Problem: The implementation of a learning culture is grossly inhibited by the desire to define all known variables and mitigate risk throughout an organization.
Opportunity: While there is room for precision in certain corporate functions, creating, accelerating, and replacing revenue functions should be adaptive rather than prescriptive in nature.
Resolution: Use data to inform experimentation and edge initiatives that have the potential to lead to discoveries, insights, and new opportunities that were not part of the initial hypothesis or scope of possibilities.
I remember the first multi-million dollar business unit that I managed. I was in my late 20’s. This particular unit had a three-tier approach to customer segmentation, and the top tier was a continuity program. My unit generated approximately 10 percent of my division’s profit. I felt like I had finally made the big leagues.
I initially focused our efforts around operational efficiencies by introducing new technology, renegotiating supply chain contracts, and creating new lead generation efforts through content marketing, which at the time was still a very unknown discipline.
While we increased the bottom line by more than 25 percent and increased the top line by 10 percent in the first 12 months, I wasn’t convinced we had realized the full potential market value yet. I carefully worked through my budget to see where I could trim some underperforming marketing initiatives and create some margin to introduce new products into the marketplace.
My plan was to increase the lifetime value of the current customer segments and create new revenue from new places and spaces.
Looking for New Revenue Potential
I locked in on two or three ideas that I believed had merit. I was careful to build a prototype that could be defined, measured, and repeated. I found existing customers to use as a test as well as new customers to use as prospects. Once the research and build were complete, I prepared an internal report for my up line management to explain what I had learned and outline the revenue growth potential.
But rather than getting a pat on my back, two things happened that I didn’t anticipate. One, a completely different part of the enterprise decided that it was a better fit to take my new initiatives to market even though I had made a business case, built the prototype, and invested my marketing budget to demonstrate evidence of future growth potential. Second, management explained to me that since my business unit was performing so well and I was able to identify the marketing budget for new initiatives, they determined those dollars would be rerouted to underperforming units that could use the help to get back on plan.
I was dumbfounded. When I pressed in to better understand why this had happened, one of the finance leaders in the room explained that when you already have maximized a known value, you then redistribute any excess to areas where you have a high potential to extract additional known value. This made no sense to me and, ultimately, confirmed for me that this was not going to be a place where I invested my efforts long term.
Managing Growth to Mitigate Risk
It wasn’t until nearly a decade later I began to understand what was driving his decision at the time. In an effort to mitigate risk, the finance leader decided new initiatives that might lead to new revenue were incredibly riskier than allowing existing business lines to continue to operate below plan. Even though I understand where he was coming from today, I am still convinced his thinking was flawed.
His perception of risk was directly related to the lack of flexibility in the algorithms driving his decision.
His framework was based on assumptions that were static rather than dynamic. Thus, he didn’t have a way to learn and adapt to new inputs.
The risk was introducing something that, while prototyped and generating revenue, might contain an unknown variable he did yet understand, especially in relation to the entire organization.
I don’t fault him for his decision. In fact, I have a great deal of empathy for him as I reflect on this experience. He was doing what he needed to do to get an “A” on his scorecard. And, ultimately, that division, which generated about $100M in annual revenues at the time, no longer exists within that enterprise today.
While I don’t believe the decisions about my business unit directly led to this reality, I believe it was this type of thinking and management that created a culture that mitigated risk so well it never learned how to create new value in the marketplace.
Algorithmic Thinking Works as Long as All the Variables Remain the Same
His frame of reference was tied to management theory and practice that really came of age during the industrial revolution. The only problem is that the business climate at the time was anything but business as usual. Massive disruption was just beginning in this vertical, and there was no evidence yet to suggest adaption of his algorithmic thinking was required.
When norms are established and don’t change much over the decades, the idea of defining all known variables and mitigating potential risk through compliance and audit measures is very appealing. But in a climate where the marketplace is changing faster than an organization can assess, assimilate, and integrate into its strategic functions, it can be debilitating.
The reason why most organizations will efficiently put themselves out of business is that managing to current norms is a self-limiting reality and a self-fulfilling prophecy.
When and Where a Heuristic Leadership Framework Is Needed
That’s why areas within an organization responsible for driving revenue should apply a heuristic technique to their strategic planning. A heuristic is a funny word really, but its implications are profound. It suggests a posture that allows for experience, data, and learning to become the new variables in the equation for success.
When learning is at the heart of our growth strategies, then-current constraints are merely signals to new opportunities.
Leaders who are empowered to prioritize and fund new initiatives will surely fail and succeed. In fact, they’ll likely fail more than they succeed. But when they succeed, it will influence and inform current and future growth potential.
This is why the greatest risk within management practice today is to allow algorithmic thinking to minimize or eliminate a heuristic approach to discovering new revenue potential.
As we live into what many believe will be the decade of persistent disruption to existing business models, perhaps we need to revisit the problem solving and cognitive science that affords a more flexible and agile leadership framework.
Operating from a place where everything is defined essentially means any new inputs that might invalidate current formulas are categorically rejected. And that decision will be the downfall of organizations that today are perceived to be immovable objects.
Ben Stroup is Chief Growth Architect and President at Velocity Strategy Solutions where he helps leaders design, develop, and deploy smarter business growth strategies. Ben is a futurist, disruptor, and data champion. He leads a team that takes a structured learning approach to business challenges, which allows them to assist leaders in bridging the gap between ideas, innovation, and revenue—taking ideas from mind to market.
Velocity Strategy Solutions is an on-demand, next-generation business strategy and management consulting firm which provides clients with a relentless focus on data, execution, and results that positively impact the bottom line. Velocity delivers integrated people and revenue strategies combined with a disciplined approach to growth architecture that elevates the capacity of leaders, teams, and organizations to succeed and win more.