Spreadsheets, correlation and ‘data of the past ’….They’re needed. I use them every day.
But I don’t rely on them when it comes to predicting the future.
As an executive, your tasked with predicting the future or more like reducing the odds of failure. If you are guessing, you are more likely to be guessing again a machine or a competitor executive, aided by a computer. That machine is empowered with machine learning, AI or something else your human brain can’t outmaneuver by guessing.
The growth operating system we all want does not exist yet, but it’s getting closer. To get there, you need to balance three data sets asap.
— Data of the past forms a foundation of where you have been
— Real-time data, where are we right now
— Predictive data, not based on the past in any way, shape or form
Today I had an exciting conversation about artificial intelligence. It’s incredible to think in late 2018, data from traffic cameras can change the market share of healthcare companies dramatically. Not because of something that happened in the past but predicting using ways of thinking far beyond a person. In this one example, millions if not billions are at stake.
How much of which kind of data do you need?
Sometimes, depending on who you are and what kind of business you’re in, the organization needs a little bit more of one versus the other.
If there’s no data of the past, whatsoever, you don’t have a choice but to look forward. Maybe you can model something from the past but not likely with too much accuracy. We use to call that assumption.
If you have slow growth or stagnant business, that’s not going many places anytime soon, and with few competitors, you can rely on data of the past. Sadly, that’s the foundation of how schools teach us. Using data from the past models are excellent when it’s a lazy river. Things are changing too quickly. There’s got to be a competitive advantage that is different from the past.
Today it’s very different if you’re in a fast-paced environment. If you’re impacted and disrupted by technology, you’re feeling the pain. People are predicting and connecting things that have never done before. It is a multiplier effect of efficiency and girth.
Plugging in the right operating system at the right time and the right stage is essential to driving the company to its rightful size and volume. It’s also important when creating the right value expectations.
Most companies I helped grow usually start with a ‘data of the past’ business model. Getting people to look at predictive data sets is difficult. It feels unnatural. Excel is such a warmer, more cozy place to be for middle management, who often arm senior leadership with ‘facts.’
By adding in elements of a growth operating system, we combine data of the future with data of the past and sprinkle in a little bit of real-time data. It’s the elements of a pretty good growth operating system. Not perfect but good. Adding in sources of truth and understanding exactly what type of source you are looking at, helps define limitations, anomalies and builds the foundations of an OS.
An example: Very early on I remember pushing a major airline to increase its CPA from $2.50 per ticket to $11.75. I wanted to factor in the early-stage customer journey, as $2.50 was competing with its consolidators, and the result was only adding very few net new customers. My theory was people who were aware of the airline did not shop the airline because of that classic ‘out of sight, out of mind’ issue. Too many options. I had NO DATA to support my claim. Instead, I devised a test, and it worked. Today, over 50% of new customer sales come from a more advanced full customer journey. And it is a system that does not rely on cookies and session IDs to ‘prove’ value.
When we append jobs to be done theory as a driver to defining our total addressable market, a real total addressable market, we have a foundation of an operating system for business. When we know how many people can be customers and should be customers, we can define things like customer journey far better than the limited, linear systems our competitors struggle with today.
Growth operating systems combine complex, multiple sources of data that don’t usually play well together. Smart organizations focus on the right things while others live in fear. It’s time for more people to leap ahead.