how densely populated the target is. A good example of GIS at work is the John Hopkins Covid-19 Map (see below). It’s a clear visual representation of Covid-19 cases, and density clusters. If you wanted to know where the best places are to catch the Corona Virus, just look at the map. For our purposes, the first thing a Geo Mapper will do is obtain data from Statistics Canada and create a map of where your target customers are located.
3. Location considerations. Now that you know precisely where your customers are located and how many, it’s time to develop a list of locations that address your target. But, it’s not that easy. A few things I’ve learned over the years:
Some locations may show poor target density but swells 3-4 times during business hours. Downtown is an example.
Malls can show poor density in the immediate vicinity but can draw customers from 20 km away; but will have the highest rent.
Street Front locations can only draw customers from less than 5km, but can have the lowest rent.
Strip plaza locations can draw customers from 5-10 km away
Transit patterns are a consideration based on demographics (if applicable)
Complimentary businesses are good. Don’t be shy locating near competition unless the density can’t support it.
4. Forecast. As the saying goes among realtors: “there are no stupid land lords out there”. Landlords know what their properties are worth and will charge appropriate rent - sometimes more. Mall stores are open 7 days a week and labor costs are the highest line item on any PNL, just ahead of rent cost (not to mention malls have crazy design requirements). Strip Plazas will have the next expensive rent but will have lower traffic and less sales volume.
You can make all this as easy, or as complicated, as you want to make it but the basics are all there. Whatever you decide to do, make sure your plan is based on hard data and avoid observational data. It’s all about reducing your risk.