The scene in the movie Moneyball in which a group of scouts sits around a table opining about the pros and cons of various baseball players really paints a picture.
These scouts – fixtures of Major League Baseball – voice their approval of those who seem to be the most promising prospects based on their good looks and style. And they discount potentially valuable players because of an unorthodox pitching style or, even, an unattractive girlfriend.
It shows how, in the past – and to a great extent even in the present, the actions of people and organizations have relied heavily upon personal impressions and gut instincts as opposed to actual fact.
This is true whether you’re talking sports, politics, marketing, or just about anything else.
Professional sports teams now capture and analyze every move of their own athletes as well as those of competitors. Data analysis is even happening at the foundational end of atheletes. For example, my husband is the statistician for our 13-year-old daughter’s softball team, using specialized softball software to track how each player reacts to every pitch, and capture the path of every hit ball.
While emotions will always run high in both sports and politics, the sport of politics is also getting more analytical – at least in some respects.
Nate Silver put the importance of data analysis center stage during the 2008 U.S. presidential election, in which he correctly predicted the outcome in all but one of the 50 states. (As an aside, it’s interesting to note that Silver has one foot in baseball and the other in politics. He first gained acclaim for developing a system for forecasting the performance and path of MLB (News - Alert) players, then rose to prominence in political circles, and this year left The New Yorks Time to move to ESPN to pursue his first love.)
Now more organizations aside from just sports are trying to put data to work for them.
As Michael R. Levy, principal of market research firm GZ Consulting, recently noted, one exciting area on this front it to use predictive analytics tools to identify the key triggers and attributes of top customers.
“While historically, targeting was limited to segmentation based upon firmographics and job functions; predictive analytics identifies buying signals across a vast array of news, company websites, job boards, filings (e.g. patents, trademarks, UCC), social media, and other structured and unstructured information sets,” Levy wrote in a blog he does for OneSource (News - Alert) Information Services.
“From this broader set of sources and trigger types, you may identify new clients in non-traditional verticals and focus sales and marketing efforts on your top priority leads as scored by the predictive models,” he continued. “Furthermore, the predictive models will recommend products and sales messaging in line with the mined intelligence.”
Larry Freed, CEO of ForeSee and author of the new book “Innovating Analytics”, recently told CUSTOMER magazine that while big data presents a great opportunity for companies to better understand their customers, there’s also a need to rethink existing models for categorizing customers.
The Net Promoter Score has risen to prominence in recent years as a way for organizations to easily classify their customers as detractors, passives or promoters. But Freed said NPS has hit its peak and is on the way down because it’s not a very predictive metric. As noted in his book, NPS has a high margin of error, is overly simple, and doesn’t take into consideration that customers may at some times fit into more than one of these categories.
A better method for understanding customers, according to Freed, is what he calls the Word of Mouth Index.
“WoMI evolves NPS by measuring both likelihood to recommend and likelihood to detract from a specific brand by adding a second question,” explained Freed. “‘How likely are you to discourage others from doing business with this company?’”
The idea that the days of the NPS are numbered is not entirely new.
Earlier this year I met Matt McNerney, president at Ipsos Loyalty, Research & Consulting. He said NPS doesn’t necessarily translate into increased wallet share. To illustrate this point he noted that Kmart reported its highest customer satisfaction rate the same year if filed bankruptcy. Meanwhile, WalMart had an initiative to improve the appearance and aisle width in its stores, which did make customers happier, but didn’t ring up more sales.
What organizations should be doing is looking not only at customer satisfaction but also at the competition, and their own rank in the market. For example, it would be very helpful for a store to know that only 3 percent of its customers shop there for beauty products exclusively. That means there’s a huge opportunity there to bump up sales for beauty products. Armed with this information, the store might want to assess its beauty production selection and pricing, which if adjusted could increase the brand’s rank and per customer spend in this category.
Edited by Stefania Viscusi