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Turn Your Operational Data Inside Out to Understand Your Customer Experience

Data for Customer Experience (CX) is like pie for me – I always want some, I always want more, and no matter the flavor, whatever pie (sorry . . . data) you have will be just fine. CX leaders often want the “perfect” data sources to generate metrics that describe and tell the seamless description of a customer interaction along a customer journey touchpoint. The problem for CX leaders is that data sources which cleanly and clearly describe the customer interaction from the customer’s perspective rarely exist in the quantity, detail, and accessibility that CX leaders need. Just like pie, we cannot always get apple, sometimes we need to take a piece of blueberry and make it work.

Operational data sources found in the functional areas of orders, logistics, accounting, finance, customer service, communication records, and IT problem logs offer effective sources for CX metrics.  The transformation of turning operational data inside out will make it useful for a CX analysis. CX leaders must understand that while your operational data may not be “perfect”, it will be more than adequate to make a positive impact on your CX program.

Follow these guidelines for making the most of your operational data to describe and to improve the customer experience.

GUIDELINE 1 – Use Non-Analytic Sources to Identify Customer Problem Areas. One of my constant temptations is that when I get a data set, I start checking the data distributions, graphing, and looking for outliers. That is a mistake. I need to ask if the data I have describes my customer’s primary challenges.

The critical questions CX leaders need to ask are:

  1. What are my customer’s challenge, issues, and priorities?
  2. Where can I find existing qualitative data sources that confirm my customer problems?
  3. Does the data that I have help me to understand CX issues from the customer’s perspective?

CX leaders need to turn to traditional CX tools such as VoC interviews, survey analysis, persona research, and journey maps to find precise customer problems. Once the customer problems are found, then you can turn to bringing operational data sets to life and that highlight the customer perspective.

GUIDELINE 2 – Turn the Operational Data Inside Out to Find the Customer Experience. Ordering, shipping, and delivery have been a series of constant challenges over the past 2 ½ years as the economy was transformed in both B2B and B2C with new purchase and logistics requirements. The vast majority of operational databases were designed to capture, organize, invoice, track, and fulfill customer orders.  Operational databases can tell us a good deal about the quality of the customer experience when we turn the data around from the customer perspective.

Below are a few metric examples how to employ operational data that can be transformed to tell the customer experience.

  • Original Order Quantity by Item Vs. Delivered Order Quantity by Item. When a customer orders an item with a specific quantity how does that compare to the original order quantity? For the customer, an order is a promise from the organization to meet their needs. CX analysis using order data from the the customer’s perspective is, “What was I promised and what did I get?” If there is a mismatch from the customer, unapproved substitutions, or shortfalls, this is creating CX challenges that must be addressed.  Remember, even if your orders are 99% accurate, you still have 1% of your orders with a disappointed customer.
  • Original Delivery Date Vs. Actual Delivery Date. As important as the ordered item and the ordered item quantity is meeting the original delivery date for the customer. The transportation delivery promise is critical for the customer because if a customer cannot receive their product, then a customer interaction designed to create trust and confidence has not been accomplished. Additional importance must be applied to delivery date and rescheduled delivery date accuracy. If an adjusted delivery date is inaccurate or non-existent, then another CX problem has been created.
  • Order Change Notification Date Vs. Original / Adjusted Order Delivery Date. Order adjustments happen, but how proactive and consistent was the organization notifying customers of the change: 7 days, 2 days, or never? If a customer expects an order and the order status update is inaccurate, inconsistently delivered, or non-existent then a poor order delivery CX has been amplified by a poor customer communication experience.

GUIDELINE 3 – How Do the Metrics Look Like in a Time Series of Events? Is There Wide Variation in Customer Journey Touchpoints? Great CX is a consistent delivery of a positive customer journey touchpoints from the BUY stage, through OWN activities, and concluding with ADVOCACY. Operational data should be used to track the performance of a customer touchpoint.  CX leaders must look at the consistency of execution along a series of touchpoints using a time series analysis approach. For example, if a customer has one item substituted out of several in an order this can still result in a positive CX. But, if I have a substitution AND the order is late AND I have a billing error, then the series of consecutive adverse events along an order is almost universally a CX failure. Instead of looking at functional area performance, look along an entire order (using the order tracking number) at all customer touchpoints to evaluate the true CX quality experienced by the customer.  Additionally, look at functional area performance variation (recommend standard deviation of a process) along different orders for the same customer.  If delivery took 3 days for an item and then 10 days for a similar item, then a potential CX disappointment exists.

Gartner – The Buy/Own/Advocate Journey

Operational data is a leverage point to design and transform your CX programs. Leverage and transform the operational data that you have to deliver effective CX programs today.

Turn Your Operational Data Inside Out to Understand Your Customer Experience

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