Even if, according to Wikipedia, he did not invent the phrase “lies, damned lies and statistics”, Mark Twain did much to popularise it - and it often seems that many of our current politicians are doing their best to perpetuate the term.
But as anyone familiar with the power of numbers knows, correlation does not prove causality. The relationships are frequently more subtle than that - but properly mastered, as Bob Thompson argues in this guest contribution, analytics can unquestionably improve the customer experience…
Are You Torturing Data to Tell Lies?
By Bob Thompson, founder and CEO, CustomerThink Corporation
Economist Ronald Coase famously said, “If you torture the data long enough, it will confess.”
A classic case of statistics abuse was published by the San Francisco Chronicle, when a journalist wrote about the United States lagging behind other countries in average Internet speeds. With the US “limping behind Latvia and the Czech Republic” in ninth place, he concludes this is “troubling news” because studies link faster Internet connections to the economy. Doubling Internet speeds would increase GDP by .3 percent, or $126 billion, for the US economy.
Was the columnist taking creative license with the report findings? Sadly, no. I reviewed the full report, sponsored by Ericsson, and found these conclusions in the section “The Socioeconomic Impact of Data Speeds.”
- Doubling the broadband speed for an economy increases GDP by 0.3 percent.
- Eighty new jobs are created for every thousand new broadband connections.
- For every 10 percent increase in broadband penetration the GDP growth is around 1 percent.
The report says it used regression analysis, a statistical technique that attempts to relate one or more independent variables (broadband speed, in this case) with dependent variables (economic impact). But correlation doesn’t imply causation. While it may seem reasonable that advanced economies use technology more aggressively, it doesn’t follow that technology usage literally causes economic growth. In my view, broadband speed is probably just one of many different growth enablers including roads, bridges, power, and schools.
Actually, it’s quite possible that cause and effect work in the opposite direction. As economies grow, businesses decide to invest in faster Internet speed. The correlation would look exactly the same.
Confusing correlation with causation is one of the more common mistakes in using analytical techniques. Yet correlation is frequently used to promote the idea that some new technology or trend will lead to business success. Rarely is the research done in a way to show true cause and effect.
For example, excellence in Customer Experience (CX) is often promoted as driving business performance. Jon Picoult of Watermark Consulting compared the total stock market returns of top ten (“leaders”) and bottom ten (“laggards”) publicly traded companies in Forrester Research’s annual Customer Experience Index ranking. He concluded that for 2007 to 2012, CX leaders “outperformed the broader market, generating a total return that was three times higher on average than the S&P 500 Index.”
The problem with this study, like many others, is that it’s just a simple correlation. While I agree that a better CX is probably one factor in business success, it doesn’t follow that CX is the only reason or the main “driver.” When I’ve examined practices of companies that lead their industries, I find they are more adept at a number of disciplines, including leveraging technology, process management, using analytics, and being more social or collaborative. In fact, most every popular management discipline can show a correlation to business performance.
Getting valid insights from data and analytics requires specialized training and a lot of experience. The expanding world of “big data,” an IT industry term meaning the increasing volume, velocity, and variety of digital information, has elevated the role of the so-called data scientist.
Using Analytics to Improve the Consumer Experience
Business leaders are turning to analytics to uncover insights in so-called big data. However, big data is like a vein of gold buried under your feet. Unless you can mine it effectively to improve business performance, all that data could be a worthless distraction.
Analytics is a terms applied broadly, perhaps too broadly. The most common form is descriptive analytics used to slice and dice data to understand what happened in the past. But increasingly attention is turning to forward-looking analytics, using specialized algorithms and software. Prescriptive analytics take it a step further and attempt to actually influence the future. For example, analytics can be used to help a call center agent decide the best offer to present to a customer to increase the odds of making a sale, or to suggest actions to deal with a service issue.
Macy’s is a great example of a major retailer competing for the loyalty of “omnichannel” shoppers—those using multiple channels, such as retail stores, websites, mobile devices, and even social media. Several years ago, the company began a customer-centric shift, led by Julie Bernard, group VP of customer centricity.
Speaking at a 2012 conference, Bernard said her goal was to “put the customer at the center of all decisions.” Sounds good, but old habits die hard in a 150-year-old brand where data was organized around products. The retailer used POS data to analyze product sales but couldn’t figure out what individual consumers were doing. One simple example: Did a spike in sales of a new pair of jeans mean the product was a hit or that one person bought all twelve pairs in a store?
By also looking at data from loyalty programs, credit cards, and other sources, Macy’s was able create a more complete understanding of the products, pricing, and experiences that move “loyals”—those consumers already buying regularly.
Let's look at another example in the world of e-commerce. Let’s say you want to present shoppers with hotel options in a major metropolitan area like New York. According to then Expedia VP Joe Megibow, most users won’t do a complex search of hundreds of hotels, so it’s critical that Expedia put the “best” options at the top of the list. If your instincts told you to present the cheapest or more popular hotels first, Expedia would frustrate a lot of shoppers and lose bookings.
Analytics determined the factors most likely to meet customer demand, such as real-time availability, inventory by class, rate deals, reviews, and purchase frequency. Then, using technology from an analytics software vendor, Expedia built a predictive analytics model based on the handful of factors that really mattered, out of about two dozen possibilities. The model was operationalized using Expedia’s own proprietary technology.
Result: When consumers search NY hotels, they’re more likely to find the hotels that they really want, and Expedia will get the sale. A great example of technology enabling a win-win.
Bob Thompson is an international authority on customer-centric business management who has researched and shaped leading industry trends since 1998. He is founder and CEO of CustomerThink Corporation, an independent research and publishing firm, and founder and editor-in-chief of CustomerThink.com, the world's largest online community dedicated to helping business leaders develop and implement customer-centric business strategies. His book Hooked on Customers (April 2014) available on Amazon UK here reveals the five habits of leading customer-centric firms.
For more information visit http://hookedoncustomers.com