Five big ideas to profit from analytics and Big Data

Using customer scenarios, Bob Thompson investigates how data analytics has helped a host of companies to improve their customer picture in good times and bad

BIG IDEA 3: Personalise the E-User Experience

Put yourself in the shoes of Joe Megibow, vice-president and general manager of Expedia US, which serves millions of travel shoppers each month. Let's say you want to present shoppers with 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 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 an analytics software vendor, Megibow says the company built a predictive analytics model based on the handful of factors that really mattered, out of about two dozen possibilities. Expedia then operationalised the model using its own proprietary technology. Result: when a consumers searches on NY hotels, they're more likely to get the hotels they really want. Another interesting example is LinkedIn, which accumulates a massive amount of data as its users interact. Speaking at the marketing conference in 2012, senior data scientist, Scott Nicholson, said the fir, can use that data for the benefit of users (such as finding future job opportunities) or for LinkedIn (e.g. present the right ads for monetisation). What complicates matters is that LinkedIn offers lots of choices – 40 different actions according to Nicholson. Using analytic techniques too complex to discuss here, LinkedIn can serve up experiences that are more personalised to the user, or ads more likely to be clicked on.

Analytics can also be used to optimise email marketing. Gilt Group's Tamara Gruzbarg says the retailer is using analytics to influence their merchandising and promotion strategies. The heritage of the retailer is upscale urban fashionistas, but as the company has grown and expanded it has become more challenging to make smart decisions. Email remains a critical promotion channel even as users adopt mobile devices. One predictive model that paid off helped Gilt tune the email frequency based on engagement and age indicators, to maximise revenue while minimise unsubscribe rates.

BIG IDEA 4: Mine Insights from Unstructured Text and Speech

If you've eaten at a restaurant or shopped at a store lately, you may have seen an invitation on your receipt to call a toll-free number and respond to a survey about your recent experience. Surveys are now commonplace tools for companies to solicit customer feedback. Most surveys allow customers to add their comments. Other sources of text feedback include call centre agent logs, transcripts from recorded phone calls, mobile (SMS) text messages, email messages, chat sessions and posts on discussion forums or blogs.

Text mining can be used to 1) determine what the original author was trying to say or 2) learn something completely new. Typically the goal is to identify the topics or categories in the text, such as products and service problems. Taking it a step further, it's also possible to use ‘sentiment analysis’ to determine whether written text has a positive, negative or neutral tone.

Garden Fresh Restaurant, operating a chain of over 100 buffet-style restaurants in the US, wanted to take better advantage of customer feedback in transcribed phone survey comments. It was cost prohibitive to manually process about 10,000 pieces of unstructured text a month, so Garden Fresh worked with a text mining vendor to find meaning in all that feedback. As a result, it created new monthly praise and complaint reports, showing important trends and drilling down to individual customer comments. One insight mined from customer comments resulted in an expansion of the soup varieties offered.

At JetBlue, text mining was introduced as a result of the infamous New York ice storm of 2007. After being overwhelmed with 15,000 emails in just two day, a text mining vendor helped the airline learn that customers were upset about the delays and cancellations, and disappointed JetBlue didn't have a backup plan. Since that crisis, JetBlue has worked to more systematically mine customer sentiment, as well as providing "tangible data around how to augment JetBlue services", according to the airline's customer feedback analyst, Bryan Jeppsen. By tying feedback data to a specific aircraft or even a seat number, JetBlue can find and fix problems that have a direct impact on the customer experience. If you have called a customer service department for help, you probably heard a message like this: ‘Your call may be recorded for quality assurance’. Recorded speech is yet another form of unstructured information with valuable insights.

For example, Blue Cross of Northeastern Pennsylvania used speech analytics to understand the reasons for extremely high call volumes. According to customer service director, Bob McDonald, it was able to validate that, in one case calls were the result of something anticipated – a recent system change. The data gave him "ammunition that the problem really needed fixing", he said. In another case, McDonald discovered, much to his surprise, that customers were circumventing processes to get faster service. Armed with this insight, McDonald changed the call flow and improved agent training.

BIG IDEA 5: Harmonise the Cross-Channel Service Experience

One of the more advanced uses of analytics is optimising cross-channel customer journey. Interaction data is usually managed in silos, and you can't easily get a complete picture of what's happening as a customer navigates multiple channels. CustomerThink's 2009 study found consumers exposed to companies suffering from ‘touchpoint amnesia’ (requiring customers to repeat information during a multi-touchpoint experience) were 50 per cent less likely to recommend that company.

In 2007, the mobile telecom company Sprint achieved unwanted notoriety by firing its unprofitable customers for making excessive support calls. Unfortunately, leaders failed to account for the media backlash. Worse, firing bad customers didn't address the core issues of why those customers were calling and therefore unprofitable.

Sprint engineered a turnaround by systematically uncovering and fixing customer service problems. The process took a couple of years and required top management to get serious about improving the customer experience. On a CustomerThink webinar, Lance Williams of Sprint Nextel explained that in 2008 Sprint had the worst IVR customer satisfaction in the industry. The company used cross-channel analytics to understand why customers were abandoning the IVR to call the agents – a frustrating experience for customers and a very expensive issue for Sprint. After improving customer usability, by Q4 2009 Sprint's IVR CSAT was leading the industry. That helped the contact centre to contain (complete interactions in the IVR) "tens of millions more calls" in 2009 as compared to 2008. Translation: huge cost savings.

Sprint's improvement has been impressive. In 2008, Sprint's ACSI score (a measure of overall satisfaction and loyalty) was a dismal 56 vs. an industry average of 68. By 2012, Sprint's score had improved to 71, a point above the industry average and competitive with other major mobile operators.

The use of cross-channel analytics is becoming more accepted now, but justification can be challenging when managers fight over budgets. One large technology firm told me it took about three years for management to get fully on board and finally commit some real budget. Once they did, analytics showed the true cost of bad cross-channel experiences in agent call time savings. Similar to Sprint, poorly designed IVR systems forced customers to call an agent for help. This degraded the customer's experience and wasted agent resources.

Towards Better Decisions

What makes Big Data interesting is the new types of information such as website clickstream data, social media posts, video surveillance feeds and even sensor data from consumer products. These new forms of data definitely pump up the volume, requiring new data storage techniques such as Hadoop. And, there's probably a tool that can analyse any data you can collect.

However, more data (big or otherwise) doesn't necessarily mean better decisions. The key is picking right decisions, says James Taylor of Decision Management Solutions. 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 Julie Bernard at Macy's and Joe Megibow at LinkedIn who focus on key decisions that improve the customer experience and increase profitable revenue.

Further reading: The Origins of ‘Big Data’: An Etymological Detective Story (NY Times).

This article is based on Bob Thompson's e-book How Customer-Centric Leaders THINK Clearly with Analytics and Big Data, available for free download to CustomerThink members. Republished with permission from Copyright CustomerThink Corp.

Bob Thompson is CEO of CustomerThink, an independent research and publishing firm focused on customer-centric business management, and founder/editor-in-chief of, the world's largest community dedicated to customer-centric business. Thompson is a popular keynote speaker, blogger and author of numerous reports, articles and papers including CrowdService: Harnessing the Wisdom of Crowds in Customer Service and Support.

Follow CMO on Twitter: @CMOAustralia.

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