Transforming customer service: KPMG Intelligent Interactions · Transforming customer service: KPMG Intelligent Interactions. Bill Cline, US Advisory Lead, Intelligent Automation

Transforming customer service:KPMG Intelligent Interactions

Bil l Cl ine, US Advisory Lead, Intel l igent AutomationKPMG

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Driving new service experience standards Intentional

Personalized, empathetic experiences

Anticipation before problem arises

Streamlined, optimized channel mix

Single line of communication across channels

Contextual information provided to agent

Singular, integrated experience

FUTURE

By 2020, 85% of all customer interactions will be powered by a chatbot.1

In the future, the focus of service activities will no longer reside in a collection of buildings that house call center agents but in a virtual ecosystem of digital and human assistants.

INTEGRATED PERSONALIZED & ENABLED PROACTIVE

Three trendsthat matter

High cost, low satisfaction customer service has long been evident.

Why hasn’t it been fixed?

AutomationSilos Human capital Insights

KPMG Intelligent Interactions framework

Automated responses

Customer Interactions

Customer Insights & Analytics

Performance Measures

• Key KPIs (call handling, first response handling, etc)

• Key KRIs (complaints, issue resolution, etc)

EDCO Engine

• Eradicate

• Deflect

• Contain

• Optimize

Actions

Augmented agent activity

Automated completion

and follow-up

Channels

Customer Insights

• In-Store & Online Customer Data

• Sales and Service Data

• Social Media Interactions

Interactions Engine

Customer Insights

• Bringing relevance and personalization to the conversation

Customer Intent

• Understand customer needs & sentiment

• Predict best handling of interaction

Interaction Optimization

Analytics

• Data Validation

• ML Models

• Speech-to-Text Transcription

• Classification & Insights

Optimized agent actions

Automated responses






EDCO Engine

• Eradicate

• Deflect

• Contain

• Optimize

Actions



and follow-up

Channels

Customer Insights




Interactions Engine

Customer Insights


Customer Intent




Analytics

• Data Validation

• ML Models




• Maximizes use of data and machine learning models across channels

• Get faster, consistent customer experience by design

• Avoids back-end system integration inherent in silo channel enhancement


Automated responses






EDCO Engine

• Eradicate

• Deflect

• Contain

• Optimize

Actions



and follow-up

Channels

Customer Insights




Interactions Engine

Customer Insights


Customer Intent




Analytics

• Data Validation

• ML Models




• Outputs from intent engine drive highly automated outcomes

• RPA, workflow, and other automation technologies create fully automated responses and augment agent effectiveness


Automated responses






EDCO Engine

• Eradicate

• Deflect

• Contain

• Optimize

Actions



and follow-up

Channels

Customer Insights




Interactions Engine

Customer Insights


Customer Intent




Analytics

• Data Validation

• ML Models




• Data and analytics from on- and off-company sources develop keen customer insights that span interaction channels

• Customer insights enable a more personalized experience

• D&A is used to incent desired customer behaviors and improve cross-selling


Automated responses






EDCO Engine

• Eradicate

• Deflect

• Contain

• Optimize

Actions



and follow-up

Channels

Customer Insights




Interactions Engine

Customer Insights


Customer Intent




Analytics

• Data Validation

• ML Models




• EDCO framework reduces call volumes and handling times

• Unnecessary calls are Eradicated

• Calls are Deflected to self-service and digital channel

• More are Contained in a smarter IVR

• Remaining calls are Optimized via automation and process improvements


Getting started: 10-12 week channel POC

Within about 12 weeks, we can enable a

prototype with a roadmap to start small

and scale fast.

Interaction Analytics and Intelligent Interaction Strategy

• ML driven analytics against selected channels (multi-channel may require more time)

• Interaction strategy developed (e.g. automation, augmentation, self-service)

• Business requirements defined and documented

• Technology assessment conducted and high level future architecture designed

• KII vision, roadmap and business case developed

• POC level ML model for a defined but representative scope of customer communications in the chosen channel

• Defined architecture for required ML and integration components needed to put into production

• Defined benefits case relating to move into production

• Interaction treatment strategy for scope of communications contained in POC (e.g. automation, augmentation, self-service)

Channel Proof of Concept

Builds a strategy and roadmap with associated benefits for the optimal deployment of KII in your environment.

Builds a POC & case for change for a selected channel to prove that KII can be delivered with the data available within the client environment.

Do this:

If you are unsure where to get started, what the business impact will be or whether it will work in your environment

Do this:

If you have a view on where you want to prioritize the channel focus (e.g. chatbots or email)

Transforming customer service with ML and Intelligent Interactions

Today, customers demand seamless and

positive interactions with a company,

extending into customer service. To meet

these expectations, organizations should

adopt a proactive customer service

approach and excel at four dimensions.

Appropriate

Anticipatory

Integrated

Trustworthy

14

Jason sees an ad in the paper about a new phone WorldPlaceElectonics is offering. His current phone is more than years old.

Jason gets to the front of the line to meet with a “Einstein”.Jason goes into the store and waits in a long line to purchase.

Unfortunately, Jason finds out they are out of the phone in the color he wants. He settles for a red one instead.

Jason arrives home and is excited to start using his phone. Jason can’t get e-mail set up and starts feeling frustrated.

15

Jason calls customer service and gets put on hold with an unknown wait time.

Later that day, Jason tries to make a call, but his calls keep getting dropped.

His issues are fixed after waiting 15 minutes for a tech support representative. His excitement to use the new phone is mitigated by the email frustration.

Jason goes online and can’t find the answers. Chat queue provides frustrating and ineffective

solutions due to the nature of the issue.

Jason gets frustrated and calls customer support, only to be 34 in the queue. At this point, Jason is frustrated at WorldPlaceElectronics.

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Documents

Transforming customer service: KPMG Intelligent Interactions · Transforming customer service: KPMG Intelligent Interactions. Bill Cline, US Advisory Lead, Intelligent Automation