What the Jason Alexander hoodie ad teaches us about the past and future of spend analytics capabilities [PRO]

jason alexander Jason Alexander is known for "Seinfeld" — and now, being on a hoodie. (Adobe Stock)

One of our favorite Super Bowl advertisements this year was the Jason Alexander “hoodie” — a totally hilarious plug for Tide. Any true “Seinfeld” fan knows that you’re usually laughing at George Costanza, not with him, but somehow you feel sorry for the guy, at least part of the time, which is precisely why this commercial is so effective. While you want to see George get what he deserves most of the time, it just feels wrong to dishonor the short man or beat him up after a certain point, which is why the Super Bowl ad is such a work of genius — it doesn’t stop.

George is a bit like spend analytics in the broader source-to-pay world, at least some of the time. It’s a bit of a sideshow, even if it shouldn’t be. And sometimes, at least in the worst of cases, we have to laugh at it, not with it — like when it tells us we’re buying mice, not furry lab mice! But just like the hoodie, if we can laugh at it or at least acknowledge its flaws in terms of where analytics’ value generation typically comes up short, we will end up respecting the potential for it even more in the end.

Now, we’re not saying that a typical procurement organization cannot get value out of a spend analytics solution. Many provide material value to their clients, at least power users, and have for some time. Most users, in fact, do extract value for their investment in these tools. But like a Jason Alexander hoodie, until you just come to accept the technology for what it is, you might find yourself disrespecting what you’ve actually bought.

Said more directly, you’re likely to not get (nearly) as much value out of your analytics solution as you should, and not getting this value will cost you more than you realize — maybe even multiples of what you are paying for a solution that is supposed to identify, and help you prevent, overspending.

Here’s why.

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