September 11, 2018
Stack the chips in your favor with the Physics of ROI
By: Ryan Garner

Here’s a dirty little secret: Four and a half years ago when we launched Clearhead (before we led Experience Design & Optimization for Accenture Interactive), we were a solution-led company—not a problem solving company.

We pitched ideas. We pitched hypotheses. We sent out newsletters like “10 Test Ideas Guaranteed to Boost Your Bottom Line”. You know the type I’m talking about—we’ve all received them at some point.

We saw some success with this solution-led approach…but then we started running into some challenges:

  1. Too many inconclusive results

  2. Limited resources being invested in ideas that didn't yield ROI (return on investment)

  3. Running into dead ends

We were successful. We were growing. But we were spending limited resources on ideas that didn’t yield ROI. We knew we could do better for ourselves and our clients.

In order to better understand where we were falling short, we asked some big questions as we did some self-reflection:

  • Why do ideas fail?

  • Why do they succeed?

  • Why are results inconclusive?

  • How do we improve the odds of generating ROI?

We looked at internal resources including a catalog of every experiment we’ve ever run—winners, losers, inconclusive. We dug deep looking for commonalities and correlations in our test result history.

Then we looked externally—business research about epic business failures: New Coke, Segway, Wow Chips). Then we looked at epic successes.

Along the way we came across a quote from Albert Einstein that really resonated with us (at least it’s thought to be from Albert Einstein, though it’s apparently the subject of some debate):

“If I had one hour to save the world, I would spend fifty-five minutes defining the problem…and only five minutes finding the solution.”

Einstein’s quote really flipped a switch for us. It helped us realize that the problem is THE thing.

We hadn’t been spending enough time thinking about what problem we were trying to solve, understanding them, sizing them, getting to their roots. And then—only then—starting to look for solutions. Rather, like so many others we were diving into solutions first and seeing what stuck. We knew there had to be a better way—and we finally found it.

While beginning with problems before diving into solutions is a supremely simple concept, we believe it’s often overlooked. Sure, everyone talks about solving problems, but when you get into that room for the brainstorm, notice how quickly the conversation shifts to solutions—pet projects from execs or a marketing initiative, something a competitor is doing, software from a hot new vendor.

Guilty! Not only did we do this in the early days of Clearhead (now part of Accenture Interactive), but at every organization I’ve ever been a part of including JetBlue and Warner Music.

But with our new approach, we began seeing more successful outcomes repeatedly—so much so that we began to think of this problems-first approach not as a hypothesis, but as a law. This was no longer a guess—it was tried and true physics. Thus, we now lovingly refer to our revelation as the Physics of ROI.

The Physics of ROI

Returns go up when the volume of problems goes down.

Returns go down when volume of problems go up (i.e.—you break more stuff than you fix).

Returns are flat when the volume of problems stay flat.

This last point, by the way, is the definition of inconclusive tests. They neither solve problems or create new ones. You know, that button color test, that footer test, the test where you moved the link 10 pixels. They are all inconclusive because they didn’t solve or create problems.

In other words, returns and problems are negatively correlated. You only get a return if your stack of problems gets smaller. If you integrate a hot, new piece of software just because it’s hot and new, there’s a chance that not only won’t it solve YOUR problems, it just might make them worse.

Companies need to identify their problems most worth solving first and only then start exploring solutions that could potentially solve those problems.

Our revamped approach

With the Physics of ROI as our guiding light, we went about reorienting everything we did to spend more time examining problems before diving into solutions. We developed a Cheat Code to hold us accountable.

Our Cheat Code:

  1. Love your problems. They are your road map. They show you the way to returns.

  2. Find them. Get to their roots. Size and prioritize them.

  3. Then focus your investments on solving the problems most worth solving.

  4. Take the volume of problems down and ROI goes up.

We believe this is card counting for the optimization casino and exactly how we came to our Problem Solution Mapping (PSM) methodology. We deeply believe every company that’s doing digital product development should tactically organize itself around the problems it needs to solve. Seriously—spend a significant portion of your budgets and time on this activity.

Products don’t take hold in a marketplace, metrics don’t go up when you launch, unless you reduce the number of problems that stand in the way of you and your users’ goals.

Every goal. Every problem.

Launching a feature or product, or even buying shiny new martech? If your investment doesn’t solve any problems, the metrics will stay flat. Even worse, what if it ends up creating entirely new problems? You’ve already made the investment, and now you’re draining revenue.

My advice to you is this: If you want to see positive ROI, orient all of your activity, investments and work around solving your most important problems. Stack the chips in your favor by leveraging the Physics of ROI.

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