BEAT helps you listen to what your customers are saying BEAT is Deloitte’s voice analytics platform that uses cognitive technology and risk algorithms to monitor voice interactions based on speech, behavioural and human emotional tendenciesBEAT is a fully integrated voice and interaction surveillance solution that monitors customer interactions, manages alerts through risk scoring and provides a view on key risk indicators resulting from an interaction. Models can be trained to detect various different risks and outcomes, including: Mis-selling, potential complaints, repeat calls, Welfare issues, Fraud and Churn.

Inputs BEAT platform Outputs

Language analysis

BEAT Language

• Transcription based

• Fuzzy searching

Search Packs

• Disclosures

• High Risk

• Internal protocols

Phonetic algorithm

• Phonemes

• Phonetic searching

Behavioural analysis

BEAT Behaviours

• Tone, Pitch, Spectrum

• Call Dominance

• No Talk / Cross talk

• Word rate

• Hesitation

BEAT Behaviours

• Sentiment: Anger, sad, happy …

• Vulnerability

• Undermining

Audio data

Transcription

Risk score

Machine learning

Customer segmentation

Customer indicators alert

Meta data

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© 2018. For information, contact Deloitte Touche Tohmatsu Limited.

Outcome based cognitive call centre platform

ContactsShane WallisSenior Manager | Risk Advisory M: +44 7470 685690shanewallis@deloitte.co.uk

Vincenzo OpekaSenior Manager | Risk AdvisoryM: +44 75 845 94029vopeka@deloitte.co.uk

Reasons of calls

Call details

Change of personal details39

Tax queries22

Others2

Life-changing events209

Request for PoA/LoA115

Voice clip

Cardinal emotions

Vulnerable customer

Anger

Anxiety

Neutral

Sadness

5%

Happiness 2%

Potentially 2%

28%

15%

50%

End of ID&V

Average amplitude

End of query

Outcome driven Behavioural analysis

Risk scoring Language analysis

BEAT

Combining the behavioural and language analyses to predict outcomes

Risk-based segmentation using language and behavioural analyses.

Behavioral analysis looks at the audio elements of an interaction, to understand if there is a potential for negative outcomes or customer vulnerability beyond the language used.

This is done by using natural language processing to understand sentiment and the potential risk of non-disclosures and sentiment analysis.