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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.
许多高绩效组织至今依然热衷于意大利工程师、经济学家维尔弗雷多·帕累托（Vilfredo Pareto）的犀利言论，信奉他的80/20效率法则，即80%的结果（如销售额、收益等）是由20%的原因（如产品、员工等）产生的。但是，机器学习和AI算法创新使得分析学发生改变，笔者认为，下一代算法会进一步拓展帕累托基于经验的争议性模型。AI与机器学习将如何重新定义组织运用帕累托法则的方式，超越传统分析，以数字途径推动盈利创新，以下有3种重要方法。
Smart Paretos
智能帕累托
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.
首先，数据量更大、更多样，使算法系统能够得到所需要的智能培训。数字网络因此成为帕累托平台，引导各种变量产生新的价值。
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.
例如，创新工作场所分析，可以让更多的组织辨识出为产品、流程或用户体验贡献了80%价值的20%员工。业务流程、平台和客户体验的数字化进展，同样需要我们用创造性的帕累托视角去看待：平台升级的哪20%创造了80%的影响？客户体验的哪20%造成了80%的客户满意度或不快？C级高管须运用算法解决那些数据驱动的问题。
Super-Paretos
顶级帕累托
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.”
其次，数据分布已经与以往大相径庭。在大数据的影响下，帕累托的80/20法则沦为一种过时的经验。越来越多热衷分析的公司看到，帕累托法则的比例由80/20变成10/90、5/50、2/30，乃至1/25。根据数据被掰开揉碎分析解读的程度，甚至会出现1/50、5/75和10/150这样的比例。帕累托的“重要的少数”（vital few）变成了“重要的更少数”（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.
极值分布超越并主宰了行业。例如，10%的饮酒者贡献了一半以上的烈酒销量。还有更极端的情况，不到0.25%的手游玩家贡献了游戏内购收入的一半。
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.
不过，清楚地识别并沉迷“顶级帕累托”，分析深度还不够。市场和市场增长，需要这些分析性的统计数据导出预测性、规律性的信息。换言之，要把数据集转化为智能算法需要的“训练数据集”（training sets）。
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.
组织也须找出帕累托倾向。组织须从算法的角度，对能够促成巨大影响的微小调整进行解析。管理者及其数据科学团队必须关注极致帕累托的潜力和可能性，不要只追求更多、更好的数据。
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.
例如某家数十亿欧元的工业设备公司，SKU超过2000。该公司认定，不到4%的品类贡献了1/3的销售额和近一半利润。但把分析拓展到服务和维护方面，结果显示约100个产品贡献的利润超过2/3。这样的结果促使公司从根本上重新制定价格和捆绑销售战略。
Finer-grained Pareto analytics around product attributes and features, not just the products themselves, offered more provocative insights. The company’s engineering and account teams explored data-driven redesigns around desired features and function sets rather than the products themselves. Processing a different unit of analysis led to even more valuable Pareto insights. Targeted feature removal, for example, not only reduced costs but also led directly to measurably better user experiences that, in turn, increased share in a growing customer segment.
对产品特征（而非产品本身）进行更精细的帕累托分析，提供了更具挑战性的洞见。公司工程和会计团队针对有需求的产品特征和功能（而非产品本身），开展了基于数据的重新设计。不同的分析引出了更有价值的帕累托洞见。例如，移除某项特征不仅能够减少成本，还会大大优化用户体验，近而在发展壮大的细分顾客群体中获得更高的份额。
Supra-Paretos
超帕累托
Third, as data become more granular and algorithms process complex patterns in smarter ways, Pareto portfolio management has changed. The analytically and operationally astute already manage Pareto portfolios — that is, a number of different Pareto insights across the entire enterprise. For them, KPI stands for “key Pareto information,” not just “key performance indicator.” If KPI dashboards don’t facilitate data-driven looks at key Pareto information, people are blind to future optimization and value-creation opportunities.
其三，数据变得越来越颗粒化，算法以更智能的方式处理复杂模式，帕累托组合管理也发生了改变。精明的分析和实践足以形成帕累托组合，即整个企业中一系列不同的帕累托洞见。KPI在此处不只是“关键绩效指标”的意思，也代表“关键帕累托信息”（key Pareto information）。如果KPI面板无法以数据形式更好地显示关键帕累托信息，人们就看不见未来优化及价值创造的机会。
Where individual process owners, product managers, and sales teams once emphasized optimizing their own core Paretos, they now poke, probe, and play with other people’s Paretos. Serious managers and executives break down and burst out of analytic silos. They recognize that their Paretos can analytically intersect, overlap, and productively recombine with Paretos across the enterprise.
过去，流程所有者、产品管理者和销售团队着重对自己的核心帕累托比例进行优化，现在他们开始寻找试探其他人的帕累托比例。明智的管理者和高管会打破分析孤岛。他们发现，自己的帕累托比例可以通过分析和公司各个部门的帕累托比例产生交集，重组以提高效率。
Increasingly, the surest way to rethink and revitalize a Pareto is to link it to another Pareto. As data-rich and algorithmically aware firms shift from individually managing a dozen key Pareto indicators to overseeing hundreds, even thousands, of enterprise KPIs, brave new Pareto ensembles will emerge. Which ensembles will offer the greatest insights and opportunities for new creation and capture?
对某个帕累托比例进行反思和更新，最可靠的方式是与另一个帕累托比例联系起来。数据量大、算法意识强的公司发生转变，以独立管理12个关键帕累托指标转为全面把握成百上千个企业关键帕累托指标，由此将会产生崭新的帕累托组合。哪种组合能够为创新提供最佳洞见和机会呢？
Networking Paretos has consequently become one of the most exciting and productive analytic initiatives I see. What 10% of KPI clusters might explain 90% of new customer, growth, or margins? The challenge of supra-Pareto creativity demands data-driven cross-functional collaboration. Sophisticated managers and intrapreneurs across the enterprise want to innovatively fuse their vital fews.
