The increased complexity and endless disruptions of the modern world brought on by the transition to the digital age means uncertainty is everywhere. All across our businesses, we face new challenges, as what used to work no longer achieves desired outcomes. The self-awareness of admitting what we don’t know is the first step toward figuring out new best practices. Fundamentally, people must act differently in the face of uncertainty. Businesses need to adopt learning strategies in order to improve, adapt, or even reinvent their execution strategies.
The horizon model originally came from the book The Alchemy of Growth by Stephen Coley, Mehrdad Baghai, David White. The purpose for the model was to help businesses think about their need to develop new revenue sources over time. Near term, you must protect your existing revenue sources and if possible, even extend them. But at the same time, you must consider products or markets that are in decline and how you might replace that revenue. The trick is, you have to start building the replacements today in order to realize the revenue in the future.
Most big companies think technology when thinking about innovation, regardless of whether or not they’re technology companies. Startups are often considered to be de facto innovators, but is that the case? Marketing tends to have their own way of talking about innovation. Is there any Fortune 500 company that doesn’t talk about their innovation capabilities in their About Us section of their website?
According to the Duke University/CFO Business Outlook survey, the longest-running and among the most respected research of financial executives, nearly half of the CFOs surveyed believe that the United States will enter a recession by the end of 2019, and 82% expect that a recession will happen by the end of 2020.
For those of you out there running your company’s innovation program, you know what that likely means. Despite the fact that mature innovation programs are constructed in such a way to protect against downturns, they are often the first programs to be cut. Worse, despite the increase in uncertainty concomitant with economic downturns, businesses forget what they learned in their innovation programs. They revert to entrenched execution behaviors, trying to squeeze blood out of the turnip.
I would like to offer a different, proactive strategy; one that demonstrates the opportunity cost of not investing in innovation far outweighs the immediate cost savings. The strategy is derived from our experience guiding clients through uncertainty.
For those of you out there running your company’s innovation program, you know what that likely means. Despite the fact that mature innovation programs are constructed in such a way to protect against downturns, they are often the first programs to be cut. Worse, despite the increase in uncertainty concomitant with economic downturns, businesses forget what they learned in their innovation programs.
“Feedback” is a loaded word. Asking for it in the wrong way not only will result in poor information, it can actually hurt your business because more often than not it’s predicated on opinions and not actual facts. Bad feedback sends you in wrong directions, provides false positives, and false negatives.
So how does one get valuable, actionable input from users, potential customers, and other stakeholders?
Focus on insights, not feedback.
Understanding the problem
Filtered.ai is a Techstars startup led by CEO Paul Bilodeau and Derek Bugley, whom I had the good fortune of mentoring as part of the 2018 Techstars Anywhere cohort.
Filtered got its start as a data science consulting firm trying to solve their own recruiting problems. Seeking to hire software developers, they received hundreds of unqualified resumes from recruiters. In response, they developed a simple coding test to be used as a filter.
Their minimum viable product (MVP) immediately paid dividends. Recruiters could only send resumes of engineers who achieved a specific score on the test, dramatically cutting the time and cost of recruiting new developers.
The return of a portion of the output of a process or system to the input, especially when used to maintain performance or to control a system or process.
Feedback is not what people think it is. Originally, feedback is an electronic signal received in response to an electronic output. The signal received back can help determine if the outbound signal was “right” or received properly. Today, the term can be applied to non-electronic and non-automated processes, too.
Feedback is good for improving or correcting a process. It’s not good for measuring the impact of the process. Feedback on a product feature, for example, may tell you if it works as expected, but not if the feature contributes to some larger desired outcome. If you want to know about the desired outcome, you have to ask explicitly for that.