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Powering GROWTH with BIG DATA

November 2014

| 1

#1 Big Data

The Big Data storm is real – but not easy to

navigate

| 2

Available data is exploding

There’s a hrowing torrent of data, sophisticated analytical tools

SOURCE: McKinsey Global Institute; Hilbert and López, “The world’s technological capacity to store, communicate, and compute information,” Science, 2011

Exabytes

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

201520112007200019931986

3% 25% 94% 99,0% 99,9%

x % of data in digital form

| 3

Most companies store more data than the size of theentire 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

| 4

…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!

| 5

#2 Big Data

There’s tremendous value in applying big data

technology to drive business impact

| 6

With ClickFox, clients connect customer touchpoint data to create transparency on cross-channel journeys

E-mailAgent chat

Stores / branches

Mobile /SMS

Social media

Field

Web

Callcenter

Customer database

Interactive Voice Response

SOLVING THE “DATA CHALLENGE”

▪ Quick set-up, no need for major IT investments

▪ Aggregating Bns of touchpoints sitting in 20+ systems — and proliferating quickly

▪ Managing structured and unstructured / messy data

▪ Refreshing data on a daily basis to follow the market pace

▪ Built-in algorithms to visualize Journeys, navigate through hundreds of millions of data points and surface opportunities quickly

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

Issue 2 – IVR not recognizing card; customer is forced to ask for an operator

Issue 2 – IVR not recognizing card; customer is forced to ask for an operator

Issue 1 – Web site asking clients to place a phone call to finalize card activation

Issue 1 – Web site asking clients to place a phone call to finalize card activation

| 7

The ClickFox platform connects data into Journeys…

TasksRaw Data Events Paths Journeys

Completions & Departures

Instant Transform All Events Connected

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

Unstructured IVR Logs

Structured Agent Logs

Retail Desktop Data

Web Logs

Cross Channel Outgoing Paths from IVR promise

to pay event

Cross Channel Outgoing Paths from web online

payment event

Enrollment Journeys

Churn Journeys

Agent Steps

Retail Steps

IVR Prompts

Web Pages

| 8

For example, US retail client used Cross-channel analysis was used to improve low digital/call to branch conversion

We would only need to enhance the e-Chat (and phone) presence

on selected pages

. . . and potential ways to optimize the discussion

to convert into leads

Looking at e-Chats in depth, we identified

2 key leakage points . . .

37.5% 19.4%0.3% 17.5%

62.5% 80.6%99.7% 82.5%

DeclineNo offerDiscoNo chat

Chat click

Chat start

Offer made

ConvertWeb

Web

Phone

Chat lead

Phone lead

<0.02%

Web lead

Nudging users from Web to phone or e-Chat would improve lead

creation significantly

1.3%

10%

0.1%

▪ Few leverage chat and phone from Web today▪ Those who do experience higher conversion

Greatest opportunity to increase chat use in▪ Shopping/vehicle overview pages (high chat usage)▪ Current offers pages (high usage and conversion)

▪ Reduce chats disconnected due to wait time▪ Increase rate of offering to contact dealer

▪ Systematically engage users (1) in longer chats and (2) on leasing and incentive topics to increase conversion

x10

x100

x1

E-Chat0.3%

| 9

#3 Continuous flow

With Big data, companies can uncover never

ending flow of highly specific opportunities

| 10

US Telco built out ClickFox big data platform over time, now 80 million sessions per month

0

10

20

30

40

50

60

70

80

SessionsMillions

20131109072005

Cross channel evaluation: client CTO performs internal/external evaluation of cross channel analytics platforms. Client picks CG as the platform

Broadband

repair: 5M sessions

Retail: 20M sessions

Additional Additional

products

Wireless, fixed/TV:

20 new sources of data across 4 business units (ongoing RFP)

Wireless: 34M sessions gradually added (software, hosting, maintenance and managed services)

output analytics” team to increase HOA: wireless business creates a “high output analytics” team to increase velocity of findings and change

Currently ingested

| 11

US Telco used the platform to drive shift to digital, with over $60M impact 3 years

