Clear Ownership And Real-Time Context Push CX Toward Revenue Growth

For years, customer experience teams have faced a clear mandate to stop being a cost center and start driving revenue. The execution, however, has been messy. Companies are hoarding customer data and stockpiling digital tools, yet the payoff remains elusive. The bottleneck is a structural lack of ownership for turning those insights into action.

Jeff Lawrence, Country Manager for QuestionPro, sees this ambiguity at play constantly. With nearly two decades of experience spanning direct revenue roles and startups, Lawrence has seen firsthand how many companies focus on collecting more customer data when the real issue is that no one clearly owns the results.

"Most companies don’t actually have a data problem. They have an outcome ownership problem. Organizations already have mountains of customer insight. The challenge is deciding who owns what happens next." In his experience, moving the needle requires a focus on accountability rather than more tools.

• Honest investigation: Before deploying new software or adding another listening tool, Lawrence believes leaders need a truth-seeking mindset to diagnose their actual challenges. "The leaders doing this best are unafraid to examine the truth," he says. "They're humble enough and honest enough to dig in and actually find out the core challenges."

• Prescribing a cure: He likens this to a medical diagnosis, where a doctor can only prescribe a cure if the patient is willing to confront the symptoms and accept the test results. "From a doctor's perspective, you wouldn't want a patient who is so delusional they reject the data entirely. You have to come at it from a truth-seeking mindset."

That mindset matters more as CX moves deeper into AI-driven territory, where companies can easily pull together digital breadcrumbs that exist independently of direct feedback. Lawrence points to telecom and consumer tech as testing grounds where companies are blending behavioral, transactional, and third-party data to understand intent. "There are digital signals, on-app signals, and different ICPs built out over the years," Lawrence explains. "There are Reddit forums, open sources of information, and people walking in stores. There is a slew of data out there, and a lot of it can be dissected with AI."

• Finding the 'why': The next challenge Lawrence identifies is synthesizing the data in a way that unifies business functions. "Data usually sits in silos. Traditional CX teams come in when there's an issue. They'll measure whether customers are happy or sad, but they don't actually own the product. The far more important question is, 'Why is that the answer?'"

• Bridging the disconnect: He says transforming CX from a cost center to a revenue driver requires a mindset shift that enables a continuous feedback loop. "It's not them versus us. It's them and us. The companies that are really knocking this out of the park are the ones that are able to put all their data together, build those correlations, and orchestrate it."

Looking at the tools behind this orchestration, Lawrence sees more teams experimenting with a composable model, building custom layers on top of their own data. This puts pressure on data quality. "Bad data in, bad data out. Platforms will have to start taking that seriously." He says the usefulness of this approach depends heavily on how organizations manage security and privacy risks. "I foresee a lot more governance coming into play because agentic AI has to answer data security and privacy questions."

For Lawrence, the point of AI isn't simply to surface more insight, but to deliver it back to humans in a way that makes it easier to act. This is where journey management is key, as AI must distinguish between contexts in real time to deliver relevance. "If I'm flying alone for business, what I care about regarding airline, timing, and pricing is radically different than if I'm flying with my wife and three kids on vacation," he illustrates. In Lawrence's view, the competitive advantage won’t come from collecting more signals, but from understanding the context behind them quickly enough to act. "If you're not able to segment the individual from the journey they're on in real time, you miss the context entirely."