Customer Experience Optimization with Reporting and Analytics

As a customer experience executive, you likely have a well thought out vision of the ‘ideal experience’ for your customers. In fact, your vision is probably articulated in the form of goals that have been well socialized throughout your organization – particularly to the front line employees you depend on to implement that vision on a day-to-day basis. But do you have visibility into whether or not your customer experience goals are being met? Unfortunately, many organizations don’t. Despite having valid and realistic customer experience goals; they have no way of validating that customer experience goals are being met, or diagnosing the reasons why goals go unmet.

Well, this is where reporting and analytics come into play. Reporting and analytics capabilities can provide you with full visibility into your customers’ experiences, help you more quickly respond to negative experiences, and to implement policies and procedures that allow you to proactively prevent negative experiences before they occur.


Many think of reporting as synonymous with analytics. I tend to think of reporting as telling you ‘What has happened?’, or in the case of real-time reporting, ‘What is happening?’ In other words, reporting helps you to keep a ‘finger to the pulse’ of your customer support operations. Consider the questions below, which are just a few examples of questions a robust customer experience reporting capability can answer:

  1. What was the average customer wait time for our call centers last week? How does it compare across individual call centers?
  2. How many new customers did we bring onboard last quarter? Which of our marketing campaigns brought on the highest portion of new customers?
  3. What is our average customer satisfaction? Are there meaningful differences by region? What about by customer tenure?

As the examples here indicate, reporting can help you gain insights on your customers’ experiences and interactions with your organization throughout the customer lifecycle – from initial on-boarding through attrition. Not only can you get a full 360 profile of your customers’ experiences as they progress through the customer lifecycle; but you can remain aware of significant departures from the ‘ideal customer experience’ that you envision. These insights will give the knowledge you need to make changes to current systems and optimize the customer experience.


So what about analytics for optimizing the customer experience? Well, analytics helps you take things a few steps further. Whereas reporting can tell you ‘What happened?’ or ‘What is happening’, analytics can help you understand ‘why it happened’. Moreover, in the case of negative customer experiences, analytics can help you understand what actions to take to prevent – or minimize – the occurrence of negative events in the future.

Use Case Scenario for customer experience goals

Let’s take the first reporting example discussed above, on call center wait times. Let’s say that your weekly call center performance report reveals that 2 of your 5 call centers considerably underperformed in the way of ‘average customer wait time’ for the past week. And for both of these call centers, last week’s performance represented a significant deviation from historical performance. Through analytics, you can investigate the potential drivers of the increased customer wait times for these call centers, and statistically determine which of these factors – or what combination of factors – best explains wait time underperformance.

Now, this likely isn’t your first time analyzing the drivers that impact a metric as important as ‘customer wait time’. So, you have an idea of the factors that have historically impacted, or helped predict, customer wait time (e.g. total call volume, the ratio of call volume to call center agents, call type allocation, etc.). These historical factors provide a good starting point for your investigation of wait time underperformance.

Analyze this data to determine if these factors can better explain the reasons behind recent wait time underperformance. For instance, you may find that – although call volume for the two underperforming call centers was within normal parameters – the ratio of call volume to the number of agents on staff at each call center was considerably lower than usual on account of a new staffing plan having been implemented at each call center (Obviously, this staffing plan wasn’t informed by analytics). This may suggest a need to re-evaluate the staffing plan for those call centers, perhaps in conjunction with an interim step of routing fewer calls to those call centers until the staffing plan assessment is complete.

Customer experience optimization starts with visibility

In closing, I’ll say this – a well thought out vision of your ‘ideal customer experience’ throughout the customer lifecycle is a great start… but it’s only a start. Being in a position to actually validate the customers are in fact having this experience is absolutely critical. Through reporting and analytics, you can do just that.

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