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Starter Kit for Customer Experience Data Analytics 

Bill Price | Oct 20, 2016 550 views No Comments

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Applying data analytics to understand better what drives Customer Experience (CX) is a hot topic, but one that might appear to be a daunting undertaking across these four issues:

  • “Where do I start?”
  • “Do I need “Big Data” and, if so, what the heck is it?”
  • “We have solid spreadsheet junkies but do I also need “data scientists” and, if so, where can I get them?”
  • “How can I track progress?”

Let’s create a starter kit for CX data analytics to begin to address these and other critical issues in this month’s column, and add details in next month’s column.

1. “Where do I start?”

What was it that Maria said in The Sound of Music? “Let’s start at the very beginning; A very good place to start.” It’s important to figure out first what are your gnarly issues, the ones that keep you up at night and the ones that your execs keep asking, for which you don’t have clear answers.

Here are some of these vexing questions that my clients have been asking, covered as well in my two books The Best Service is No Service 1 and Your Customer Rules!2:

  • “How can we create an awesome customer experience across all of the touch points that we offer, and that our customers use to contact us?” This so-called “omni-channel” challenge typically derives from data that are silo’d in different data bases, including cases where the customer isn’t immediately recognized when she moves from the web site to an IVR and onto a customer service agent, or when she speaks to a bank teller.
  • “How can we hold onto our best performers?” This classic HR and retention dilemma stems from years of tribal knowledge that’s never been tested, like “We only need to pay our staff a little bit more than today” or “It’s all about having the right manager”. Possibly right, but there are so many more possible drivers to consider, and each company faces different amounts or degrees of these drivers. Here again we encounter multiple data sources or inadequate data points, e.g. very few exit interviews (and the ones that we do obtain are often self-serving, and not insightful).
  • “Why has our NPS declined, and what can we do to be better than Amazon?” OK, an unfair pairing of questions but admit it, haven’t you been confronted with questions like this from your executives? With NPS used (or misused) so much, and CX leaders such as Amazon often held up as role models or best practice, this issue is fraught with many variables across your customer segments and their experience with your brand and service offerings or products, how they have been treated recently and over time, and so much more – Again, some data known but not collected nor compared, and some data missing.

As I’ve suggested alongside each of these three questions, the next step is to inventory your data sources that might shed light on figuring out answers, or at least to decide where to begin your quest. Nothing magical here, something you’ve probably done many times in other areas. A handy checklist helps, something like this for VOC data sources, selected from the 26 different VOC data sources that we often inventory, such as focus group logs, post-purchase surveys, customer service agent notes, and Tweets.

  • If the data are collected today, who initiates data collection? At what frequency? What forms of data (text, speech, notes, surveys, etc)? Where is it stored, and who “owns” that storage? Who uses these data, and for what purposes?
  • And if so, to what extent do these data sources match “the customer journey”, as opposed to reside in separate repositories?
  • Who consumes these data, and for what purposes, how often, and in what forms?

But if the data are not collected today, yet appear to be useful, you need to start asking, “Where could we get what we need?”

You’re getting close to being able to address the second issue so let’s take a peek at it now.

“Do I need “Big Data” and, if so, what the heck is it?”

“Big Data” is often used very broadly to mean “lots of data” or “lots of complex stuff”, but let’s pause to see what Gartner says about it …

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.3

As well as what Harvard Business Review tells us about using Big Data:

Because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.4

So do you need “Big Data”? Most likely the answer is going to be “yes,” in different degrees based on your inventory and your ability to answer the three vexing questions that I posed at the start of this column. If your response to any of them is “Not sure” or “Don’t have many clues”, then you’re going to need the breadth and depth of “Big Data” and its ability to “mash up” current and new data sources in order to “enhance insight” and “decision making”.

We will dig more deeply next month into Big Data, data scientists, and how to track progress. Stay tuned!


1. The Best Service is No Service: How to Liberate Your Customers From Customer Service, Keep Them Happy, and Control Costs Bill Price & David Jaffe (Wiley 2008). Based partly on my years as Amazon’s 1st Worldwide VP of Customer Service, but also on “Best Service” providers around the world who have made it easier for their customers to do business with them, we proposed 7 Drivers that start with “Challenge demand for service”:

  1. “Eliminate dumb contacts”
  2. “Create engaging self-service”
  3. “Be proactive”
  4. “Make it really easy to contact your company”
  5. “Own the actions across the company”
  6. “Listen and act”
  7. “Deliver great service experiences”

2. Your Customer Rules! Delivering the Me2B Experiences That Today’s Customers Demand, Bill Price & David Jaffe (Wiley 2015). Based on original research into 12 recognized CX leaders there are the 7 Customer Needs that produce a winning “Me2B” culture, with a total of 39 sub-needs:

  1. “You know me, you remember me”
  2. “You give me choices”
  3. “You make it easy for me”
  4. “You value me”
  5. “You trust me”
  6. “You surprise me with stuff that I can’t imagine”
  7. “You help me do better, you help me do more”

3. http://www.gartner.com/it-glossary/big-data/ accessed 19 October 2016

4. https://hbr.org/2012/10/big-data-the-management-revolution accessed 19 October 2016

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