The healthiest-looking account in your book of business might be the riskiest one.

Key takeaways

• Operational data shows what an account is doing. It can't show what the people behind it think.
• Sentiment without a sense of scale reads as noise. Attach account value to it, and it becomes the earliest warning available.
• Walker’s Account Health Intelligence Agent turns sentiment into a number: revenue actually at risk. 

On paper, an account looks healthy. Revenue is up. Usage across stakeholders is steady. Support tickets are flat. 

But those signals only show what actions are being made. They don't show what the people behind it think.

When sentiment does surface—an NPS dip, a pointed comment on a survey—it rarely comes with a sense of scale—how big is the account? And what does a change in reported experience mean in terms of revenue risk or potential? Different stakeholders within the same account can respond differently, shaped by their own priorities and experiences—when voices compete, which one carries the most weight? 

Without knowing how much value is tied to an account, or which voice matters most, sentiment reads as noise, not signal. That gap is expensive. A 5% improvement in customer retention can raise profits by 25% to 95%.¹ 

That's too much revenue to leave riding on a guess.

The real question isn't whether an account looks healthy. It's which accounts need protecting—and which are ready to grow.

Answering that takes two things most companies don't have:

• Whether experience data confirms what behavior and operational data already show—or reveals a mismatch.
• The business value, in revenue, of changes in sentiment.

Most data stacks weren't built to hold both. Operational data—CRM records, contracts, usage logs—centralizes easily. Experience data is unstructured, inconsistent across surveys and hard to roll up to the account level.

So it stays in separate systems and gets shared in reports. Provided. Referenced occasionally. And then forgotten. And it stays disconnected from the systems that drive day-to-day decisions.

Without experience data, one account appeared to be at-risk out of several hundred, with revenue looking secure. Add experience data, and the number jumped to 100 accounts, with $16 million in revenue suddenly showing as uncertain.

That didn’t happen because of one isolated bad signal. In some accounts, usage held steady because switching felt expensive, not because anyone was happy. In others, the champion who liked the product moved on—and their replacement inherited a tool they never chose, using it out of necessity while saying otherwise on the next survey.

Same analyst. Same query. The only variable was whether sentiment was in the data.

That's a mechanism, not a case result. It shows what happens when experience data joins the rest of the stack, not a claim about what any one customer’s account base will show.

Walker's Account Health Intelligence Agent unifies operational, behavioral and experience data inside an existing Snowflake or Databricks environment, so a plain-language question about account risk returns a direct answer instead of a support ticket.

At the center is the organization's own data cloud. Walker connects experience data into that same environment, alongside what's already there. Ask where the money's at risk, which ten accounts customer success should work first, or what predicts churn 60 to 90 days out—the agent answers directly.

Not every stakeholder's sentiment carries equal weight. An account executive with a large pipeline attached to a relationship is a different signal than a single end-user comment. Walker can layer role-weighting on top of the core build for organizations that want that distinction made explicit.

Data never leaves the organization's environment to make this work, and the agent's recommendations aren't final calls. It runs natively inside the same Snowflake or Databricks environment, under whatever access and governance rules are already in place—every output is a starting point, not a verdict.

The versatility is the point. The same agent renders as a dashboard for the team that wants a morning glance, and answers a specific question on demand for the team that doesn't. One system. Two ways in.

Risk isn't the only thing hiding in the data. The same combination of usage patterns and experience signals also surfaces which accounts—or industries—are ready to grow, sorted by opportunity type—upsell, cross-sell, adoption gap—instead of by customers who happen to be top of mind that week.

The question shifts from who's happy to who's ready. That's a more useful question for a sales team to build a plan around.

The account that looks perfect on paper and the account that's about to churn can look identical, right up until someone asks the right question in a system built to hold the whole picture.

Watch the full walkthrough below—worked live in Snowflake and Databricks. If you’re interested in how this could apply for you, we could also have a 15-minute chat about your needs.

¹ Qualtrics, "30 Statistics About Customer Churn," 2026.

Additional Resources

Demo in Databricks.

Demo in Snowflake.