Before we begin, I’d like to take some time to help you understand that there is no perfect method. To that end, I’m going to share with you some things to avoid in the minefield of Jobs-to-be-Done research. I expect each of you to experiment and learn yourselves because what I share is not 100% absolute.
Navigating Beyond the Constraints of Perfection
One of the things I’ve learned once the fanboydom of Outcome-Driven Innovation wore off is that Executives are hiring growth and innovation strategy consultants for one functional reason …
They have Jobs to get Done
That sounds cute. But they are responsible not only for making decisions that result in a competitive advantage, they’ve already invested in internal sources to execute.
The problem is that they don’t know what to execute on, and they most likely have a record of stutter starts, just like everyone else.
They want to know what to do now!
They can no longer tolerate guesswork.
When you tell them you’ll have some targeting information and a basic strategy in 4-6 months, that no longer flies. That is a failed innovation method.
To compound this problem, the end result (a long-assed PowerPoint deck) is impossible to interpret and has numerous questionable assumptions. So what do they do?
They go out and hire the JTBD boutique firm that says they can be done in 2 weeks! What are the pros and cons of that?
Pros
- Done in weeks
- It’s simple and easy to digest
- Cheap
- Lots of emotional appeal
Cons
- The work product is sticky notes and maybe some emotional videos
- Focused on the job of their product, not the job of the consumer / customer - so it’s the flipside of a journey (the corporate pathway)
- Only high-level actionable insights based on a small sample of people - the insights make you say “No duh!”
- All that's identified is a set of jobs to be done
- You just wasted a few thousands bucks and 2 weeks
What if you could get simple and quick and powerful?
Most people don’t want to be data scientists, yet there are those that tell them …
- you must learn a new language of innovation
- you must trust the algorithm
- you must trust us, we’ve been doing it this way for 30 years
Here’s the problem with doing something the same way for 30 years and ignoring signals to the contrary:
Maria had always believed that Mount Everest was the tallest point on Earth, a fact ingrained in her mind since childhood science classes and nature documentaries. One lazy Sunday afternoon, however, while scrolling through an article on geological discoveries, she stumbled upon a startling revelation: the tallest mountain from base to peak was actually Mauna Kea in Hawaii, standing taller than Everest if measured from its base on the ocean floor. Her eyes widened as the realization sank in, and she felt a disorienting mix of disbelief and betrayal. It was as if a pillar of her elementary education had crumbled away, exposing how easily widely accepted 'facts' could be misleading or oversimplified. The idea that something so widely accepted and rarely questioned could be flawed left her feeling unsettled and more skeptical of other seemingly unassailable truths.
I’ll just come out and say …
That’s right, the surveys we put in the field capture ordinal data.
Executive stakeholders have just enough time to ask…
Executive: “What percentage of the market have those top 3 ranked needs, and how many dollars of opportunity does that represent?”
Consultant: “Let me explain our proprietary algorithm”
The pursuit of perfection often leads one into an abyss when their customer simply wants to wade in a tidal pool for a few minutes.
There’s a reason I believe in ‘ranking’ metrics and associated models for segmentation that honor ordinal data vs. interval data (which we don’t capture). It deserves a whitepaper. I’m not the one to write it though.
I’ll put a placeholder here for anything that wants to contribute!
Prioritizing Needs
Executives that invest in innovation research generally want to know what they can do with all of the resources at their disposal to create new value in the market … so they can grow their business.
And they want to know it now
This isn’t rocket science. Giving them anything that is short of a prioritized list of actionable, value-creating initiatives is a waste of their time and money.
Yet, they continually make these investments, find cheaper fake JTBD consultancies … or they just guess.
Is one better than the other?
While I’m going to get into something a little more sophisticated down below (just for fun!), the simple ranking of customer success metrics, steps, or anything that points to a value-creating path forward should not be laughed at. It doesn’t sound as sophisticated as statistical needs-based segmentation but it can be powerful, and it’s extremely accurate.
Here’s a very basic approach to this that I hope will help you out.
Predictive Metrics
I have a whole section coming on this. It will blow your mind!
