Poker Is A Bad Teacher For Decisions
The Real Skill Poker Builds Is Behavioral (Executive) Skills
Poker players love talking about the skills one learns from the game that can provide lessons for business or life. I have always found these claims or beliefs to be ridiculously misinformed.
In this article (my longest ever for poker), I will describe various poker skills that transfer well to a career in tech startups and businesses in general. Also, I will talk about poker skills (and learned traits) that hold people back in other fields. This latter part is just as important as the former but poker players hate hearing it.
Most of what poker players (pros who play poker full-time or recreational players) talk about revolves around decision making. Decision science is a highly popular area of discussion for people in business. As such, it would be correct to assume that most people highly overestimate what they know and underestimate what they do not know.
This happens with topics that attract a great deal of popularity.
I will say a few things about decision making in poker vs. business decision making before digging into other, much more important skill areas - such as behavioral competencies.
DECISIONS, DECISIONS, DECISIONS
All decisions are based on data of some kind. Data is collected, compared, analyzed, and narrowed down before it can be put to use. Data which is put to use (ie, evaluated and considered) must get weighed against costs and benefits.
In business, this only serves as the starting point of decisions. Data does not drive most decisions - it either supports decisions or enhances or modifies decisions. In poker, data is a decision driver.
ONE DECISION CAN GET VERY CONVOLUTED
Let’s use a real-world example to make the above observation easy to understand.
A common decision for a company is to determine distribution channels. How should a company deliver a product or service to an end customer who pays for it?
An executive would collect data to figure out which is the right channel to bet on. A direct sales model is very costly upfront but proves to be the most profitable long term. An indirect model (using VARs and/or SIs) proves to be less costly upfront but proves to be less profitable, and less permanent, long term.
The above gets us to around 5 percent of the data required to make a good decision. There are about two dozen other factors that must be analyzed and all of it requires additional and new data.
How do our customers procure and receive our type of product(s)? Are there channel conflicts that can stretch the sales cycle out unnaturally? What is the breakdown of the target segment’s cost center locations? Distribution reach and footprint?
How do the margins for each GTM model change over time in terms of financial modeling with and without presumed economies of scale? What is the cost of training indirect channel partners? What impact does carrying exclusive vs. tiered partners have on the outcome?
Is our product type and customer class abundant with enough pre-skilled sales professionals? What is the average fully ramped, fully factored quota a rep can carry? What is the industry norm on sales turnover for this type of sales motion? How much field engineering or pre-sales engineering is required per account, per territory, per vertical industry?
Will the channel intervene as our product either becomes an extensible platform or overlaps with competing parts of their price book? Who will have account control? How does our product get serviced? What about renewals, upgrades, and future integrations?
As you can see, the decisions are highly complex even as they seem ordinary. And, the above only captures the distribution aspect which is roughly 20 percent of the strategic GTM equation. Beyond strategy we also have tactical and functional decisions on resources.
We also have behavioral, cultural, and core competency tradeoffs. We have financial, time, and skills constraints. We normally have unplanned events that force us to revisit decisions constantly and continually.
This is why someone like Annie Duke, a former self-proclaimed superstar poker pro and decision expert, advising companies on how to improve decision making is pure nonsense. So, why do a handful of companies hire clueless people like Annie Duke?
Companies hire people like Annie Duke for the same reasons why they let employees expense books from people like Tony Robbins (useless) or Jim Collins (useful) - as a company perk, to get executives and employees some relief.
The novelty and general insights are what those companies are paying for. They are not paying to deeply learn anything much less implement what comes out of these self styled gurus’ mouths.
ARE THERE ANY SKILLS FROM POKER THAT APPLY?
Yes, there are skills we learn through poker that are highly portable and transferable to other fields such as technology startups or companies. Unfortunately, making decisions is not one of them.