MARCOM22 / SIGNAL INTELLIGENCE
PUBLIC-EVIDENCE PROTOTYPE Accessed 23 Sep 2026

ALMATAR · CUSTOMER EXPERIENCE

From scattered reviews
to actionable signals.

01 / EXECUTIVE VIEW

A decision-support prototype using public reviews to identify recurring experience failures, prioritize investigation, and demonstrate what becomes possible when connected to internal data.

Reviews analysed 165 Across three public platforms
Negative signals 75 45% of the public sample
Validated high severity 36 Manually reviewed cases
Priority evidence 08 Validated for investigation and intervention
02

Where friction concentrates

All 165 reviews

Positive experiences are retained to avoid a failure-only view. Bars show first-pass classification across the full sample.

03

Signal composition

Public sample
Negative75
Positive88
Mixed2
THE OPPORTUNITY

Connect signals to booking, payment, refund, notification and support data—then act before a review is written.

04

Priority customer evidence

Eight manually validated public reviews selected for specificity, traceability and operational relevance.

05

CONTROLLED PILOT

Turn signals into
measurable intervention.

Public evidence reveals where to look. Almatar’s internal data can determine scale, ownership, financial exposure and whether an intervention worked.

  1. 01
    Listen

    Unify reviews, support conversations and operational events.

  2. 02
    Diagnose

    Classify recurring failure patterns and connect them to root causes.

  3. 03
    Prioritize

    Rank cases by urgency, customer consequence and business exposure.

  4. 04
    Intervene

    Route the next best action to an accountable team and measure closure.

Evidence standard

This prototype uses a convenience sample of public reviews. Customer statements are treated as reported experiences—not verified operational facts. A pilot would validate findings against Almatar’s internal systems before business decisions are made.

MARCOM22 · CONFIDENTIAL DEMONSTRATION