In an age when every bit and byte of consumer data is instantly captured, analyzed and acted upon, we’ve all grown used to being in the shallow end of the retargeting pool. We’re fed a steady diet of “relevant” ads based on our demographics, psychographics, browsing behavior, model and age of dishwasher, last book read, etc., wherever we may roam. OK fine.
My siblings and I, known collectively as Mom Inc., recently decided our elderly mother was badly in need of a new vacuum for her condominium. The battery powered wonder my brother purchased about three years ago was now holding a charge for maybe 10 minutes (or two rooms and a hallway).
So one of my sisters searched online and sent us all an offer from Sears for a canister vacuum on sale at $179.99 (regularly $229.99). We decided this worked for our purposes and I took the popular “buy online, pickup in store” route (BOPS or BOPUS, depending on your preference), selecting a store in a nearby town.
I was all set to pick up the unit over the weekend and offer my mom same-day delivery (and possibly actual vacuuming), when not five minutes later an ad from Sears appeared in the right rail of my Yahoo Mail account. Here was basically the same unit, in lime green (!) for $149.99 with free shipping! Fortunately having taken Econ 101 in college, I immediately canceled the first order and jumped on the better deal. With tax, it set us back just under $160, or $32 a sibling.
Even better, Sears over-delivered on shipping – the order, promised in two days, arrived within 24 hours.
The software that powers big data analytics is a beautiful thing for retail marketers. It allows them to deliver just-in-time messaging and offers with pinpoint accuracy, increasing conversions in a highly efficient, cost-effective manner.
But sometimes – fortunately for this consumer – the next-gen machinery that makes the magic happen can be too smart for its own good.