4.0

0.5

3.5

3.0

2.5

2.0

1.5

0

40

35

10

5

0

1.0

5.0

4.5

30

25

20

15

65

60

55

50

45

Dec Feb

13

Feb

63

JanJan

6

Sep

5

20

Mar

58

Dec

54

Nov

50

Oct

16

12

Cumulative

10

Monthly

Nov

8

Oct

45

Sep

41

Aug

36

Jul

32

Jun

28

May

24

Apr

Cumulative*

Monthly

2010 2011 2012

| 12

Next best

action

▪ Retention

action:

email sent to customer with 50% discount on annual fee

▪ Timing:

Immediate response

Use case – Machine-enabled decision making to leverage churn prediction models for trigger-based retention actions

‘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…

Jan 1st Jan 31st

Existing model

1.0

0.8Retention Action

Threshold

0.6

0.4

Billing dispute

Product1 downgrade

Jan 13th

Mobile port out

Questions about disco. process

| 13

Use case – Leverage social media data for quicker, faster identification of root causes for revenue growth

Using

analytics to improve social

campaigns

3. Combine journey and social data to improve campaign

▪ Deep dive on leakage points during and after the campaign ▪ Use correlated social data from YouTube comments, Facebook posts, Twitter,

blogs, etc. to augment journeys to identify root causes of leakage or errors

3

1. Launch new campaign

▪ Launch program for cardholders on traditional and social sites (e.g., YouTube/Facebook)

1 2. Map journeys for customers

▪ Trace journeys including adoption, experience and pain points for the 1K customers clicking through to enroll

Churn

Billing

Truck RollNeg-SocPayment

Acct Changes

PaymentTruck Roll

Neg-Soc

Activation Billing

Plan First Bill Acct Details

PlanTech Supp

Tech SuppPlan

2

| 14

811 -24%

tofrom

Targeted capability building% of calls

Example - Spotting underperforming stores / leading to calls, identify root causes, test improvements, and track impact daily

3

5

5

6

13

Payment arrgt

SIM change

Bill review

Voicemail issues

Credit adjustment

… and 5 reasons (% call)

Root cause

In-store on phone boarding/upgrade process not followed through, leading in phone / voice mail activation and billing issues

Issue driven by 3 markets/30 stores …

378740711

754

321377713

901

101

742995

297

380377

785

Embedding in management system

▪ Daily KPI fed back in store (7 days call rate)

▪ Coaching approach▪ Deploying nationally

Journey insight▪ Phone upgrades highly

correlatedwith negative NPS (10 pts)

▪ 11% store visits driving calls after 7 days (2.5M calls in total)

