From Big Data to Big Decisions: Three Ways Analytics Can Improve the Retail Experience


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Despite our best efforts to collect and analyze data, good business decisions will always include elements of judgment, intuition or just plain luck. Many day-to-day decisions are made with little or no thought, because the option selected just seems “right.” Gut-feel decisions might be examples of what Malcolm Gladwell called “thin-slicing” in his provocative 2005 bestseller Blink.

However, the best decision can sometimes be counter-intuitive. For example, the financial services firm Assurant Solutions wanted to improve its “save” rate on customers calling in to cancel their protection insurance. The industry’s conventional wisdom, which resulted in 15-16% retention rates, was to focus on reducing wait time to boost customer satisfaction. But data analysis found a solution that tripled the retention rate: matching customer service reps with customers based on rapport and affinity.

The question is not about tools or even data. It’s about picking the right decisions.

Now business leaders are turning to analytics to uncover insights in so-called Big Data, the latest IT industry buzzword to hype the increasing volume, velocity, and variety of digital information. In 2011, researcher IDC found that the world’s information is doubling every two years. Last year alone that means about 1.8 zettabytes was created. Don’t know what a zettabyte is? Me neither. But 1.8 zettabytes is the equivalent of 200 billion HD movies. Does that help?

Said another way, Big Data is like a vein of gold buried under your feet. Unless you can “mine” it effectively to improve business performance, big data is a worthless distraction. Here are three examples of how the power of analytics applied to Big Data can help retail leaders make Big Decisions to improve the consumer experience.

  • Improving the Online Shopping Experience at Expedia

    Put yourself in the shoes of Joe Megibow, VP and General Manager, Expedia US, which serves millions of travel shoppers each month. is a complex web site. What’s the best way to improve the user experience and increase the percentage of shoppers that book online?

    Too much? OK, let’s narrow the problem down a bit. What if you want to present shoppers with the hotel options in the New York Area. Megibow says most users won’t do a complex search of the roughly 800 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. That’s because the options most likely to meet customer demand depend on a number of factors like real-time availability, inventory by class, rate deals, reviews, purchase frequency and more.

    Using technology from analytics powerhouse SAS, Megibow says they built a predictive analytics model based on a handful of factors that really mattered, out of about two dozen possibilities. Then they operationalized the model using their own proprietary technology, so that when a consumers searches on NY hotels, they’re more likely to get what they want.

  • Macy’s Journey from “Mad Men” to “Math Men”

    Macy’s is a great example of a major retailer competing for the loyalty of omichannel shoppers—those using multiple channels such as retail stores, web sites, mobile devices and even social media. Five years ago, the company began a shift from product- to customer-focus, led by Julie Bernard, Group VP of Customer Centricity.

    Speaking at a Forrester 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 12 pairs in a store?

    Customer Centricity at Macy’s

    1. CEO sponsorship for the on-going use of customer data
    2. Data analyzed to guide strategic customer focus
    3. Data organized into customer languages to unify the organization
    4. Data leveraged to inform customer insight activations

    Source: Macy’s (Forrester’s Customer Intelligence Forum 2012)

    Initially, Bernard’s data-based attempts at busting myths about consumer preferences were largely ignored, until CEO Terry Lundren got more personally involved as a self-appointed Chief Customer Officer. You can see in the box to the right that Macy’s takes a data/analytics approach to “customer-centricity,” and that CEO sponsorship is crucial.

    By also looking at data from loyalty program, 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. In the future, Bernard thinks analytics can also help the retailer make smarter decisions about the $40B spent annually on merchandise, a much larger expenditure than marketing.

  • Best Western Pioneers Social Feedback Management

    The previous examples have shown how analytics can help retailers present more targeted offers to loyal customers (Macy’s) or deliver a more engaging and profitable online shopping experience (Expedia). But let’s not overlook another key strategy for a retailer that wants to stand out: Listening. Hear again Big Data can help, because the Social Web is a veritable gold mine of information about what customers like, or don’t.

    That’s what Best Western International (BWI) has been trying to do the past few years. The hotel brand currently has over 4,000 hotels worldwide, each independently owned and managed. One of the problems that BWI has struggled with is what to do about negative reviews. It’s not enough to have someone in marketing monitor brand buzz. The real issue is closing the loop with a consumer who has posted a negative review, before it can do long-term damage.

