Contemporary Analysis

Data Science

Grant Stanley

Customer Experience Metrics

How can you measure the effectiveness of your customer experience? What impact does it have on profitability, loyalty and purchasing activity?  Companies are increasing relying on their customer experience to differentiate themselves in increasingly competitive markets. In 2008, 64% of companies listed Customer Experience as having a critical role in their strategy (Forrester Research 2008).

Creating a smart customer experience can be hard enough.  Measuring your customers's experience can be even more challenging.  Customer Experience Metrics have to go beyond traditional call center metrics to capture customer loyalty, satisfaction.  They have to measure intangible concepts, complex interactions, over large amounts of time.  Need help?

Examples of Customer Experience Metrics Include:

  1. Channel of Choice: How often can customers effectively resolve their issue in their channel of choice.  For example, if I start online can I finish answering my question online.  This also should include how many times someone has to be transferred to a new representative.
  2. One Right Answer:  Your customers love quick answers.  Is your knowledge management system designed to make it easy?  Too many knowledge management systems provide pages of search results.  Returning many search results might be great for Google, but your reps need answer fast.  Your Customer Experience depends on it.  How often can your representatives find the one right answer to your customers issues/questions? One Right Answer metric provides you insight into often and how quickly representatives can find the one right answer, instead of a list of potential answers.
  3. Customer Sentiment: Knowing how your customers are feeling is key to delivering a great customer experience.  Using natural language processing and surveys gives insight into what customer experiences build and erode customer loyalty and profitability.  When to measure this insight depends upon which company you ask.   Wufoo asks customers how they are feeling before an interaction, Marriott collects customer sentiment during interactions, and Apple asks customers how likely they are to recommend Apple after each interaction.  No one approach is best, you just need to measure how your customers are feeling.
  4. Predictive Metrics: Knowing what your customers are most likely to do in the future used to be only a dream of science fiction.  Now, with Big Data and Data Science, this has become possible.  Predictive metrics allow you to get ahead of your customers, and know what they are most likely to do next.  This allows you to not only be where the customers are, but where they are going to be as well.  Imagine knowing who is most likely to leave, who could be made more profitable, who is most likely to reffer friends, and who is most likely to buy what next.  It will allow you to offer customer service better and treat you customers they way they want to be treated.  Are you interested in using predictive metrics to go beyond simple customer experience metrics, learn more?

But where do you start?

1. Define Your Own Customer Experience
Your customers are unique, and so is the customer experience they want.  You can't use the same metrics as every other company. You have to be unique.  Start by ranking the qualities that are most important to your customers, define what your unique customer experience is, and what you want it to be.

For example if you customers care most about price and care less about agents exceeding their exceptions, as long as expectations are met, creating a WOW event most likely won't  add any addition value.  It might actually frustrate customers that just want cost effective solutions.  Developing a great customer experience starts by knowing who your customers are and what they want.

2. Go Beyond One Dimensional Metrics
Your Customer Experience Metrics need to be multidimensional. Each metric needs context.  Metrics with context allows you to take action. Make sure your metrics are connected to a time, representative, product, issue, channel. Also, avoid averages. They can hide important details. Instead use histograms. They will allow you to see the distribution of results, not just an average.  This will allow you to identify outliers, and remember it is outliers that people remember.  No one tells their friends about their average customer experience.

3. Work at Your Customer's Pace
Allow your customers to provide feedback at their pace.  If you need to survey your customers, avoid automated phone surveys.  Instead have representatives work questions into the call, or send customers a survey via email or text.  This will allow customers to set the pace, and answer when it seems relevant.  This is relevant when it might be days before a customer knows whether the representative actually resolved their issue.  Interactive Voice Response (IVR) doesn't allow customers to move at their own pace, as an email, text or person does.

4. Customer Experience Metrics = Sales + Marketing + Customer Service
Developing a Smart Customer Experience requires that you engage sales, marketing and customer service.  The metrics should also be shared throughout the organization so that they impact sales, marketing (including product development), and customer service.  You don't want a pushy sales person, a poor product, a missed message, or ineffective call center experience to ruin the competitive advantage you have tried so hard to build.

I hope that you have found this post insightful.  Would you like to learn more about how CAN can help you develop a smart customer experience?  Using predictive analytics we can determine which customers are most likely to leave and why, which customers could be made more profitable and how, and which customers are most likely to buy what times?

Thoughts? Post a comment.

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