AI Decision Block in Customer Experience Scoring & Churn Prediction

Purpose

This workflow converts raw, free-text customer-support logs into an executive view of customer experience and retention risk. Using Omniscope’s AI Decision blocks, it automatically reads every customer conversation, reconstructs each client’s chronological journey, and scores overall account health - allowing teams to spot churn risks and service bottlenecks without manually reading hundreds of support tickets.

The Use Case

Customer-support records are inherently uneven: one client might have a single routine enquiry, while another has seven interactions spanning multiple departments, channels, and unresolved follow-ups. Looking at individual messages in isolation misses the broader trajectory - an early complaint that was resolved smoothly looks very different from repeated follow-ups ending in an escalation.

This workflow bridges that gap by evaluating the data at two distinct levels before bringing them together in a management dashboard:

1. Interaction Level (What happened in each touchpoint?): The first AI Decision block reads each comment alongside its operational context (department, interaction_type, comment_author, and case_status_after_interaction). Rather than relying on brittle keyword searches, it interprets the full context to assign:

   • Interaction Category: Classifies the conversation topic (e.g., Complaint / Escalation, Billing / Refund Dispute, Technical / Delivery Issue, Sales / Upgrade Enquiry, or Positive Feedback / Resolved).

   • Churn Signal (Yes/No): Flags whether the interaction shows unresolved frustration, repeated chasing, a refund/cancellation demand, or formal escalation.

   • Interaction Sentiment (0–2): Scores the tone from frustrated/escalating (0), to neutral/routine (1), to happy/resolved (2).

2. Customer Level (What is the overall health of each account?):  Interactions are ordered chronologically per customer and rolled up into a single account summary containing interaction count, average sentiment, churn-signal count, latest case status, and a full transcript of the journey. A second AI Decision block reviews that combined evidence to assign:

   • Client Health Status: Happy, Neutral / Routine, At risk of churn, or Critical / Churning.

   • Retention Health Score (0–2): Rates overall retention health from imminent churn risk (0) to satisfied advocate (2).

3. Management Dashboard (Where should teams act?): The customer-level health scores are joined back onto the interaction log so managers can explore macro trends over time, see which departments or channels drive negative experiences, and drill directly into the full chronological journey of any at-risk account.

What This Functionality Can Do For You

• Turn unstructured text into structured metrics: Convert free-text comments into consistent categories, boolean risk flags, and numeric scores using plain-English rules instead of complex formulas or keyword lists.

• Evaluate full journeys, not isolated messages: Account for sequence and resolution - distinguishing a recovered customer from an escalating one across accounts with any number of interactions.

• Prioritize proactive retention: Surface At risk and Critical accounts immediately, complete with an AI-generated summary of what went wrong and what the latest status is.

• Pinpoint operational root causes: Connect customer dissatisfaction to specific departments, interaction types, channels, and timeframes so leaders know where process improvements will have the biggest impact.

• Provide transparent, auditable AI outputs: Constrain the model strictly to your predefined categories and recorded data - producing structured early-warning indicators that guide human follow-up rather than black-box predictions.

Read more about AI Decision block.

Demo file IOZ attached below.

Last updated on 13 November 2026