    In 2007, Best Western launched a customer care training program called “I Care” for its North American hotels. Later, the program was expanded to help international members, and an integrated feedback management solution from Medallia was implemented to deliver surveys, analyze responses and distribute feedback to hotel managers.

    But this only addressed solicited, survey-based feedback. Unsolicited social media feedback—on review sites like TripAdvisor but also Facebook, Twitter, and many more—started as a trickle a few years ago, but quickly turned into a torrent. BWI collaborated with Medallia to develop a new “social feedback” solution. Now if a Best Western guest posts about a bad experience on TripAdvisor, the system harvests the data, associates it with a specific hotel, then sends an email alert to the hotel manager. The manager can then use Medallia to read the review and respond to TripAdvisor via an integration.

Bigger Data Doesn’t Mean Better Decisions

So is Big Data really all that big? The truth is that the volume of data has always been bigger than our ability to store and analyze. What makes Big Data most interesting now is the new types such as website clickstream data, social media posts, video surveillance feeds and even sensor data from consumer products.

Analytics expert Karl Rexer explains in a recent CustomerThink interview:

For some of our clients, we certainly have analyzed over a million US tax returns or tens of millions of bank transactions or grocery store transactions. Now, to us those seem like big datasets, and so to us that seems in a way to be big data and big data analysis. But if you were Google or Facebook, Amazon or looking at web traffic, or if you’re in a scientific field looking at some astronomy data or some genome research, you might have data that’s much larger and different. Sometimes it’s wide, in terms of lots of columns, or very deep in terms of the number of rows, and so other people’s data might be far larger than the datasets that we’ve been using.

These new forms of data definitely pump up the volume, requiring new data storage techniques such as Hadoop. But bigger data doesn’t necessarily mean better decisions. Wilson Raj, Global Customer Intelligence Director at SAS says that “20% of available data will yield 80% of predictive insight.”

There’s probably a tool that can analyze any data you can collect. The question is not about tools or even data. It’s about picking the right decisions, says James Taylor of Decision Management Solutions. He says the biggest mistake is to start with the data or the technology, rather than the decision. “Big Data projects should focus on how to improve how we run the company,” advises Taylor.

A recurring theme from industry experts is the importance of knowing what’s possible. While so-called data scientists are emerging high-impact positions designed to mine Big Data effectively, I believe the real leverage is in data strategists. These are business leaders like Bernard and Megibow who focus on key decisions that improve the customer experience and/or increase profitable revenue.

Everyone seems to believe that “thar’s gold in them-thar hills.” I do wonder if the excitement around Big Data will follow the same path as the Great Gold Rush of the 1850s. Miners flocked to California in search of gold, but most came up empty. The companies that made money were the suppliers of picks, shovels, and what came to be known as Levi’s jeans.

To sum up, Big Data is a big opportunity in retail, but the challenge is focusing analytics on the Big Decisions customers will care about. Otherwise, you’ll end up with fool’s gold, not the real thing.


  1. Thanks for another great post Bob.
    These are great examples of how BigData can be used effectively to improve customer centricity and deliver the best customer experience possible. Whether it’s saving switchers from leaving, to increasing spend, Big Data can really help.
    With one of my partners, we managed to increase customers' annual spend by 15% for a US retailer, whilst growing the number of categories bought by 42% and saving 13% in promotions and discounts.
    How’s that for a triple win?!

  2. One of the most interesting consequences of thinking about decisions first is that the decisions we come up with are often “little” – decisions about an individual customer or transaction, operational decisions. These little decisions are the key place to apply big data. Or as I said on my blog recently, Big Data, Little Decisions

  3. Thanks for your excellent post Bob, which focuses on knowing what you want to use big data for. So to explain why I like the term thinslicing in this context, first take a look at the cool piece about data interpretation written by Lithium’s Dr Michael Wu.

    Then consider this, that my response to reading his blog post clarified a key thing I have been trying to say. Firstly, that I’ve come to term the business objective of finding the “interpretable, relevant and novel” in data as Michael terms it – namely that of thinslicing.

    But now I’ve made the next step. Identifying the strategic value of thinslicing lies in the elegant and powerful way the term thinslicing connects the approach to data analytics to the behaviour that creates that data – namely with the thinslicing of online consumers who “tend to ignore most information available and instead 'slice off' a few relevant information or behavioral cues that are often social to make intuitive decisions,” as Brian Solis puts it.

    I don’t think this connection is easy to achieve, but equipped with such a thinslicing mindset can help you organise your business’s analytical activity to help make the most intelligent use of your social data.


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