Summary: New to Big By David Kidder
Summary: New to Big By David Kidder

Summary: New to Big By David Kidder

Discovering a big unmet customer need starts with shifting your focus away from planning mode to discovery mode. It can feel counterintuitive and awkward to shift focus from our core competencies to the customer’s unmet need, but it’s a shift that reorients our companies toward authentic growth and productive creativity.

The Discovery process can be simplified as follows:

  1. Assemble a small, designated team
  2. Pick a group of potential customers, listen, and observe
  3. Consider relevant new enablers that could serve their needs
  4. Understand the current and emerging business landscape, technology road map, and startup/venture ecosystem
  5. Combine all of these inputs to identify Opportunity Areas (OA)
  6. Consider sizing, timing, and fit of each OA to arrive at the prioritized OAs


Step #1. Assemble a small, designated team

Discovery work should be led by someone who has the ability to see beyond what the company is already doing and envision what it could be doing. This may be a contrarian, someone who is eternally playing devil’s advocate, or a person with a vivid imagination and the courage to pursue out-of-left-field ideas.

Typically, this role is filled by an executive who is obsessed about the future of the company.

The people working alongside her should be three or four folks who show a love of deep questioning and unbridled creativity It will likely include a financial analyst who can help with market sizing, perhaps someone from your corporate venture capital team who has an eye on startup trends and VC investment flows. You might consider a technology expert from R&D, a mad-scientist type who’s five years ahead of the technology curve, as well as a customer insights expert with ethnography skills.


Step #2. Pick a group of potential customers, listen, and observe

Discovery work starts with people, but not all people, of course. Our interest is in identifying pain points, but we never ask about them directly. Instead, we observe people in the wild.

For example, instead of asking subjects about their frustrations around a pain point like meal planning and grocery shopping, we look in their fridges and pantries, ask what they cooked for dinner each night this week, find out if they go to the nearby farmers’ market, and watch them pack lunches for their kids.

The point is we want to learn about their daily life and understand the values and beliefs that drive their behaviors.


Step #3. Consider relevant enablers

We want the Discovery teams to concentrate on technologies and business models that could plausibly be brought to bear on the problems of our target audience, but also remain fiercely imaginative about everything on the table.

For instance, if a fashion brand wants to deliver a personalized customer experience, the company might say, “AI couldn’t possibly help us with this. There’s no way we’re putting robots into customer-facing roles!” But AI encompasses things like speech recognition, sentiment analysis, and chat bots, which could have applications in e-commerce and in-store experiences. As we consider enablers, we want to keep our minds as open as possible so that we can see all the myriad possibilities.


Step #4. Understand the ecosystems

We take a few giant steps back from the work we’ve done so far and scope out all the relevant activity we can see. Considering this spread helps us form a thesis as to how the industry is changing and compile a macro view of the market landscape. By putting both players and events on the board and considering how they’ve interacted in recent months and years, we begin to see trajectories and patterns. We see what’s been done, what’s been overlooked, and what’s failed miserably.

For instance, we’ve seen over and over again that while companies tout 3-D printing as the future of personalization, it has not delivered mass personalization at scale. We see relevant trends in adjacent industries. We see ideas that should’ve succeeded but didn’t…and if we’re lucky, we see why.

This 50,000-foot view allows us to predict when world-changing enablers and shifts in behavior will collide, and to make sure we’re there when the collision occurs.


Step #5. Plot the Discovery Grid

The grid exercise is designed to help us get an even clearer picture of the overall ecosystem, and also shine a spotlight on untapped OAs. Here’s how we do it:

  • All potential enabling technologies are plotted on the x axis.
  • All issues that are top of mind for our target population are plotted on the y axis.
  • If a competitor company has addressed an issue from the y axis using an enabler from the x axis, that company’s name is written at their intersection.
  • If no one has addressed an issue from the y axis using an enabler from the x axis, the blank square at their intersection represents a nice, ripe OA.

So simple, yet so fundamentally effective. Having done your research before plotting this grid, you’ve looked into your competitors’ mistakes, you know how much money everyone is getting from investors, and you have a sense of how much revenue everyone’s raking in.


Step #6. Consider sizing, timing, and fit of each OA

We start with sizing, since we want to dispense with any OAs that don’t have the potential for massive impact and continued profit. The Discovery team does this by studying proxy markets, exploring how similar or related products are viewed, used, and consumed. This is simpler for some OAs than others, of course. Say we were interested in exploring self-driving cars. We have data galore on car-purchasing habits, and although autonomous vehicles are quite new, they’re close enough to traditional cars that we can mine existing data and make reasonable predictions. Drones, on the other hand, have few logical proxies. In the past, if you wanted an aerial photo, you needed a helicopter…but far more people can afford camera-equipped mass-market drones than to rent choppers for the afternoon.

Our first timing question is always “Are there any blockers out there that would make pursuing this OA right now substantially more difficult?” The most common blocker is governmental regulation, either in terms of laws that prevent something from happening (like those that prevented Amazon from implementing drone-based delivery) or laws that need to be in place to protect our proposed venture (like pending patents or stronger copyright enforcement).

Our second question is “Why now?” We assume that a dozen entrepreneurs exactly like us, or smarter than us, have tried similar ventures and failed. So if we’re going to chase that opportunity ourselves, we need to know how the world, the market, and our own capabilities have changed.

Finally, we take a look at fit: “Is this OA a good fit for the company’s core competencies, aligned with its mission, and in a space that appeals?” To be clear, our current strengths shouldn’t be our sole focus. We need to look beyond our core competencies when considering fit. To truly innovate, we must see beyond what we do well now, and imagine what we could do well in new spaces with the same skill set. Or imagine how building new core competencies could complement our existing ones.