I have a use case for this. A good one on why AI is the only future for site selection. Today there is infinite data, which is good, but it is also bad because its infinite data. It would take an expert the rest of their life to sift through and glean insights from the data ... such as the one I chat about here.
Two eye lash extension franchise units are successful. Doing well. Nothing wrong, so nothing pops up in anyone’s head, not from the team nor the analytics. In the meantime the AI is churning in the background and comes up with an insight, a problem, but poses a question to both units. “How many beds do you have?” The one on the southwest side goes, “I have 6.” The one on the northeast side says, “I have 8.”
The AI responds, “found it.”
The unknown problem detected was customers we’re migrating from the southwest area’s unit to the larger northwest unit on the trade area’s fringe. Nothing accounted for it. Not the drive time, competive landscape, ampliphiers, suppressors, nothing ... so its an anomaly, its timely and it’s live. That is where the AI shines and trumps an expert. It looks for the oddities that go missing.
Why did it ask about the beds? What it saw was the anomaly and taking into account everything else, only the merchandising (for lash extensions the “beds” are one of the main products) was unknown. It looked at the POS system and the scheduling system and saw that bookings were packed weeks in advance for the southeast store, primary culprit was too few beds and for eye lash extensions, that is too long. The wedding is this weekend. That big opera is Saturday, so people farther out gave up and took the extra drive for faster turn around.
So it suggested increasing the bedding by 4 to meet demand but more importantly, it stated if they do not do this, they may be out of business within 9 months.
Why? Because the area so ripe for picking. Good old supply and demand. If a competitor was parked across the street from the southeast store and saw the volume, smiled, and opened a 10 bed unit before the existing store took notice, it would spell the end.
Now let’s say we didn’t have the AI, what would have happened a year from now? The southeast store gets a competitor, it scrambles, tries to regain and retain customers. Eventually the competitor opens a second store ... we know how this story ends. But what did the retailer say: “Couldn’t be helped, we didn’t see it coming.” And that is where AI is the game changer in site selection.
I love this! I know the article is old, but it's actually more important today then when it was written. Now I'm a little biased being in GeoSpatial Artificial Intelligence and in this subject, but it's smack on point for the complexity ... and the "finesse." Crazy complicated stuff, but it is no longer the future, it is now ... today.
And solutions are needed because you can hire 100 McKinsey analysts to pull all this together ... and they'll have it in 6 months, when it's already too late and the competition has zipped past you.
I believe all the solutions and stories that other writers have entered here are valid and on point, but wow would I like to hear what they have to say today.
To me this is "Demand Forecasting and Planning + Unified Commerce." Okay, my head's spinning just from typing that, but that is the holy grail (for this year at least). Or so I think. But love it.
Thank you Tom for the write.