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Customer Experience Analytics Solutions

Nyenrode, June 5

Overview document

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#1 The WorldCustomer behaviors

change rapidly, navigating across channels, evaluating the full

experience not just one part of it

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#2 Customer Journeys For customer experience, touch points matter, but journeys matter more;

journey-led transformations deliver

great impact

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Web IVR Agent Store Mobile

Traditional customer service approaches focus on touchpoints or “moments of truth”

of all Companies still “work” with a silos based approach>70%

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Customer journeys are different than “moments of truth”

OnlineFAQ

Techagent

Storefor phoneupgrade

Activation app

IVR “team issue”

A journey is …

… an event typically across time and channels, and therefore, cross-functional in nature

… anchored on how customers experience about it, not the way functional silos do

of all customer interactions happen during multi-event, multi-channel, cross-time journeys>50%

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Journey performers win in Customer Experience ...and Growth

R2 = 53% vs. 14% for single touchpoints satisfaction

R2 = 53% vs. 14% for single touchpoints satisfaction

R2 = 53% vs. 14% for single touchpoints satisfaction

SOURCE: McKinsey U.S. multi-industry survey

Example: PayTV – Journeys winners are overall customer experience winners

Example: Auto Insurance, a 1/10 of a point in journey sat worth a full point of revenue growth ($200M on average)

Overall CSAT2011

8.4

8.0

7.6

7.2

6.8

6.4

6.0

Journey satisfaction2011

7.87.67.47.27.06.86.66.46.2

A

6.0

I

H

J

G

FE

D

L

K

CB

-2%

0%

2%

4%

6%

8%

Revenue growth2011 vs. 2010

9.2

Journey satisfaction2011

9.4

N

9.08.88.68.48.28.0

M

EK

C

B

A

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Journey-led transformations deliver impact across multiple dimensions

Fuel revenue growthChurn, upsell, acquisition

Improve customer satisfaction

Engage employeesLower cost to serve

20 to 30%20 to 30%

15 to 20%15 to 20%

20%20% 10 to 15% 10 to 15%

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#3 Big Data There’s ever more data …but companies often don’t know what they or

how to drive value from it

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Mobile

Everyone, everything, every interaction generates “exhaust” data

Transactions

Social Audio/video

Scientific/engineering ‘Internet of things’

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Most companies store more data than the size of the entire collection of the library of US Congress…

>500=WalMart data warehouse in 2004

US EXAMPLE

235 = Library of congress collection in 2011

Average stored data per firm with more than 1,000 employees (Terabytes, 2009)

SOURCE: IDC: US bureau of labor statistics; McKinsey global institute analysis

1,800

231

278

319

370

536

697

801

825

831

870

967Discrete Manufacturing

Utilities

Healthcare Providers

Securities and Investment Services

Banking

1,312

1,507

1,931

3,866

Education

Retail

Wholesale

Construction

Resource Industries

Process Manufacturing

Insurance

Communications and Media

Transportation

Professional Services

Government

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…however in a very fragmented and not consistent ways

Structured and

unstructured

HadoopMassive parallel

processing,XXX

500m to 5bn

touch points per year

Disparate customer data

sourcesagent call, IVR, Web,

Mobile, Social media, transactions,

retail/stores, segmentation

data

XX TB of data

Complex calculation, predictive algorithm, immediate

access

Recent research: 0,5% of available data is actually used!

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…but those that are able to master Big Data outperform their peers

Revenue 1999-2012Percent; 10-year CAGR

EBITDA 1999-2012Percent; 10-year CAGR

13

19

13

11

18

6

6

8

6

7

Healthcare

Retail

IT

FinancialServices

Telecom

12

9

20

10

13

6

6

10

4

5

Other companies

Big Data leaders

SOURCE: Bloomberg; Datastream; annual reports; McKinsey analysis

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#4 CE = BDWinning in Customer

Experience is winning in Big data, uncovering never ending flow of

highly specific opportunities

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1 E.g., demographics, products owned, segment, CLV

Connecting Customer Journeys across hundreds of millions of touchpoints …

… to accelerate CSAT, retention, up/cross-sell improvements and cost

Increase retention and up/cross-sell, by several bps e.g.,

▪ Path to churn/cross-sell

▪ Channel preferences

▪ Micro segment campaigning enablement

Email Social andchat

Stores

Web

Call center

Customer data-base1

Field

Mobile/SMS

Check minutes balance

Change phone

Repair wireless router

Churn

Upgrade data plan

Drive up CSAT/NPS by 5-10 pptsacross Journeys, e.g.,

▪ Pain points across channels

▪ “Ideal Journey”

Reduce customer service cost by 10-25% across channels, e.g.,

▪ Call / field service deflection

▪ Shift to self-serve

McKinsey and ClickFox connect touch point data to visualize Customer Journeys and drive CSAT, cost and revenue improvement

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At the heart of Journey Analytics is the powerful ClickFoxplatform that connects data into Journeys

