The document discusses how big data and analytics can help companies gain insights and drive business impact. It provides examples of how companies have used big data platforms and cross-channel analytics to improve customer experiences, increase revenue, reduce costs, and implement a continuous process of testing and learning. Specifically, it highlights how one telecommunications company increased digital sales by over $60 million in three years by leveraging big data analytics to optimize marketing campaigns and customer journeys.

1. Powering GROWTH with BIG DATA
powered by
November 2014

2. | 1
#1 Big Data
The Big Data storm is
real – but not easy to
navigate

3. There’s a hrowing torrent of data, sophisticated analytical tools
x % of data in digital form
| 2
Available data is exploding
Exabytes
8,000
SOURCE: McKinsey Global Institute; Hilbert and López, “The world’s technological capacity to store, communicate, and
compute information,” Science, 2011
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
1986 1993 2000 2007 2011 2015
3% 25% 94% 99,0% 99,9%

4. | 3
Most companies store more data than the size of the
entire collection of the library of US Congress…
Average stored data per firm with more than 1,000 employees (Terabytes, 2009)
Securities and Investment Services
Banking
Communications and Media
Government
Discrete Manufacturing 967
870
831
825
801
536
370
319
278
1,507
Insurance
Process Manufacturing
Resource Industries
Transportation
Retail
Wholesale
Professional Services
>500=WalMart data warehouse in 2004
US EXAMPLE
235 = Library of congress collection in 2011
SOURCE: IDC: US bureau of labor statistics; McKinsey global institute analysis
1,800
231
697
Utilities
Healthcare Providers
1,312
1,931
3,866
Education
Construction

5. | 4
…however in a very fragmented and not consistent ways
Structured
and
unstructured
Hadoop
Massive parallel
processing,
XXX
500m
to 5bn
touch points
per year
Disparate
customer data
sources
agent 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!

6. | 5
#2 Big Data
There’s tremendous
value in applying big data
technology to drive
business impact

7. Example of Journeys leading to low CSAT in cards – card activation example
| 6
With ClickFox, clients connect customer touchpoint data to
create transparency on cross-channel journeys
E-mail
Agent
chat
Stores /
branches
Mobile /
SMS
Social
media
Field
Web
Call
center
Customer
database
Interactive
Voice
Response
SOLVING THE “DATA CHALLENGE”
▪ Quick set-up, no need for major IT investments
Issue 1 – Web site asking
clients to place a phone call
to finalize card activation
▪ Aggregating Bns of touchpoints sitting in 20+
systems — and proliferating quickly
▪ Managing structured and unstructured / messy
data
Issue 2 – IVR not recognizing
card; customer is forced to ask
for an operator
▪ 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

8. | 7
The ClickFox platform connects data into Journeys…
Raw Data Events Paths Tasks Journeys
Completions &
Departures
Instant Transform All Events
Connected
Paths to
Outcomes
Failed Web Pay
Enrollment
IVR Call
IVR Pay
By Phone
Transfer to
Agent
Agent New
Account
Web Auto
Pay Success
Web Pay
Confirmed
Info
Unstructured IVR Logs
Web Logs
Structured Agent Logs
Retail Desktop Data
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

9. 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
| 8
. . . and potential ways to optimize the discussion
to convert into leads
Nudging users from Web to phone or e-Chat would improve lead
creation significantly
0.1%
Looking at e-Chats in depth, we identified
2 key leakage points . . .
No chat Disco No offer Decline
99.7% 62.5% 82.5% 80.6%
Chat
click
Chat
start
Offer
made
x1
Web Convert
0.3% 37.5% 17.5% 19.4%
Web
Phone
Chat
lead
Phone
lead
<0.02%
Web
lead
1.3%
10%
▪ 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
0.3% E-Chat

10. | 9
#3 Continuous flow
With Big data, companies
can uncover never
ending flow of highly
specific opportunities

11. Currently ingested
Wireless, fixed/TV:
20 new sources of
data across 4
business units
(ongoing RFP)
| 10
US Telco built out ClickFox big data platform
over time, now 80 million sessions per month
Sessions
Millions
80
70
60
50
40
30
20
10
0
Cross channel evaluation: client CTO
performs internal/external evaluation of
cross channel analytics platforms. Client
picks CG as the platform
HOA: wireless business creates a
“high velocity of findings and change
Broadband
repair: 5M
sessions
Retail: 20M
sessions
Additional
products
output analytics” team to increase
Wireless: 34M
sessions gradually
added (software,
hosting, maintenance
and managed services)
2005 07 09 11 2013

12. 65
60
55
50
45
| 11
US Telco used the platform to drive shift to digital,
with over $60M impact 3 years
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
0.5
0
40
35
10
5
0
1.0
30
25
20
15
13
Dec Feb
Monthly
63
Feb
54
50
41
36
32
28
24
Jan Jan
6
5
Sep
20
Mar
58
Dec
Nov
Oct
16
12
Cumulative
10
Monthly
8
Nov
Oct
45
Sep
Aug
Jul
Jun
May
Apr
Cumulative*
2010 2011 2012

