Insights, Innovation and Intuition
For people who are more familiar with stark differences and quantitative answers, this sea of ambiguity is very difficult to confront. Faced with hazy information, inferences, wants and needs, they are quick to try to identify the "best" and most certain data in the mix, rather than find the most important trends or currents that all the research suggests. We've managed over the last 30 years to train people to be very decisive when the data are clear, and to be very hesitant when the data aren't clear or definitive. The most common refrain is: "how can you be certain"? In innovation, the fact is that you often can't. If the data were evident and certain, the data would be evident and certain to everyone, and a solution wouldn't be radical or disruptive because everyone would be building it.
In response to this uncertainty, two activities or frameworks emerge in many projects to lower uncertainty and attempt to provide more insight or rigor to analyze quantitative data. The first is evident from the discussion above: bring me more data, and more quantitative data that we can "prove". The second activity, and one that has good purposes often misused, is quantitative testing of ideas. I'd like to spend a few minutes talking about the strengths and gaps of testing needs.
Many innovation advocates will follow a prescribed innovation process, where they gather research or insights, then winnow those insights or needs into a structured, ranked list of needs. But to ensure that they aren't introducing bias to the list, they will test the needs with customers and consumers. This action also often occurs after ideas are generated as well. The goal is to get customer feedback and ranking of the importance of ideas. A noble goal, but a team must proceed very carefully.
Customers can rank needs based on the context you provide. Are you interested in needs that are unmet now, and that customers believe you can solve now, or needs that are unmet that customers wish you could solve, but aren't sure the technologies or business models exist? Customers, when they rank needs or ideas, will often use a filter that is more rigid and more incremental than an innovation team will use. This is because they expect any company to create incremental solutions and they base their decision making on existing products or solutions.
Insights, Intuition and Innovation
Managers and executives LOVE surveys of customers. They want to know what customers want, and their reaction to needs and ideas. But the risk is that the customers respond too narrowly, answering only immediate needs. They answer based on what they believe a company can or will develop, rather than based on what's possible. Anyone using only customer feedback to identify needs or ideas is probably missing the point.
A well-run innovation program considers trends, customer needs, research, observational insight and a host of other data. It also includes potential solutions and emerging technologies, shifts in consumer and market behavior, and potential competitive responses. When deciding what to create, an innovation team must synthesize the entirety of its experiences and knowledge, including customer feedback, but customer feedback should be a component of that decision-making, not the final analysis.
Where we've failed our managers
Here's where we've failed an entire generation of managers. Business isn't a science. It doesn't always work on predictable patterns, but can be driven by trends, fads and new technologies or business models. Anyone applying pure science and reason to a situation that doesn't operate or accept simple and predictable models is going to miss a lot of interesting shifts and opportunities. While we've bred the intuition out of many of our managers, it's never been in higher demand. There's probably no skill that is more valuable right now than the ability to correlate and synthesize wildly different kinds of research, insights, trends and needs to direct a company as to which new products and services to build. You can't simply apply known patterns or models, because the future resembles the past but isn't the same. It's in the places where they differ that real opportunities emerge.
Good innovation managers can corral quantitative results, qualitative insights, customer needs, market trends and a host of other kinds of data and insights and arrive at an interesting opportunity, applying the appropriate weight to each kind of insight or data. Many current managers reject qualitative data, trends and needs to focus only on the data they've been taught to trust, which is whatever data is statistically significant. And if you can get that data, you can rest assured that your competitors have it too. It's in the gray areas, the ambiguity where the real meaning lies. Do you have managers and executives who can mine qualitative murky insights effectively?