Analytics · Predictive

Predictive
Satisfaction Score

Predict customer satisfaction before feedback is collected, and intervene before issues escalate.

Predicted satisfaction

Near real time

Predicted avg score

72.9/100

Predicted NPS

+42

Risk distribution

Low risk 40% Medium risk 25% High risk 35%

Top satisfaction drivers

Product Stability 0.32
Support Responsiveness 0.25
Onboarding Experience 0.18
Feature Completeness 0.15
Billing Transparency 0.08

Illustrative output. Real models are built on your data.

Overview

Stop waiting for the survey

Surveys only reach the customers who answer, and only after the experience has already happened. Predictive Satisfaction Score uses behavioral, operational, and interaction data to estimate how every customer feels right now, so you can act before dissatisfaction turns into churn.

The silent majority

Most unhappy customers never say a word. They simply leave. Prediction surfaces them while you can still act.

What you get

  • Predicted satisfaction score for every customer
  • At-risk customers surfaced before they churn
  • Driver-level insight into what moves satisfaction
  • Segment and priority analysis
  • Integration into your CRM and retention workflows

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Unified survey, behavioral, and operational dataset

Real-time scoring and risk visibility

Driver analysis behind every prediction

Continuous scoring across the customer base

How it works

From signals to early warning

01

Unify

Combine survey, behavioral, and operational signals into one dataset.

02

Predict

Score satisfaction and risk continuously, without waiting for a survey.

03

Explain

Surface the drivers behind each prediction so action is obvious.

04

Intervene

Route at-risk customers into outreach and retention workflows.

Market reality

Why this matters now

96%

of dissatisfied customers never complain directly, but many silently churn

Source: Esteban Kolsky

67%

of churn is preventable if issues are resolved in the journey

Source: ThinkJar

Know who is at risk before they tell you

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