据笔者所见，帕累托网络逐渐成为一种激动人心的高效分析方式。KPI集合中哪10%贡献了90%的新顾客、新增长或新利润？应对超帕累托创新的挑战，需要数据驱动的跨职能合作。公司各部门成熟的管理者和创新者希望能让“重要的少数”合并起来。
At one global telecom, Pareto analytics of all kinds — descriptive, predictive, and prescriptive — had been developed to anticipate, prevent, and minimize churn. The churn management team had done excellent work identifying and retaining literally millions of at-risk customers. But diminishing returns had set in; performance had plateaued.
某全球电信公司的各种帕累托分析，都已经从描述性、预测性和规律性发展为具体的预测、规避和最小化（客户）流失的举措。流失管理团队成功地识别并留住了几百万个快要流失的客户，然而在回报上收效甚微，公司绩效陷入停滞。
Everything changed when the group decided to go wide. Instead of emphasizing Pareto insights around customer satisfaction, complaints, or service, they discovered several sales and marketing Pareto data sets emphasizing upselling: the 20% of customers who accounted for 80% of new services purchased; the 25% of customers responsible for 75% of the new lines or data plans.
团队决定扩张时，情况变了。他们不再强调客户满意度、投诉及服务方面的帕累托洞见，转而发觉销售和市场营销上的帕累托数据集，注重向上销售（upselling）,关注贡献了80%新服务购买量的20%客户，以及购买了75%新服务或套餐的25%客户。
Analytically armed with these Paretos, the churn team asked whether they could actually upsell their customers, not just retain them. Straightforward regression analysis and simple agent-based modeling techniques found significant profile correlations between Pareto churners and Pareto “upsellees.”
有了这些帕累托分析结果，流失管理团队开始思考如何向上销售，而非单纯的保留客户。简单的回归分析和基于主体的建模技术发现，帕累托流失者和“接受向上营销者”（upsellers）间存在明显的关联性。
Writing scripts and experimentally testing offers proved fairly fast, simple, and cheap. While the ultimate results weren’t revolutionary, they went well beyond incremental. Not only did retention numbers improve, but the churn team spent less to keep them and managed to successfully upsell a percent or two.
事实证明，编写脚本和试验订单是简单、快捷且低成本的方式。最终结果虽然不是革命性的，却也具有超出发展性的价值。不只保有量得以提升，流失管理团队留住客户的成本也有所降低，而且成功实现了1%到2%的向上销售。
But this Pareto ensemble also generated a serendipitous, if obvious, business bonus. The churn team’s new Paretos proved helpful to the upsell sales and marketing function. Their innovative ensembles boosted customer satisfaction and NPS numbers while reducing their own churn rates. Everybody won.
不过，这种帕累托组合显然也产生了偶然的业务红利。结果证明，流失管理团队的新帕累托比例可以用于向上销售和市场营销。他们的创新组合大大提升了客户满意度和净推荐值（NPS）,同时降低了客户流失率，实现了多方共赢。
The preliminary success of Pareto ensembles recalls the critical insight from the Netflix Prize competition: The best results came not from improving individual model performance but from creating ensembles where the best attributes were collectively amplified. Ironically but appropriately, Pareto analytics could determine the most valuable ensembles.
帕累托组合的初步成功，让我们想起奈飞大奖（Netflix Prize）角逐中得来的重要洞见：最好的结果并非来自提升单个模型的表现，而是创造各项优势协同发挥的最佳组合。帕累托分析能够准确判定何种组合最有价值。
The lesson here is that having lots of models is useful for the incremental results needed to win competitions, but, practically, excellent systems can be built with just a few well-selected models.
我们从中获得的经验是，拥有许多模型，对于赢得竞争所需的增量结果有益，但从实践角度来看，少数几个精心挑选的模型足以构建绝佳的系统。
Rigorously applying the Pareto analytics to Pareto analytics seems obvious, but few organizations demonstrate that discipline every day. That must change. Strategic plans and technology road maps need to be analytically informed by “Pareto pathways.” The ability to better predict tomorrow’s vital few, the opportunity to combinatorially combine KPIs from across the enterprise, will become sources of not just greater efficiencies but also determinants of disruptive value creation.
将帕累托分析严格运用于帕累托分析本身，看似简单易行，但没有几个组织能够一直坚持帕累托原则。这个现象必须改变。战略计划和技术路线图必须通过“帕累托路径”分析。若能更好地预测未来的“重要的少数”，各部门KPI就可以有机结合，公司不仅能够提高效率，还能实现颠覆性的价值创造。
The smarter your algorithms, the more they — and your organization — need to be learning from and with Pareto.
算法智能程度越高，越有必要与帕累托原则结合并从中学习。组织本身也是如此。
原文出处：harvard business review
原文标题：AI Is Going to Change the 80/20 Rule
Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, is the author of the books Serious Play (HBR Press), Who Do You Want Your Customers to Become? (HBR Press) and The Innovator’s Hypothesis (MIT Press).
迈克尔·师拉格是麻省理工学院斯隆管理学院数字商务中心（MIT Sloan School’s Center for Digital Business）研究员，著有《研究游戏》（Serious Play，哈佛商业评论出版社）、《你希望客户成为怎样的人》（Who Do You Want Your Customers to Become?，哈佛商业评论出版社）及《创新者假说》（The Innovator’s Hypothesis，麻省理工学院出版社）。
