Why do business professionals tend to believe something that takes a lot of effort to build, or was difficult to model, over something that was faster / easier? Understanding is the way, isn’t it?
Unfortunately, most Corporate Strateate meetings follow an ambiguous script.
The business team enters the meeting room with a 127-slide deck filled with regression models, sensitivity analysis, or Monte Carlo simulations. The end goal seems to be appropriate decisions that can be tested in a few weeks using real people, people who represent our consumers. Somewhere we started to convince that “difficulty” equals “ror”. We concluded that there is great difficulty in making honest decisions, and that anything obtained without visible struggle has no real value.
Behavioral scientists call this effort certainty. When we cannot measure quality directly, we use effort as its proxy. What started as a mental barrier has been attacked in the doctrine of the organization. We have built all the company’s structures to reward him for working with results, systematically punishing quick thinking that wins wins in the markets.
In the real world, consumers don’t care about how we arrived at our pricing strategy. As far as anxiety goes, the biennial reassessment is like a quick sketch drawn on a napkin. What do they really want to know if the price feels on the right. Shareholders don’t pay for a good method, they pay for results. However, within many businesses, we behave in a contradictory way.
Why do we create complexity when simplicity works
An 18-month transformation involving multiple vendors and endless stakeholder meetings looks safer than it is. When something fails, the burden begins to change faster than a fart in a high place. It’s a seller’s problem. Even if the counselor comes down. Or a working group that cannot synchronize. At the end of the day, Nobody’s job takes a direct hit, because we all want to put the blame somewhere else.
Compare that to a three-week price test done in the real world. If it fails, there is nowhere to hide. Failure is always there as clear as day for all to see, immediately available to certain people who have made certain bets.
So what do we do? We avoid using those types of tests!
We avoid small, fixed decisions that teach faster than competitors. Instead we create fancy models and justify them in meetings. Customer recognition as “People abandon carts because they can’t find the checkout button“It’s being promoted into heat maps and funnels and engagement matrices. Instead of adding clarity, we’re adding explosion.
What happens when a brilliant model meets reality
While their competitors poured millions into the speculative model, airbnb decided to zig where everyone else was. Instead of running one test with a gazillion variables, they run thousands of smaller, faster tests. Look at what customers actually do instead of modeling what they can do. When tests reveal something important, fix them. When they’re not, they leave things alone and move on. The Judging Hierarch was incredibly flat – there were no long committee meetings full of little people to do more than validate the model, or six-month cycles. All they had was the learning of a real world user covering quarter after quarter. The rest is history.
Starbucks didn’t face it for using endless customer research. Amazon didn’t build customer recognition based on statistical models. These businesses won because they made fast, dynamic bets (the famous two-door Amazon) and learned from the user’s real behavior instead of imagined behavior.
The tyranny of value thinking
Too many businesses continue to treat measurement data as evidence and qualitative data as anecdote. Interviews, observations, and direct customer feedback are all filed under “Soft”, because they are difficult to measure and measure. Finance wants models for five years before agreeing to the tests, although those models rested “wet finger in the air” more consideration than the original programs that were intended to be tested. Meanwhile, the real competitive advantages that move the markets are often included in the spreadsheet.
Thinking about the marketing team getting three customer conversations that a certain phrase increases the size to be successful in conversion. They bring this to the leadership meeting and watch as everyone gives it respectfully. Then they deliver a dashboard with engagement metrics from 100,000 users and look at all the people in the room going forward. The dataset is noisy, and the understanding is weak. But because it has a bunch of big numbers and it’s done in a pseudo-science way (look! It gets pivot tables and everything!) It looks legit and it’s taken a lot.
What does solid mean
Of course, there are many degrees that confirm doubts. I don’t want my pilot to take a punt on doing anything different this time”… just to see what happens.“Security systems, control points, or bets we can’t reverse all the situations where they ‘work,’ most business decisions are not. We use all the complexity of the mouse.
Real rigor is not about the volume of data or the positification of the method. It’s about clear thoughts and quick learning. A hypothesis like, “This banner increases signups by 15% because it removes confusion about the next step“It is stronger than the 100-slide model built on full thinking. The first test in days, while the other closes incomplete bets, and includes bets that do not turn quickly, and make bets that do not turn quickly on purpose than those who expect certainty that never existed.
Actual cost of modeling instead of testing
Six months of decision modeling doesn’t just cost a budget, it costs learning cycles. A competition that runs a dozen small tests in that same window is learning more than we can. Their judgment improves, as does their competitive position. The more iterations we can do in a predetermined amount of time, the more we can learn. Fail fast, fail often, right?
Markets do not benefit from thorough analysis or an impressive approach. The speed and time of most of the time. Every dollar we spend on skills that justify justifying “a lot of theater” is a dollar spent preventing us from effective learning. The business that gets the fastest, wins.
Not because they are smart, but because they have had more to do.
