Telecommunications — Turn Call Sentiment into Campaign Lift
Telecom Call Sentiment Analysis Identifies Winning Feature and Lifts Campaign Engagement.
Executive Summary
Client: National telecommunications provider analyzing thousands of customer support and sales calls each week.
Challenge: Product teams lacked a scalable way to detect which features sparked positive reactions across calls; marketing relied on surveys and anecdotal feedback.
Solution: Deployed call sentiment analysis to transcribe voice interactions, score emotion, and surface topics driving strong positive sentiment; the standout feature was elevated in creative and targeting for upcoming campaigns.
Results: Campaigns featuring the positively received feature saw stronger engagement and measurable lift in click‑through and assisted conversions versus control creatives.
A consumer and SMB telecom brand offering mobile, broadband, and TV bundles. With millions of interactions each quarter, the team needed AI‑powered customer sentiment analysis to spot “what customers love” and turn it into a performance creative.
Challenges
Hidden signal: The “happy path” moments in calls weren’t detected at scale, so winning features were under‑promoted.
Voice nuance: Text‑only analysis missed tonal cues like enthusiasm or relief, reducing accuracy of topic sentiment.
Slow feedback loops: Manual QA and surveys delayed insights getting to product and marketing.
Solution
Multimodal call sentiment analysis: Dual‑channel models combined transcript and acoustic tone to boost accuracy and minimize false positives.
Topic mining at scale: Thousands of calls were clustered to identify co‑mentions (feature + positive sentiment) and rank drivers of delight.
Campaign activation: Creative and landing pages highlighted the identified feature; audiences were refined to segments showing prior positive sentiment for that benefit.
Closed loop measurement: Engagement, CTR, and assisted conversions for “feature‑led” creatives were compared to control.
Outcomes
Insight: One network reliability feature emerged as the top positive driver across support and sales calls.
Performance: Feature‑led creatives delivered higher engagement and brand‑positive sentiment lift compared to baseline.
Operating model: A repeatable “listen → detect → promote → measure” loop now informs quarterly campaigns.
Client Say
Conclusion
AI call sentiment analysis converts raw conversations into marketing signal. By amplifying features with proven positive sentiment, telecom brands improve customer experience and campaign ROI in a closed loop.
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FAQ
It uses AI to detect positive, negative, or neutral emotion in call recordings and transcripts, turning thousands of conversations into actionable insight for customer experience and marketing.
By clustering topics that consistently co‑occurred with strongly positive sentiment across support and sales calls, revealing a feature customers praised repeatedly.
Phone call recordings and transcripts were primary, with optional enrichment from chat, email, and social comments to validate patterns across channels.
Combining acoustic cues (tone, pace, stress) with transcript keywords typically improves precision over text‑only approaches, especially for enthusiasm or relief signals.
In most teams: 2–4 weeks to connect recordings, define taxonomies (features/benefits), validate the model on labeled samples, and publish a “Delight Drivers” dashboard.
Track a closed loop: sentiment trend for the promoted feature, campaign engagement and conversion lift versus control, and downstream impacts like churn risk and NPS for exposed cohorts.
Product uses the insights to prioritize roadmaps around proven delight drivers, while CX teams refine scripts and deflection content to amplify what customers value most.
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