Rep Name

FCR Rate for

2/10 - 3/9

FCR Rate for

2/17 - 3/16

Customers

Handled 2/17 -

3/16

Customers

Handled Callers Calls

ABDALLA, AHMED (RSC) 94.1% 94.0% 117 37 2 2

ALCALA, ELVIA L. (SSR) 100.0% 100.0% 7 4 0 0

ARVIZU, CESAR R. (ASM) 100.0% 100.0% 8 2 0 0

BARRIOS, RUBEN J. (RSC) 95.7% 95.6% 68 23 1 1

BLANKENSHIP, KELCY (SSR) 95.7% 95.0% 20 11 1 1

BOZOYAN, NAREG (SM) 100.0% 100.0% 7 0 0 0

BRIANO, ROSEMARY (SSR) 100.0% 100.0% 10 0 0 0

CARTER, MICHELLE R. (RSC) 91.9% 95.0% 60 12 0 0

CASTRO, ESMERALDA M. (ASM) 100.0% 100.0% 9 2 0 0

COPELAND, JADE A. (RSC) 87.0% 85.7% 49 15 2 3

Week of 2/17 - 2/23

Rep Name

FCR Rate for

2/10 - 3/9

FCR Rate for

2/17 - 3/16

Customers

Handled 2/17 -

3/16

Customers

Handled Callers Calls

ABDALLA, AHMED (RSC) 94.1% 94.0% 117 37 2 2

ALCALA, ELVIA L. (SSR) 100.0% 100.0% 7 4 0 0

ARVIZU, CESAR R. (ASM) 100.0% 100.0% 8 2 0 0

BARRIOS, RUBEN J. (RSC) 95.7% 95.6% 68 23 1 1

BLANKENSHIP, KELCY (SSR) 95.7% 95.0% 20 11 1 1

BOZOYAN, NAREG (SM) 100.0% 100.0% 7 0 0 0

BRIANO, ROSEMARY (SSR) 100.0% 100.0% 10 0 0 0

CARTER, MICHELLE R. (RSC) 91.9% 95.0% 60 12 0 0

CASTRO, ESMERALDA M. (ASM) 100.0% 100.0% 9 2 0 0

COPELAND, JADE A. (RSC) 87.0% 85.7% 49 15 2 3

Week of 2/17 - 2/23

| 15

#4 Management shift

The new management paradigm is about test

and learn at ever increasing scale, where

details matter

| 16

Connect data into journeys

Identify gaps, opportunities

for enhancement

Continuously

improve

Journeys

1

3

▪ Understand good & bad journeys, and root causes

▪ Develop cross-functional solutions

Prioritize journeys to improve2

▪ Identify journeys with highest impact

▪ Segment cus-tomers based on journey behavior

▪ Build single model across all channels

▪ Provide visualization of journeys

4

▪ Real-time journeys performance visualization

▪ Performance dashboard

▪ Integration with CRM

Measure & maintain

journeys

5

The new management paradigm is about test and learn at ever increasing scaleOverview of daily insight-to-action cycle

| 17

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

| 18

#5 Maybe …

| 1919

01/01/08 01/07/08 01/01/09 01/07/09 01/01/10 01/07/10 01/01/11

Positive SM GRP

Negative SM GRP

Neutral SM GRP

SOURCE: McKinsey

Actual Sales

Model

Promotions

Paid Search

Affiliates

Display

TV

Print specialist

Print general

Base and Price

TV halo

Input variables

Acquisitions vs. Model

Residual

Acquisitions explained by marketing activities

P value = 0.32%

P value = 0.17%

P value = 0.30%

P value = 0.34%

PZ

Computer

Year 1 Year 2 Year 3

Acquisitions

Social Media GRP

TV

Print

Paid Search

General Print

Specialist Print

Dependent variable

Explanatory variables(sample)

Acquisitions

Model illustration>10% 5% <1%

Variable significance (P value)Low Medium High

Year 1 Year 2 Year 3

Negative SM

Data driven marketing will require different capabilities (1/2)Example Measuring commercial effects of traditional and new media …

| 20

Acquisitions Response curves including Social Media

0

5

10

15

20

25

30

35

0 5 10 15 20 25

-25

-15

-5

5

15

25

35

TV

*TV Halo

Specialist Print

Paid Search

Affiliates

Display

General Print

Spend (€M)

Incremental Margin (€M)

Annual Social Media GRP

Incremental Margin (€M)

25020015010050

Negative Social Media

Data driven marketing will require different capabilities (1/2)Measuring acquisition response curves should include the Social Media effect

| 21SOURCE: McKinsey

Data driven marketing will require different capabilities (1/2)Disguised client example – Holistic Optimization of budget allocation

Optimised Acquisitions (with fixed total budget)

Retention Response curves (Losses model)

Spend (€M)Halo is an impact from investment in another product

Budget should be increased from 45M to 56M

100

102

104

106

108

110

35 45 55 65 75

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Incremental Margin minus Marketing Spend (€M)

Marginal ROI

Margin efficiency frontier

Marginal ROI=1

56

Total Budget (€M)

TV

*TV Halo

Specialist Print

Paid Search

Affiliates

Display

General Print

0

5

10

15

20

25

30

35

0 5 10 15 20 25

IncrementalMargin (€M)

TV

Spend (€M)

Specialist Print

General PrintDisplay

0

5

10

15

20

0 5 10 15 20 25

IncrementalMargin (€M)