Experience management (XM) involves the measurement and management of two types of data — operational (O-data) and experience (X-data). O-data, like sales, revenue, customer renewal rates, and employee attrition rates are major factors in organizational decision making. This is why nearly all large organizations have robust O-data systems in place today.
Modern O-data systems seamlessly collect information in real-time, (normally) without disrupting employee or customer experiences. For example, in a call center, data on call length, the systems that representatives use to solve customer issues, and the demographics of the customer are collected automatically. The customer and the representative have to do very little, if any, work to collect and record these data.
But O-data, alone, cannot answer some of the most foundational questions central to XM. Consider common questions like:
- Why are customer renewal rates dropping?
- Which potential product features will our customers prefer?
- What do our employees and customers expect from our service offerings?
Organizations need X-data to answer questions like these. Unlike most O-data, X-data, such as employee or customer perceptions, attitudes, feelings, expectations, and preferences, are commonly solicited and volunteered. Because of this, X-data is generally more onerous to collect. This is likely one of the reasons why O-data systems are more mature than X-data systems and why O-data is more plentiful.
But this raises a question that has intrigued me over the last couple of years – can we modernize X-data systems to operate more like O-data systems? After much contemplation and many conversations with XM professionals across the globe, my answer is an emphatic, yes! Here are four characteristics of O-data systems you can apply to modernize your X-data systems.
1) Prioritize X-data collection around employee and customer journeys
Today, most customer and employee X-data systems are heavily anchored on relational studies that are designed to measure a universe of potential experience drivers. As such, they tend to be lengthy and happen at arbitrary times for the customers and employees receiving them. To be clear, these tools have their place within a holistic X-data system, but organizations should shift toward a higher proportion of transactional and journey-based feedback. To get started, introduce measures of important employee journeys, such as recruitment, onboarding, and pivotal digital experiences, and of high value or high volume customer interactions. In general, the more we orient X-data measurement around what’s important to our stakeholders, the more X-data we can collect and the more useful the insights.
2) Seamlessly integrate X-data collection into the experience
Most traditional employee and customer surveys are physically and temporally separated from the experience(s) they measure. But this is often not ideal nor necessary. For example, many of us are familiar with filling out a short post-ride survey after we complete a trip with a rideshare. As consumers, we are far more likely to complete these feedback requests than if we received them via email a day later. That is in part because the act of giving feedback is seamlessly integrated into the rideshare experience. While it isn’t always possible (or necessary) to integrate feedback into every experience, there are always opportunities to improve in this area. To kick start this, consider questions like – if the feedback is solicited via email, are there other, more natural channels to collect employee or customer feedback? Can we reduce the time between the experience and the request for feedback?
3) Instrument unstructured listening elements
A large proportion of the X-data that organizations collect today is solicited through formal surveys. But organizations should complement X-data gathered through formal surveys with unstructured and even passive listening elements. For example, Ford leverages an ask-listen-observe model to generate insights about its workforce. This model includes the collection of behavioral, O-data (observe), X-data through traditional surveys (ask) as well as unstructured and unsolicited listening (listen). A great place to start is to look for opportunities to incorporate always-on listening where customers and employees determine if and when to provide feedback. Other techniques and tools such as social media scraping and natural language processing can produce X-data that is collected automatically and passively, much like O-data often is.
4) Make X- and O-data physically adjacent for leaders
Once O- and X-data are collected, they are often consumed and treated differently. While leaders may be able to view O-data in real-time through easily accessible dashboards in familiar work systems, X-data are often pushed through separate systems or even static reports. Often, leaders may regularly use O-data insights to make daily decisions, while they may only review X-data insights when it’s time to create an annual “action plan.” This doesn’t have to be the case. Modern technology enables organizations to juxtapose X- and O-data, inherently enhancing the perceived value and actionability of X-data. To progress in this area, start by structuring O- and X-data in similar formats, reporting on them at similar intervals, and presenting complimentary X-data to support business-critical O-data.
There is much we can learn from the way O-data is measured, managed, and consumed to modernize X-data systems. This is a small, yet essential part of modernizing a holistic XM effort.
Benjamin Granger, Ph.D., is an XM Catalyst with the Qualtrics XM Institute