By Andrea Martino, director of strategic growth with Xenon arc
Over the last two decades, I’ve seen a lot of companies attempt innovation. And while they can absolutely succeed on a large scale, some of the most incredible shifts have started small…micro-level small.
In a time when so many manufacturers are looking for ways to save money, create customer loyalty, and differentiate themselves in the marketplace, they’re stuck between a rock and a hard place. They want to innovate, but if their bet doesn’t pay off, they’re stuck footing the bill for a failed effort. On the flip side, if they don’t innovate quickly enough, they get left behind.
So, what’s stopping them? It’s often—though certainly not always—that they’re simply not asking the right questions.
The curiosity factor of innovation
Curiosity is the root to solving problems. I’m convinced that the root of most problems is different from the pain that people express. It follows, then, that solutions aren’t always what people expect.
It’s easy to brainstorm new ideas to solve problems in productivity or internal operations, supply chain, customer service, or product development. However, it’s neither productive nor profitable to throw spaghetti at a wall and hope it sticks.
Instead, it’s worth taking the time to probe deeper to understand the underlying problem and determine why it exists. Otherwise, it’s nearly impossible to identify which ideas are good and can actually solve the problem at hand.
Start by asking one question “Why?”
Understanding why something is a problem, why it’s happening, why it matters, and why the things they’ve tried haven’t worked kicks off a discussion where we keep digging until we can understand what’s really going on. Then, armed with a deeper understanding of the situation, we can explore the data to uncover the answer.
You might be thinking, “Great! Let’s explore the data. But where do we start?” You’re not alone.
The answer to your question is extremely subjective—“it depends.” While that can be frustrating for left-brained folks who like to check off boxes in order, the truth is that you often need more information.
As a scientist, I speak “left brain” fluently, particularly when I’m working with manufacturing clients; the reason our starting point is subjective is that there are multiple variables, which potentially include:
● Who are the players?
● What is the market?
● What opportunities can we create by solving this problem?
In a manner of speaking, you always go to data first. It’s true regardless of the problem at hand, whether you’re handling internal operations, supply chain logistics or new product development, you need to explore the data around the problem to establish a baseline.
To give you an example, let’s explore product development. In this case, it means exploring customer data, especially from non-core customers. I call this subset of data a black box, because it’s full of rich insights that we can test in smaller non-core markets and ultimately expand on a larger scale. Moreover, by uncovering problems and finding solutions, we frequently identify cross-selling opportunities:
● Targeting more customers
● Developing a market-specific grade
● Increasing profitability
● And so many more
Understanding how customers use the product, who they are buying from, and what infrastructure or hardware they have is critical not just for targeting these customers, but for identifying opportunities to shift and grow. More importantly, without data, you are lost and right back to the drawing board.
Using the data to solve the problem
One of the most important things is understanding the insight you need. However, this can be part of the challenge for many companies not used to looking at micro-level data. I frequently hear people say things like:
● “We’re overwhelmed by the data.”
● “We don’t know what to do with the inputs.”
● “We’ve never done it this way—why should we start now?”
● “I feel like we’re under the magnifying glass.”
The truth of the matter is that the old way may still work now, but it’s not going to position you for the best possible success. In addition to presenting potentially huge new profit centers, paying attention to the data gives you insights into problems you might not otherwise hear about.
Instead of getting overwhelmed by data or seeing it as big brother spying on your sales team, you have to flip the mindset to see it as a tool to facilitate the capturing insights, freeing up time, and leveraging what you’ve learned for cross-selling.
All of this takes time, which can be a source of frustration and further overwhelm. But it doesn’t have to be overwhelming. Whatever insights you derive from the data, you can and should test at a minimum viable level before diving deeper.
In the case of product development, you might be testing different forms of an existing product, like selling smaller quantities at higher prices to account for additional packaging and labor. It’s not just smaller markets that might value this—larger companies might see the appeal in stocking lower amounts of products they use less frequently.
If after testing in smaller markets to determine if there’s continued value in pursuing the solution you decide not to pursue it, it’s not a failure. You learned valuable information and have more data to sort through and more questions to ask.
In the case of operational efficiencies, you might identify a change you want to make—maybe a new meeting format designed to increase productivity. By testing it in small subsets of your organization, such as individual departments or business units, you can determine if it works and iron out the kinks before rolling it out to the entire organization.
Asking the right questions leads to innovative answers
One of the biggest objections I hear to this approach is that because of the work that goes into testing and tweaking, it doesn’t always seem like testing on a smaller scale will pay off. Truthfully, many people I talk to say, “Sounds great, but it’s just not our priority.”
But that’s exactly why there’s so much power. By now, we’ve asked the questions and uncovered exceptional data, and we can use it in a small-market test with very little risk.
It keeps coming back to a process that looks a lot like the scientific method:
● Identify a problem
● Asking questions to understand it
● Gathering data and developing insights
● Creating a hypothesis about a solution
● Testing it on a small level
● Evaluating performance
The key, of course, is in maintaining curiosity and a willingness to explore new opportunities and ideas by continually learning and innovating based on the data.
And of all the things I love about my job, the biggest is that I get to let my natural curiosity loose and harness its power to help my clients. By diving deep into the problem and leveraging the data thread, I can get to the heart of the real problem so that we can find a solution…and it just might be the next big idea to take your industry by storm.
