AI-Powered Call Analytics Platform for a Major UK Channel Telco

Client Profile

One of the United Kingdom’s largest channel communications providers, offering hosted telephony, SIP trunking, and contact centre solutions to thousands of channel partners and their end customers. The organisation employs over 1,500 people and generates in excess of £400 million in annual revenue.

Their platform carries millions of calls per month across a diverse partner base — from small businesses using hosted voice to large enterprises running full contact centre deployments. Every one of those calls generates a recording that is stored but, until now, never systematically analysed.

Industry: Telecommunications (Channel / Wholesale) · Region: United Kingdom · Products Used: VerSense (Call Analytics) · SIP Infrastructure

The Challenge

The client was sitting on one of the most valuable untapped data assets in UK telecommunications: millions of call recordings containing rich signals about customer satisfaction, agent performance, compliance adherence, revenue opportunity, and churn risk.

The data existed. The intelligence didn’t.

Call recordings were stored in Amazon S3 buckets as a compliance and dispute resolution resource — pulled out individually when a specific complaint or query required review. There was no systematic analysis, no pattern recognition, no proactive alerting. The recordings were a liability archive, not a business intelligence asset.

The consequences of this blind spot were significant:

• Partners were churning without warning. By the time account managers identified dissatisfaction, the partner had already begun migrating. There were no early warning signals being extracted from the calls themselves — calls where frustrated end-customers were expressing dissatisfaction that would ultimately flow upward to the partner’s decision to leave.
• Compliance gaps were invisible. Without systematic call review, there was no way to verify whether agents across the partner base were adhering to regulatory requirements, following scripts, or capturing required consent. Compliance was assumed, not verified.
• Revenue leakage went undetected. Agents were missing upsell prompts, failing to follow retention scripts, and letting high-value customers churn without executing save procedures. The client knew this was happening but had no way to quantify it or identify which partners, teams, or individuals were most affected.
• Agent coaching was anecdotal. Team leads reviewed a handful of randomly selected calls per agent per month — a statistically insignificant sample that couldn’t reliably identify coaching needs or track improvement over time.
• Operational inefficiencies were hidden. Excessive hold times, unnecessary transfers, repeated calls for the same issue, and avoidable escalations were all occurring at scale but invisible without data.

The client had evaluated enterprise call analytics platforms but found them prohibitively expensive, complex to implement, and designed for single-enterprise deployments rather than a multi-tenant channel model.

Our Approach

We designed and built VerSense — a purpose-built call analytics platform following an “Extract Once, Analyse Many” architecture. Rather than processing each recording multiple times for different analytical purposes, VerSense performs a single, comprehensive extraction pass that feeds seven distinct analytical modules, each answering a different business question for a different stakeholder.

Two integration paths were scoped to match the client’s infrastructure:

• Path 1 — S3 Batch Analysis: Processing existing call recordings stored in Amazon S3 buckets. This is the immediate-value path, requiring no changes to the live call infrastructure. Recordings are ingested, transcribed, and analysed on a scheduled basis.
• Path 2 — Real-Time SIP Media Forking: A future-state architecture where live call audio is duplicated to the VerSense platform via SIP media stream forking, enabling real-time analytics, live agent assist, and in-call intervention. This path requires SIP infrastructure integration with the client’s hosted voice platform.

What We Built

Module 1: Command Centre (Executive View)

A single-screen strategic dashboard designed for weekly and monthly rhythm. It answers one question: “Are we getting better or worse?” Key metrics include call volume trends, top contact reasons, biggest movers (metrics that changed most), and an overall risk score. Every number is clickable, drilling down into the relevant analytical module.

Module 2: Contact Drivers

Answers “Why are people calling, and what’s emerging?” Uses natural language processing to classify every call by reason, intent, and outcome — then surfaces trends over time. When a new product launch generates a spike in confused calls, or a billing change triggers a wave of complaints, Contact Drivers identifies the pattern within hours rather than weeks.

Module 3: Operational Efficiency

Answers “Where are we wasting time and money?” Identifies excessive hold times, unnecessary transfers, repeat calls for the same issue, avoidable escalations, and process bottlenecks. Quantifies each inefficiency in terms of cost — turning vague operational concerns into specific pound-value improvement opportunities.

Module 4: Customer Experience

Answers “Where are calls going wrong for customers?” Tracks sentiment trajectories within and across calls, identifies moments where customer satisfaction drops (long silences, raised voices, repeated explanations), and flags interactions that are likely to result in complaints or churn.

Module 5: Agent Performance

Answers “Who needs what coaching?” Provides per-agent scorecards covering talk-to-listen ratio, script adherence, empathy indicators, resolution effectiveness, and upsell execution. Replaces random call sampling with comprehensive, data-driven coaching recommendations.

Module 6: Compliance & Risk

Answers “Are we safe and audit-ready?” Monitors every call for regulatory compliance — consent capture, required disclosures, prohibited language, data handling protocols. Flags non-compliant interactions in real-time (Path 2) or within hours (Path 1), enabling targeted remediation rather than blanket retraining.

Module 7: Revenue Intelligence

Answers “Where are we losing or missing money?” Identifies missed upsell opportunities, failed retention attempts, pricing objections that could have been overcome, and high-value customers expressing intent to leave. Quantifies revenue at risk and revenue missed.

Evidence Layer: Call Detail

Every metric in every module links back to proof. Clicking any data point surfaces the specific calls that generated it — with audio playback, speaker-diarised transcript, extracted summary, key moments timeline (sentiment dips, overtalk spikes, long silences), AI scores with confidence levels, and 30–60 second evidence clips for each flagged moment.

Demonstration Results

Finding	Impact
456 instances of missed upsell scripts	Direct revenue leakage — agents failing to present available offers during qualifying interactions
89 accounts flagged as at-risk	Customers expressing churn intent or significant dissatisfaction that had not been escalated
Systematic compliance gaps	Specific teams consistently failing to capture required consent confirmations
Repeat call patterns	Identifiable issues generating multiple calls per customer, each consuming agent time

These findings were generated from a limited sample. At production scale — processing millions of calls per month — the platform’s ability to surface actionable intelligence multiplies significantly.

Why This Matters

For a channel telco, the value proposition is layered. VerSense doesn’t just improve the client’s own operations — it creates a new product they can offer to their partners. Every partner running a contact centre on the client’s platform could benefit from the same analytics, creating a new revenue stream and deepening partner stickiness.

The “Extract Once, Analyse Many” architecture means the heavy computational work happens once per call. Adding new analytical modules or new partners doesn’t require reprocessing — it’s additive analysis on existing extractions, keeping costs linear rather than exponential.

And critically, the evidence layer transforms analytics from opinion into proof. When the data says an agent needs coaching, the system can play the exact 30-second clip that demonstrates why. When the data says a partner is at risk, it can surface the specific calls that signal trouble. This is intelligence that drives action, not dashboards that drive meetings.