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Speech Analytics A tool to help drive our customer centric culture

Lee Hancock & Chris Barkataki

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ExperianThe leading global information services company

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Operations Background

Created in 2011Merging the operational

functions for:

Consumer Services

Credit Services600PEOPLE

Over 3 millioncustomer contacts

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Speech AnalyticsWhy did we need it?

“We cannot solve our problems with the same thinking we used

when we created them” – Albert Einstein

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Cost reduction

► Process improvement

► Quality assurance

► Performance enhancement

Compliance and regulatory improvement

Customer experience

Customer insight

Other opportunities – e.g. WFP for forecasting, Marketing for customer responsiveness, Product for testing and learning

How we built a Business Case for Speech AnalyticsThe Potential Benefits

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Speech AnalyticsHow have we got to where we are?

JUN 2009Process startedto look at options for

acquiring speech

analytics technology

OCT 2010Dedicate resourceto drive forwards

speech analytics

implementation

APR 2011Launch Speech

Analytics,

installed & fully

operational

JUN 2011Actionable Analytics

formed to share

insight & analysis

AUG 2011Key focus on

Sales & Retention

area to better

understand results

APR 2012Started working

with product &

marketing teams

Demonstrating

a measurable

return through

efficiency &

revenue

opportunities

Embedding

analytics into

our culture &

viewed as a

business

necessary tool

changing objectives

SEP 2009Proof of Conceptdemonstrating depth

of analysis available

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Speech AnalyticsHow have we implemented it?

• ‘Tool’ to drive culture not ‘Team’

• Use within existing resource

• Drive own agenda

40%

25%

20%

15%

10%

Compliance Projects

Ad-hoc Reporting

Other Initiatives

Balancing

the usage

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Gaining buy-inHow did we ensure colleague engagement?

Test, learn & communicate

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Continue to Evaluate SuccessActionable Analytics Forum

DISCUSSION

& DEBATE

ACTIONS &

RESULTS

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Talk Time Retentions

How has it helped?What we’ve achieved and how…

9Times Returnon Investmentcurrently split

evenly between

revenue gains &

efficiency savings,

although revenue

continuing to rise..

65%

70%

75%

80%

85%

Scripting

2%

4%

6%

8%

10%

Promotion of Call Backs

Request

Compliance

Reason

Discussion

Agreement

80%Discussing

customer

needs

Revenue

Efficiency

More likely to convert to loyal

customer when full scripting used20%SUCCESS (AHT)

TIME OF DAY

• Better understanding of what’s said

on the call

• How this impacts on customer

behaviour

• Identifying wasteful contacts

• Mapping the optimum ‘DNA’ of a call

• Understanding where & when we can improve…

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60%

7%

23%

8%

Why do they contact?

Cancellation AccessAlert ReportScore Stat Report

16%

6%

29%

25%

21%

What data impacts?

Electoral Roll Associations

Public Record Credit Accounts

Previous Searches Linked Addresses

9% more likely to reside in the Midlands

8% less likely to reside in the North

3% more likely to reside in the South

36%Less access

issues

TWICEAs likely not

registered to

vote

180secondsAverage increase

in call length

11%14%

37%

House Car Credit

What do they want?

more likely to remain a

customer after calling to cancel60%

Example - Student Proposition, building the caseWhat do we already know?

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34.6%

NEW STUDENT

26.3%

FORMER STUDENT

33.2%

GRADUATE

0.0%

AT UNIVERSITY

She’s not going

to have a good

credit rating as

she’s never had

any credit...

““ 60-70% of that stuff

that is on there is

from when I was in

my early 20’s after

I had just come out

of uni...

““...I’ve been skint

since I left uni, I'm

just a graduate but

I'm back into full

time work now...

““

60% rely on parent to contact

75% want advice on how to improve

things with a view to big purchases

No contact but the time when actions

have the biggest consequences

Majority struggling with shift in

circumstance

Student PropositionWhere are the opportunities?

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The future of Speech AnalyticsWhere to next?