VoiceSense — Predictive Voice Analytics

VoiceSense -AI that listens to how your customers speak and tells you what it means for your business

VoiceSense uses psycholinguistics and machine learning to find a validated connection between vocal characteristics and personality traits. The model has been built and tested across millions of voice samples in multiple languages. We integrate it into your sales, customer service and HR workflows.

Book a free call How it actually works

✓ Behavioural speech analysis✓ Vocal analysis✓ Sentiment analysis✓ Predictive analysis

Measured lift

Predictive intelligence for financial institutions

Improved targeting performance

Credit card acquisition: predictive lift of +42% for high-scoring customers compared to baseline.

Better treatment accuracy

Credit-limit increase: lift of +24% in completion among high-scoring customers.

Prediction-driven upsell performance

Additional or authorised card: lift of +59% compared to average.

More accurate prioritisation

Personal loan origination: lift of +32% in loan take-up for high-scoring customers.

Risk stratification

Lift of +25% on first-call prediction for default, improving downstream collections performance.

Collection prioritisation

High/low risk prediction: behavioural risk bands show clear separation, with 3x improved risk prediction for collection default.

Prediction improves performance

Personal credit line or overdraft: lift of +29% in open or expand actions.

Earlier delinquency prediction

Onboarding stratification: 1.7x higher default in high- versus low-risk bands, supporting tighter approvals.

Production impact

Additional select case studies

Loan approvals — production impact

A specialty lender deployed VoiceSense as a behavioural validation layer in their approval workflow. Result: the overall default rate reduced by 5% after adoption, with high-risk customers showing a 33% default rate versus 17% for low-risk customers.

› Customer — specialty lender with the goal of reducing portfolio default while protecting growth
› Approach — deploy VoiceSense as a behavioural validation layer in the approval workflow; keep the existing scorecards and adjust thresholds and manual-review rules using the risk bands
› Results — high-risk default 33.2% vs low-risk 16.6% (2x), leading to an overall default rate reduced by 5% after adoption

Loan approvals — production impact — chart

Collections prioritisation accuracy

A large US collections operation used VoiceSense to classify debtors into five risk bands at first contact. Result: the highest-risk group showed an 84% default rate, creating a 3x concentration of effort where recovery was most likely.

› Customer — large US collections operation seeking higher recovery and operational savings
› Approach — use VoiceSense to classify debtors into five evenly sized risk bands at first contact, triggering earlier outreach and escalation by band
› Results — the high-risk group presented an 83.9% default rate, a 3x lift that concentrates effort where recovery is likeliest and automates the low-risk paths

Collections prioritisation accuracy — chart

Sales

Sales conversion and retention

Convert more sales, faster, with predictive voice analytics.

On the call

Conversion probability

✓ Identify which calls, in real time, have the highest conversion probability, with existing or new customers
✓ Track the historical trends of existing customers to see when they are most receptive to a product on a call
✓ Let supervisors assess positive or negative interactions by reviewing call atmosphere, live or post-call

Segmentation

Sales and marketing data

✓ In-depth insight into the buying preferences, personalities and behavioural tendencies of your highest- and lowest-quality leads, so customers can be segmented
✓ Match remote sales agents to the customer profiles they succeed with, for better customer management

Retention

Customer churn probability

✓ Detect early signs of churn probability so preventive measures can be taken
✓ Monitor behaviour, satisfaction and upsell/cross-sell probability

People

Human resource screening and retention product

VoiceSense, with validated results from acoustics and continual AI calibration, can work with you to develop a bespoke recruitment model from a deep analysis of candidates’ responses and your company’s needs, integrated through plug-and-play software.

Hiring

Enhance candidate success

✓ AI-driven mining of large, diverse voice and video samples gives interaction and behaviour insight on candidates from the first interview
✓ Focus talent teams’ time on the candidates most likely to succeed in a specific role, with smart job-matching assessment
✓ Improve candidate ROI using job-matching scores and an in-depth profile of each candidate’s tendencies and soft skills

Speed

Reduce hiring time

✓ A streamlined end-to-end recruitment process at the top of the hiring funnel, automatically and objectively screening for top candidates
✓ Reduce total time-to-hire by identifying bottlenecks and giving candidates a swifter, smoother, more personalised experience

Retention

Employee retention and wellness

✓ Track satisfaction levels and churn probability within key departments, identifying at-risk employees before their performance drops or they leave
✓ With the wellness tracking product, employees get an easy-to-use, private tool to track their own stress, burnout, energy, coping and engagement

Mechanism

It analyses how you speak, not what you say

Transcript-based tools

Words first, then analysis

× Speech converted to text, then analysed for keywords and sentiment
× Depends entirely on transcription accuracy
× And transcription accuracy depends on language and accent
× A model needed per language
× Poor on code-switched calls, which is most of them here

VoiceSense

Delivery, not vocabulary

✓ Hundreds of acoustic parameters measured in the speech itself
✓ Intonation, pace, rhythm, pitch variation, pauses, emphasis, energy
✓ Matched against behavioural models built from millions of samples
✓ No transcript needed, and not tied to a language
✓ Under a minute of speech is enough to produce a profile

That difference is the whole product. Everything else on this page follows from the analysis being acoustic rather than lexical.

