Doug Davidoff
I will admit that as a student in high school and college, I hated science classes. It really didn’t matter the discipline - chemistry, biology, physics - had no interest. The classes didn’t make sense to me, the labs were painful and frankly, I was just grateful that I was able to squeak by to graduate (to this day I still have an anxiety dream around chemistry class).
Now that I’m older (and wiser) I regret the lost opportunity that my distaste for science created. Today I find myself referring to scientific principles that I’ve learned since graduation. Even more so, I find myself talking about and modeling the process of science, and I’ve realized that the scientific model is a great model for any business desiring accelerated and sustained growth.
I’ve also learned that my distaste for these classes wasn’t really my (or science’s) fault. Instead it was the fault of how the classes were taught and the labs were run. The problem - and this is a HUGE problem that plagues small and mid-market businesses - was that the focus was on getting the right answer, rather than on discovery.
The scientific methodology is, or at least should be, all about the discovery of “the truth.” Experimentation is crucial to discovery. Effective experimentation requires that you eliminate the “judgment” of results. Testing out a hypothesis that turns out to be false is just as valuable (and in many cases more valuable) than testing one that turns out to be true.
The “Science” of Business Growth
Running a business is a highly complex endeavor; one more complicated than anyone can grasp. Multiple variables impact each other in an environment of constant change. It’s very easy to confuse causes and effects. As I’ve often said, bad decisions made in a strong economic environment often look better than great decisions in a bad environment.
While it’s okay to be lucky (and make no mistake, luck is always a part of the success equation) it’s not enough to sustain growth. Sustaining the growth of a business requires a strong balance between embracing the mysteries of discovery and exploiting what you know to be true. (Caveat: remember the immortal words of Mark Twain, “It’s not what you don’t know that hurts you, it what you think you know that just ain’t so.”)
The scientific model has an awful lot in common with the design model. In the pursuit of “the truth”, your efforts will go through three distinct phases and it’s important that you embrace each one.
Mysteries
The first stage is the mystery stage. This is the part of the process my high school and college classes skipped. The mystery phase is filled with one-off discoveries and fraught with “mistakes.” There are two dangers here:
- The fear of making mistakes. In my experience, you learn far more from failures and mistakes than you do with success. I’ve always valued a good strong “no” in the sales process. It helps me learn a lot. I’ve discovered that the more comfortable I and my team are in making mistakes, the faster we are able to make them and our journey accelerates.
- Jumping to the next stages too quickly. It’s human nature. We overestimate the predictability of outcomes. A couple of people/companies respond positively and - BAM! - we know “the truth.” The danger here is that this creates a very positive feeling (which releases dopamine to our brain to make us feel even better - see, there it is. I’m the science I abhorred in this very blog post). Unfortunately that feeling is caused by the illusion of certainty. When this happens we stop experimenting, our learning regresses and we’re likely basing our conclusions on the wrong lessons.
Successfully navigating through this stage (and please note, no company that sustains growth ever fully leaves this stage) requires that you understand the business value of mistakes. It means that you must - MUST - assign positive ROI to failure. At this stage you judge success based upon the volume and velocity of learning, not of outcomes.
Heuristics
This is the stage where truths begin to emerge. A heuristic is something that is generally true, but not always. I often counsel companies on a concept I call What Causes Sales. The best answer to this question can prove to be is one that is generally true. For example, at Imagine, we’ve learned that the when someone answers one of our resonating questions “what’s your biggest barrier to growth?” in a certain manner, the likelihood of a successful outcome greatly increases.
So we “know” that that the more people we can get to answer that question in a certain way, the more likely our sales will grow. The link between asking this question, getting an answer and getting a sale will never be 100% (it’ll never be 80%), but we know it to be generally true.
Another example of this is that we know that a company that formally assesses their current demand generation, sales and/or marketing efforts is far more likely to move forward with the types of changes we help companies implement. Therefore, the assessment process is a core part of our sales strategy.
We’ve learned those truths through tests and experiments, many of which did not work; but without them we wouldn’t know what we now know.
There are two dangers in this stage:
- There’s a signal vs. noise challenge here. With modern sales and marketing strategies, data is a crucial part of success, and with today’s systems there’s a lot of data to keep you occupied. The problem is that just because you measure something doesn’t mean that it matters, and often times the easiest things to measure are the least effective.
- Misreading something that is generally true, with “the truth.” As a friend of mine likes to remind me, “you could take 100 gallons of water from the ocean and see that there are no jellyfish; but that doesn’t mean you should draw the conclusion that there are no jellyfish in the ocean.”
Success in this phase means that you continue to test and experiment. This is the stage where you start measuring results (like revenue), but don’t lose site of the value of experimentation. The key here is don’t become complacent.
Algorithms
An algorithm is something that is always true. This is where automation and exploitation become the keys to success.
What This Means For Your Business
Just last week I was talking with a client who isn’t happy with the results they’re getting. He was asking me why they weren’t making more progress faster. My answer was quite direct, “the problem is that we’ve been trying to solve the wrong problem”.
He asked me to expand on that, and I explained that a core part of the problem was that when the strategy was initially laid out there was an assumption that they knew what their key personas wanted. The “problem” they were looking to solve was increasing the velocity of the efforts so more people would see, and therefore respond to their efforts.
At the time the strategy was laid out, we lacked the data to confirm (or deny) that belief, so we moved ahead and focused on those things that are proven to drive traffic. As we set up the structure to gather the appropriate data, we’ve discovered that the problem was that we weren’t attracting the right people to the site.
The assumption was there market/message fit had occurred, and the job was to exploit it. The reality is that we don’t have market/message fit. We’ve had success with select opportunities in the market, but we don’t have the clarity needed to drive the volume we need. We’re still in the mystery stage and we were trying to apply strategies for late heuristic into the algorithm stage.
The underlying cause of this misstep is that success, from the beginning, was defined by generating more leads, creating more opportunities and closing more revenue. Now, please note, I am totally in favor of those measurements and certainly what you’re doing needs to translate to revenue.
However, given the success criteria, the appropriate value for “mistakes” was not assigned. The goal was to be right, so the client allowed the illusion of certainty to dominate their thinking and drive the strategy.
If the company were more comfortable with and valued mistakes and failures that led to learning, they’d be much further down the path, with far more momentum than they have now.
And that is the most common mistake that is killing business growth.
