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
This type of information advantage is more and more accruing to the biggest and richest of corporations. It is a type of rich-get-richer advantage akin to the flywheel effect:
Competitive Advantage and Feedback Loops
