A prescient article from the Harvard Business Review, February 28, 2017:
Many high-performance organizations remain passionate about Vilfredo Pareto, the incisive Italian engineer and economist. They continue to be inspired by his 80/20 principle, the idea that 80% of effects (sales, revenue, etc.) come from 20% of causes (products, employees, etc). As machine learning and AI algorithmic innovation transform analytics, I’m betting that next-generation algorithms will supercharge Pareto’s empirically provocative paradigm. Here are three important ways that AI and machine learning will redefine how organizations use the Pareto principle to digitally drive profitable innovation to levels beyond conventional analytics.
Smart Paretos
First, greater volumes and variety of data guarantee that algorithms get the training they need to get smarter. Digital networks consequently become Pareto platforms that transform vital vectors of variables into new value.
Novel workplace analytics, for example, mean more organizations can more readily identify the 20% of employees contributing 80% of value to a product, process, or user experience. Ongoing digitalization of business processes, platforms, and customer experiences similarly invites creative Pareto perspectives: What 20% of the platform upgrade creates 80% of its impact? What 20% of customer experience evokes 80% of delight or distaste? Serious C-suites want those data-driven questions algorithmically addressed.
Super-Paretos
Second, traditional distributions have disruptively changed. The dirty little productivity secret of big data is that Pareto’s 80/20 insight has decayed into empirical anachronism. Analytically aggressive firms increasingly see Pareto proportions closer to 10/90, 5/50, 2/30, and 1/25. Depending on how rigorously the data is digitally sliced, diced, and defined, 1/50, 5/75, and, yes, 10/150 Paretos emerge. Pareto’s “vital few” becomes a “vital fewer.”
Extreme distributions transcend and dominate industry. Fewer than 10% of drinkers, for example, account for over half the hard liquor sold. Even more extreme, less than 0.25% of mobile gamers are responsible for half of all in-game revenue.
Clearly identifying and cosseting the “super-Paretos,” however, doesn’t go analytically far enough; market and market growth demand that those descriptive statistics lead to predictive and prescriptive statistics. In other words, turn those data sets into “training sets” for smart algorithms.
Organizations need to identify Pareto propensities, as well — they need to algorithmically crack the code on the tiny adjustments that promote order-of-magnitude business impacts. Managers and their data science teams must reorganize themselves around extreme Pareto potentials and possibilities, not just more and better data.
For instance, one multibillion-euro industrial equipment company with over 2,000 SKUs determined that less than 4% of its offers were responsible for one-third of sales and roughly half of profitability. But extending the analysis to include service and maintenance revealed that roughly 100 products were responsible for over two-thirds of profitability. That pushed the firm to fundamentally rethink pricing and bundling strategies....
....MUCH MORE
- Competitive Advantage and Feedback Loops
- How to Think About Companies: "Advantage Flywheels"
- Flywheel Effect: Why Positive Feedback Loops are a Meta-Competitive Advantage
- "Analyzing the deepening divide in learning capabilities between a few corporate giants and the rest of the world." (plus advantage flywheels)
- "America's Biggest Firms' Moat Is Becoming Impregnable" (TSLA; NVDA; GOOG)
And related, now that we see what is happening, what, if anything, should society do about it?
- FHI: Who Gets The Benefits Of Artificial Intelligence?
- "AI Designs Computer Chips for More Powerful AI" (GOOG)
- "Inside big tech’s high-stakes race for quantum supremacy"
- Not into quantum computers? Then you've made the decision to be a peasant, working for those who are.
And the rich get richer.
- "The concentration of economic power has led to spectacular investment returns"
- "An AI payout? Should companies remunerate society for lost jobs?"
- How Amazon Rebuilt Itself Around Artificial Intelligence
- A Very Smart Look at Income Inequality: The Claremont Institute Book Review of "Winner-Take-All Politics: How Washington Made the Rich Richer—And Turned its Back on the Middle Class"
- "Facebook, Google And Amazon Wield Power Over Us All, And Everyone Should Be Worried" (AMZN; FB; GOOG)
- HBR: Corporations in the Age of Inequality — Inequality isn’t just about individuals — it’s risen between companies, too.
- "Why Do the Biggest Companies Keep Getting Bigger? It’s How They Spend on Tech"
...Much more important than the direct monetization of big data is the strategic advantage it can bestow over time.
In a winner-take-all economy, as in a horse race, small differences in superiority are rewarded all out of proportion to the actual advantage. A top thoroughbred may only be a couple fifths of a second faster than the field but those two lengths over the course of a season can mean triple the earnings for #1 vs. #2.
In commerce the results can be even more dramatic because rather than the 60%/20%/10% purse structure of the racetrack the winning vendor will often get 100% of a customer's business.....
