Insights, Innovation and Intuition
I've written here many times about the conflict that many innovators often face when called on to make definitive decisions about customer needs and the best ideas to pursue as new products and services. We've noted that innovation requires - no demands - people who are comfortable operating in an ambiguous stew of information, research, trends, insights and customer needs. Few of these data points are developed with any statistical rigor, yet together they must provide a direction for the team to follow.
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.
Innovation Process
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?
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.
Innovation Process
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?
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1 Comments:
Great topic to tackle Jeffrey. I'm an advocate for getting deep on understanding customers' jobs-to-be-done (encompasses both needs and wants). But I'm well aware of the literature that talks about latent needs, and customers inability to get beyond what a product does today.
For me, solid grounding in customers' jobs is the foundation. I say that as someone who has seen the other end of the spectrum too often. It's all "intuition" that drives product decisions. If pressed, these folks don't really know what customers value and where the gaps are in what they want to do.
I agree with what you say here: "Anyone using only customer feedback to identify needs is probably missing the point". I can think of two modes where this is particularly pernicious.
(1) Responding only to what customers send your way. Customer support sites, account rep conversations, online customer communities...these are great for a variety of reasons. But you're traveling with blinders if that's all you have.
(2) Surveys are good, but are limited in what they capture. There's no back-n-forth, there's no understanding of deeper context.
Where does intuition come in here? Two big ways from my perspective:
First is recognizing when to ask "why". A customer gives feedback. Your intuition needs to kick in to recognize that this feedback is sometimes just the tip of an iceberg. Dive deeper by asking for more background.
Second is looking at areas where there are gaps in what the customer is trying to get done, and putting those in the larger context of what you offer. Understand where the opportunity fits in the broader organizational and market picture. Upon learning of a gap, your intuition should practically have a "click" moment where you can see how it fits into a broader opportunity. That "click" moment is your intuition talking.
Hutch
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