Tasks

Completions & Departures

Raw DataEvents

Instant Transform

Paths

All Events Connected

Journeys

Paths to Outcomes

Failed Web Pay Enrollment

Web Pay Confirmed

IVR Call

Transfer to Agent

Web Auto Pay

Success

IVR Pay By Phone

Agent New Account Info

Enrollment JourneysUnstructured IVR Logs

Structured Agent Logs

Retail Desktop Data

Web Logs

Retail Steps

Cross Channel Outgoing Paths from IVR promise to

pay event

Cross Channel Outgoing Paths from web online

payment event

Churn Journeys

IVR Prompts

Web Pages

Agent Steps

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Journey visualizations help to uncover first order cross channel insights to drill down into

Example of Journeys leading to low CSAT in cards – card activation example

Second order insight #1-Website asking clients to place a phone call to finalize card activation

Second order insight #2-IVR not recognizing card; customer is forced to ask for an operator

Second order insight #3 – IVR navigation not simple; customers often follow the ‘contract information’ leading to internal call transfers and increasing frustration

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0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

24 27 30 33 36 39 42 45 48 51 54 57 60 63 66

Responsespread

Average NPSof customerscompletingjourney

replacement card

lost stolen

enroll

search

offers

application

mobile features

travel notification

help

balance transfer

modify payment

error

authorized users

rewards

locked out

collections

fraud

disputes

activation

manage payment accounts

statements

message alert

authenticationmanage account

profile

recent transactions

account summary

make schedule payment

Use case 1 – For the card division of this bank, ClickFoxallowed to prioritize Journeys driving to high / low CSAT …

Source: McKinsey / ClickFox, client data

Highest

Lowest

Worst Best

Most polarized consumers

Mixed consumers

Similar sentiment across all customers

Spread of Response vs. NPS

Source: McKinsey / ClickFox

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…which was then used to “fix” key leakage points for prioritized journeys

Source: McKinsey / ClickFox

Mobile Journeys leading to frustration …▪ 42% of users accessing

Mobile Rewards (700k customers) check rewards status multiple times < 72h

▪ These customers drive 750k calls annually to the call center ($2-4M cost)

Example mobile app journey

… Driven by inconvenient back and forth between Mobile app screens

Path: Mobile Rewards ! Mobile Transactions ! Mobile Account Summary

▪ 69% of detractors who reach mobile transactions page return to mobile rewards page

▪ 90% of detractors who reach end of journey return to mobile rewards page

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#5 Management shiftThe new management paradigm is about test

and learn at ever increasing scale, where

details matter

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The new management paradigm is about test and learn at ever increasing scale

▪ Tool-enabled generation of insights, where typical business analysts can drive analyses in a dynamic, organic way

▪ Daily, weekly flow of customer opportunities and risks with potential action to take – independent of any IT constraint

FutureHistory

▪ United data sets, as all customer data connected, in one place (interactions, segmentation, transactions)

▪ Discrete data sets, as customer data sitting in disparate systems and is hard to pull together

▪ Discrete dashboards which provide silo-ed views on business performance

▪ Pilots takes months, requiring ad-hoc, hand-made measurements to understand business impact of implemented improvements

▪ Need for scarce Subject-Matter-Experts with advanced analytical skills and deep industry/BT knowledge to make sense of data

▪ Limited to static and infrequent analysis of customers behaviors, taking weeks or months – often highly dependent on IT

▪ Based on 3600 transparency on E2E customer journeys, and related customer profiles

▪ Near real time feedback, across channels, on multiple dozens of test-and-learn activities,

How are data made available?

How are insights generated from data?

How do insights inform business decisions?

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Example – Create a “Journey lab” to quickly test and refine improvement ideas before scaling them

Redesign test

BEFORE

Before

Test

Multi-channel tracking

Clicks on Send a Temp Password

Clicks on Answer Secret Questions

Success

58%

Answer secret ques-tion and reset pass-word

Secret answer error

41%

Other error

1%

Other disposition

84%

Reset password

16%

▪ Education codes▪ General info▪ Features

related assistance▪ Payments

released assistance

Did not to Web <30 days

51%

Did not return to Web <30 days 49%

Success

19%

“Cannot remember answer”

2%

Call agent <1 day

15%

Click to chat

~0%

IVR and hang-up <1 day

5%

“Password reset” failure issue

100%

▪ 15% issues ending up in agent calls

▪ … of which 84% issues not solved on the phone

▪ … of which 50% do not go back on online in 30 days

Redesign of web page

Real time tracking of results with ClickFox▪ Web usage▪ Calls related to

password issue

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Example – Machine-enabled decision making leverage churn prediction models to implement trigger-based retention actions

Data processed through ClickFoxand modeling engine

Customer data collected on a daily basis

Probability of churn updated for each customer

If churn probability above threshold, retention action is triggered

Recommender engine selects optimal retention offer for customer at risk

Information is provided to outbound systems / front-line employees for immediate response

Next best action

▪ Retention action:email sent to customer with 50% discount on annual fee

▪ Timing: Immediate response

‘Static’ profile

Gender : Male

Age: 28

Region: Andalucía

Tenure: 16 months

Product: Fixed tel, Mobile, Internet, Cable TV

Pricing: €67/year (mispositioned)

Payment history: all bills paid on time

Churn likelihood

An example of a real customer journey leading to churn…

… and what happens behind the scene to prevent it

Jan 1st Jan 31st

Existing model

1

0.8Retention Action Threshold

0.6

0.4

Billing dispute

Product downgrade

Jan 13th

Mobile port outQuestions about disco. process

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OUT