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"PiinPoint has become an integral part of my role as Retail Analyst at Cushman & Wakefield Waterloo Region. The platform allows me to put together professional looking reports and provide clients with the insights they need to make real estate decisions.

I honestly don’t know how I would do my job effectively without PiinPoint."

Jessica McCabe, M.Ed.
Retail Analyst

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3 Ways Location Intelligence is Coming of Age



September 24, 2021

Location planning historically has had a tendency to confirm organizational biases, rather than inform an overall strategy or direction. The future is to try and remove that bias and build a location intelligence and analytics capability to guide the real estate and store planning teams to strategic decisions where the human biases are minimized. Humans will still make the decisions of course, but now guided by data, analytics, and even AI technologies that are trusted and transparent. 


Make no mistake, this is not easy. Even the starting point for retailers - leveraging their own data from Point-of-Sale systems or developed loyalty programs - can prove to be difficult. Many organizations struggle to centralize their own datasets so they can rely on a single source-of-truth regarding location information and performance. However, the changes over the last year and an accelerated demand for analytics has pushed many brands into deeper investment for becoming more data-driven. Rather than taking guesses, using advanced customer analytics gives them insight on preferences and drives better uptake and loyalty. But that's only part of the equation. 

The real power is integrating internal with external datasets on market performance, competitive insight, the local retail landscape, consumer mobile shopping patterns, online/eComm penetration, demographic trends, and even psychographic behaviours through robust segmentation systems all anchored to the “location dimension”.  This arms the Retail Analytics specialist with a fuller picture of the key drivers of network success and to imagine, predict, and plan for change.

Location analytics are not analytics you provide to retail. They are retail analytics with a location dimension.  


Take Genghis Grill (New virtual brand Stir Fry Chef) for example, who, among many others, are doing incredibly innovative things in the fast-casual restaurant space as a result of the changing consumer preferences for online ordering and contactless delivery coming out of the pandemic. Stir Fry Chef meals are cooked in the Genghis Grill Kitchens, but serve a different customer segment, thereby expanding their penetration into the trade area around the store and increasing the productivity of the Franchise and the earnings to the Franchisee! 

Clearly, inventing these new experiences are based on deep customer knowledge and an overarching desire to intentionally make each concept part of that choice experience.

Network growth for these brands is not just about finding good trade areas for the brick & mortar brand, but now they need to account for the added complexity of understanding the demand for the virtual brand as well. The delivery reach and the match to the consumers in the trade area around the kitchen will be critical. The data sources don't change much, but the predictive models that define a high-performing kitchen delivering multiple brands of food, does! 


Big changes are happening in retail as well, but the core themes are that real estate analytics specialists who are willing to break the mould and work cross-departmentally with store operations, menu or assortment design, and even human resource planning, are finding new ways to drive value for their organizations. The old assumption that if you build it in the right location, the customers will come, cannot remain a simple key tenet for real estate anymore.

Many brands at the forefront of change are demonstrating how real estate is more than just securing the dirt.

As the physical manifestation of a retailer becomes more about experience and less about the four walls of a store, real estate will be performing a different function for the organization. As stewards of location data, they are the plug-in to understanding micro-market dynamics for upstream operations.

Check out PiinPoint Marketmatch and our Keto Cafe case study and find out how we have helped other Restaurant brands improve their real estate planning by providing data-driven tools that de-risk the location choice!

Learn more about how PiinPoint can help your business evolve and thrive.