Mar 1, 2017 1:15 PM PT
One of the foundational ideas of business is the Pareto analysis, which tries to identify the small portion of factors that are responsible for the majority of business results. You know -- 80 percent of profits come from 20 percent of the customer base, and similar observations.
Vilfredo Pareto, an Italian engineer and economist, formulated a business classic that bears his name, which we now refer to simply as "the 80/20 rule." In fact, the rule is malleable, and subject to change based on all kinds of factors that are unique to a business.
Some businesses see a 90/10 effect or even a 60/40 split, but the point is that a small portion of all contributions to business success make the lion's share of the contribution. So which ones are right for your business?
Cutting Through the Noise
In a Harvard Business Review article, "AI Is Going to Change the 80/20 Rule," analytics guru Michael Schrage points out three tips that can help any manager to do a better job leveraging big data and analytics. The one that most interests me is the idea that you can have too many Pareto analyses, thus making the whole exercise full of noise and less predictive.
For instance, what's most important to keeping customer churn and attrition low? You could develop models, metrics, or KPIs that assess all kinds of things about your business, from product quality to customer service to availability of online help -- the list is endless.
However, only some of the analyses you come up with will be important, and it's more likely that you'll arrive at a clear understanding only if you track the interaction of multiple KPIs and their relationships with each other.
Analyze Your Analyses
A couple of books ago, I wrote about the need for triangulation when using metrics and KPIs, and Schrage seems to be on that path with his idea, which is to perform a Pareto analysis of your Pareto analyses. This, of course, sounds contradictory, and it is, but it also makes sense.
Today, a business easily can get to a point where it has not only too much data, but also too many analyses. When that happens, going up a level of abstraction makes all the sense in the world -- especially when you have the compute resources to perform the function automatically.
Note that this is different from using one or a small set of models at the department level. Those models are more tactical and can help individuals to do their jobs significantly better. A model that can tell a sales rep that this opportunity won't happen but that one might is a real benefit, because it saves the sales team from investing resources where they are not likely to be productive, while focusing them on the better opportunities.
That's not enough to run a business, though, and the purpose of the triangulated or Paretoed Paretos is to get a bigger picture that can include multiple departments and even input from customer communities to better understand how the company is performing over all.
The Heart of the Matter
This is a great example of two things: the power of modern intelligence systems to sift through a business' voluminous data; and how technology advances open up new ways of thinking that were not available before.
Not that long ago, a business leader might have had access to churn and attrition numbers, and that leader even might have been able to see a month or two ahead to understand which customers were in danger.
Too often, those danger signals were a green light to offer discounts on a future purchase. That sounds shrewd, but what if the customer's problem had nothing to do with pricing? What if the customer needed an upsell of services or additional products to make everything work? Schrage offers examples of this.
With this advanced approach, we're becoming better able to understand the reasons our primary indicators are flashing. We can be more confident about entering a customer interaction with relevant knowledge, and we can drive our interactions toward conclusions that are more mutually beneficial.