UAE context

Why this matters on a UAE call floor

The UAE problem

Every language on the same shift

A UAE contact centre handles Arabic, English, Hindi, Urdu, Tagalog and Malayalam, often on the same shift and sometimes in the same call.

› Transcript-based sentiment tools need a model per language
› And handle code-switching poorly
› So most UAE operators analyse only their English calls
› And infer the rest

What it produces →

Why acoustic solves it

One model, every language on the floor

The same model reads a call in Malayalam and a call in Arabic, because it is reading delivery rather than vocabulary.

› No per-language model to build or maintain
› Code-switched calls handled like any other
› For a market where multilingual handling is the norm
› That removes the main reason voice analytics projects stall here

Where the score lands →

Governance

Before any of this is deployed

Five questions, agreed at scoping. An organisation that cannot answer them should not deploy this technology yet.

Consent and notification

Customers are told their call may be analysed, as most UAE contact centres already do for recording. Employees and candidates are told explicitly, because inference about a person is different from recording them.

What is retained

Whether audio is stored, for how long, and whether the behavioural profile persists after the interaction. These are configuration decisions and they should be made deliberately.

Who can access the output

Scores influencing lending, collections or employment decisions need access control and an audit trail showing who saw what and when.

Human review where it matters

Any output influencing a decision about a person needs a documented human review step. Not as a formality, but as the person who can be asked to explain the decision.

Where processing happens

UAE data protection obligations apply to voice analysis as to any other personal data processing. Residency requirements should be established before a proof of concept, not after.

Integration

API and SDK, into what you already run

How it is delivered

An API, not an application

VoiceSense embeds into contact centre platforms, CRM, mobile apps, web applications or on-premise systems. Scores return to the application that requested them, which usually means no change to agent workflows or screens.

› Live streams or pre-recorded audio
› So existing call recording libraries can be processed retrospectively
› No new screen for the agent to learn
› And no change to how the call is handled

Integration work →

How to start

Validate against your own history first

The usual starting point is to run the model over twelve months of recorded calls, compare its predictions against what actually happened, and establish whether the signal holds for your business before it influences anything.

› A validation period before any live decision
› Measured against your own outcomes, not a published benchmark
› Behavioural models are language-independent, so the mechanism transfers
› But outcomes in a different market and portfolio will differ

Wider AI capability →

VoiceSense FAQs

What buyers ask us about voice analytics

What is Voicesense?

Voicesense is a predictive behavioural voice analytics platform that analyses how someone speaks rather than what they say. Using acoustic features such as intonation, pace and emphasis, it builds a behavioural profile and predicts likely future behaviour, delivered as an API or SDK into existing systems.

How does voice analytics work?

The engine measures hundreds of acoustic parameters in a speech sample — rhythm, pitch variation, pauses, emphasis — and matches those patterns against behavioural models built with machine learning. Because the analysis is acoustic rather than based on words, it does not require a transcript.

Is Voicesense language-dependent?

No. It analyses the non-content components of speech, so the analysis is language-independent and not tied to a specific culture or accent. For UAE organisations handling calls in Arabic, English, Hindi, Urdu and Tagalog on the same line, that removes the need for a separate model per language.

What can voice analytics be used for?

Common applications are risk assessment in lending and insurance, contact centre interaction guidance and churn prediction, sales conversion scoring, and HR screening and employee wellbeing monitoring. In each case the output is a probability score feeding a decision the organisation already makes today.

How is it used in a contact centre?

It analyses live or recorded calls to indicate customer sentiment, likely satisfaction and probability of churn or purchase, giving agents and supervisors real-time guidance during the interaction. Existing recorded call libraries can also be analysed retrospectively, which produces insight from the first week.

How much speech does it need?

Under a minute of speech is generally enough to generate a behavioural profile. Analysis runs on live streams or pre-recorded audio files, so existing call recordings can be processed without changing how calls are captured or how agents currently handle them.

How does it integrate with our systems?

Voicesense is delivered as an API or SDK, so it embeds into contact centre platforms, mobile apps, web applications or on-premise systems rather than replacing them. Scores return to the application that requested them, which usually means no change to existing agent workflows or screens.

What about privacy and consent?

Voice analysis of customers or employees needs a clear position on consent, what is retained, how long profiles are kept, and how scores are used. Where output influences hiring, lending or employment decisions, document both the basis for using it and the human review step before deployment.