13. Next best
action
▪ Retention
action:
email sent
to customer
with 50%
discount on
annual fee
▪ Timing:
Immediate
response
| 12
Use case – Machine-enabled decision making to leverage
churn prediction models for trigger-based retention actions
An example of a real customer journey leading to churn…
‘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
Mobile port out
Retention Action
Threshold
Product1
downgrade
Billing
dispute
Existing
model
1.0
0.8
0.6
0.4
Jan 1st Jan 31st
Jan 13th
Questions
about
disco.
process

14. | 13
Use case – Leverage social media data for quicker, faster
identification of root causes for revenue growth
1 2. Map journeys for customers
Using
analytics to
improve social
campaigns
1. Launch new campaign
▪ Launch program for cardholders on
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
traditional and social sites (e.g.,
YouTube/Facebook)
▪ Trace journeys including adoption,
experience and pain points for the
1K customers clicking through to
enroll
Plan First Bill Acct
Chur
n
Billin
g
Payment Neg-Soc Truck Roll
Acct
Changes
Truck Payment
Roll
Neg-
Soc
Activation Billin
g
Deta
ils
Plan
Tech
Supp
Plan Tech Supp
2

15. Example - Spotting underperforming stores / leading to calls,
identify root causes, test improvements, and track impact daily
| 14
Targeted capability
building
% of calls
8
11 -24%
from to
901
101
… and 5 reasons (% call)
3
5
5
6
995
13
Credit adjustment
Bill review
Voicemail issues
Payment arrgt
SIM change
Journey insight
▪ Phone upgrades highly
correlated
with negative NPS (10 pts)
▪ 11% store visits driving
calls after
7 days (2.5M calls in total)
Week of 2/17 - 2/23
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 …
740378
711
754
377321
713
742
297
380
785
377
Embedding in management
system
▪ Daily KPI fed back in store
(7 days call rate)
▪ Coaching approach
▪ Deploying nationally
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

16. | 15
#4 Management shift
The new management
paradigm is about test
and learn at ever
increasing scale, where
details matter

17. The new management paradigm is about test and learn at ever
increasing scale
Overview of daily insight-to-action cycle
▪ Build single model across
all channels
▪ Provide visualization of
journeys
▪ Identify
journeys with
highest impact
▪ Segment cus-tomers
based
| 16
Connect data into journeys
Prioritize 2 journeys to improve
Continuously
improve
Journeys
Measure & maintain
journeys
Identify gaps, opportunities
for enhancement
1
3
on journey
behavior
▪ Understand good & bad
journeys, and root causes
▪ Develop cross-functional
solutions
4
▪ Real-time journeys
performance
visualization
▪ Performance dashboard
▪ Integration with CRM
5

18. | 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
“Password reset” failure issue
Success
58%
Answer
secret ques-tion
and
reset pass-word
Secret
answer error
41%
Other error
1%
Other
disposition
84%
▪ Education
codes
▪ General info
▪ Features
related
assistance
▪ Payments
released
assistance
Reset
password
16%
Did not to
Web <30
days
51%
Did not
return to
Web <30
days
49%
Success
19%
“Cannot
remember
answer”
2%
Click to chat
~0%
Call agent
<1 day
15%
IVR and
hang-up <1
day
5%
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

19. | 18
#5 Maybe …

20. | 191
9
Data driven marketing will require different capabilities (1/2)
Example Measuring commercial effects of traditional and new media …
Display
Variable significance (P value)
Low Medium High
Negative SM
01/01/08 01/07/08 01/01/09 01/07/09 01/01/10 01/07/10 01/01/11
Social Media GRP
Positive SM GRP
Negative SM GRP
Neutral SM GRP
Acquisitions
SOURCE: McKinsey
Actual
Sal
es
Model
Promotions
Paid Search
Affiliates
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
TV
Print
Paid Search
General Print
Specialist Print
Dependent variable
Explanatory variables
(sample)
Acquisitions
Model illustration
>10% 5% <1%
Year 1 Year 2 Year 3

21. | 20
Data driven marketing will require different capabilities (1/2)
Measuring acquisition response curves should include the Social Media effect
Acquisitions Response curves including Social Media
35
30
25
20
15
10
5
0
Specialist Print
Display
General Print
0 5 10 15 20 25
Incremental Margin (€M)
35
25
15
5
-5
-15
-25
TV
*TV Halo
Paid Search
Affiliates
Spend (€M)
Incremental Margin (€M)
50 100 150 200 250
Annual Social Media GRP
Negative Social Media

22. Data driven marketing will require different capabilities (1/2)
Disguised client example – Holistic Optimization of budget allocation
Optimised Acquisitions (with fixed total budget)
Specialist Print
*TV Halo
Paid Search
Display
Affiliates
General Print
35
30
25
20
15
10
5
0
0 5 10 15 20 25
Incremental
Margin (€M)
Halo is an impact from investment in another product Spend (€M)
Retention Response curves (Losses model)
Budget should be increased from 45M to 56M
Incremental Margin minus Marketing Spend (€M)
110
108
106
104
102
100
Marginal ROI
Margin efficiency frontier
Marginal ROI=1
56
35 45 55 65 75
1.8
1.6
1.4
1.2
1.0
0.8
0.6
Total Budget (€M)
TV
Incremental
Margin (€M)
TV
Display
Specialist Print
0 5 10 15 20 25
Spend (€M)
General Print
20
15
10
5
0
SOURCE: McKinsey | 21