Predictive Satisfaction Score

How a telecom provider forecasted satisfaction in real time—before customers were ever surveyed

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Too Late to Act

By the time satisfaction survey results came in, the damage was already done. Customers had dropped calls, visited stores, churned—or posted about it. Weeks later, a score would show up. But it was a postmortem, not a signal.

CX, Delayed

Customer experience data was reactive. Surveys captured how people felt—but long after they acted. Care teams couldn’t prioritize. Marketing couldn’t personalize. And operations couldn’t see emerging risks.

What If You Could Predict It?

What if you could anticipate satisfaction based on behavior—before feedback was even submitted? What if you could intervene early—before the next bill, visit, or cancellation?

So We Modeled the Score

We partnered with the telco’s CX, data science, and network teams to build a predictive model—one that estimated satisfaction scores using operational signals, usage patterns, and digital behaviors in near real time.

We Trained on 750K Customers

We used two years of NPS and CSAT data—linked to service usage, care interactions, call logs, network incidents, and mobile app activity. A supervised ML model was trained and validated on over 750,000 customers, predicting satisfaction with 86% accuracy at the segment level.

We Scored the Entire Base

Every active customer was assigned a predicted satisfaction score—updated weekly and mapped to key drivers. The client could now target low-predicted-sat customers with retention offers, support calls, or proactive fixes—before dissatisfaction escalated.

“Before, we were reacting. Now we’re in front of it. We can see risk coming, not just explain it after the fact.”

— VP, Customer Experience

We Integrated It Everywhere

The score was embedded into CRM systems, care dashboards, and marketing lists—used daily by agents, analysts, and journey teams. It became the new backbone of CX decisions across the enterprise.

Take the next step

Explore how Intellimark can help you anticipate customer sentiment—and act before it’s too late.

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The Outcome

Satisfaction became a leading indicator, not a lagging one:

    86% model accuracy across key segments

    750K customers modeled with weekly score updates

    14% churn reduction among at-risk groups reached proactively

    6 teams using the score daily across care, marketing, and analytics

Fin.

Knowing how customers feel is good. Knowing before they tell you? That changes the game.