Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
1. Big Data and Advanced Analytics
16 Use Cases From a Practitioner’s Perspective
June 27th, 2013
Workshop at Nasscom Conference – by Invitation
Any use of this material without specific permission of McKinsey & Company is strictly prohibited
2. McKinsey & Company | 1
Big Data and Advanced Analytics Pyramid
Make your own data,
for the problem at hand
Unfamiliar, unstructured
data. Acted upon
directly, often at scale
Unfamiliar, unstructured
data. Converted into
structured data. Acted
upon at scale
Familiar, structured
data. Acted upon at
scale
D
C
B
A
3. McKinsey & Company | 2
A. Familiar structured data, acted upon at scale
Selected examples
Campaign lead generation – finding the leads that are most
likely to result in incremental telecoms sales
2
Pricing – offering competitive prices only to the most sensitive
retail deposit customers, while maximizing value
4
Pricing – create transparency into B2B chemicals prices, to
enable more targeted price setting
1
Customer experience – knowing my hospitality customer’s
individual preferences, wherever the customer is travelling
3
4. McKinsey & Company | 3
Moving from across the board pricing
to differentiated targets, just using historical prices
0
100
200
300
400
500
600
700
800
900
1,000
Price per unit
Account sales
100,00010,0001,000100
0
100
200
300
400
500
600
700
800
900
1,000
Account sales
100,00010,0001,000100
Price per unit
Differentiated
price targets
One-size fits
all price target
DISGUISED EXAMPLE
From across the board pricing increase …
… to differentiated target-setting at customer-
product reflecting customer’s willingness to pay
SOURCE: McKinsey Value Advisor team
1
5. McKinsey & Company | 4
Telecoms companies are investing in big data infrastructure, bringing
together data from diverse sources
New services
Government,
urbanization,
and social good
Operations
Marketing
and sales
Big Data
available to
Telcos
Socio and
economic
analysis
Health care
and disease
prevention
ILLUSTRATIVE
2
Source: McKinsey Telecoms Practice, Integrated Incumbent Example
6. McKinsey & Company | 5
Create an integrated picture of the household, and its product/ brand
holdings
Full household product holdingFamily of 4
Rikke Hansen
Husband Kasper Hansen and their two kids
Storkevænget 8
2840 Holte
Household ID: 3512697
Customer ID: 3525300699
1 X Voice
1 X BB & Wifi
2 X Mobile
1 X Kids mobile
2 X Tablets
X3 HH
ARPU
1 X TV
+ HH services & solutions
First pilots with +20-50% take-rates
Source: McKinsey Telecoms Practice, Integrated Incumbent Example
2
7. McKinsey & Company | 6
This picture allows the telco operator to target offers tailored to the
product holdings at each household
15.0
HHs in the
country
5.0
Non-
customers
10.0
Currently
customer
in Group
2.0
Fully
covered
HHs1
2.5
Own mobile
and fixed
4.0
Own
fixed only
1.5
Own mobile
only
+ Competitor product holdings
Further sales potential
DISGUISED EXAMPLE
Most of this data was in the phone book
20 years ago, but was not actionable
First pilots with +20-50% take-rates
Source: McKinsey Telecoms Practice, Integrated Incumbent Example
Brand-product holdings of all households
Households, millions
2
8. McKinsey & Company | 7
Hospitality: know your customer… …everywhere in the world
▪ Commercial details
– Employer relationship
– Travel partnerships
– Payment/ credit card
▪ Room preferences
– No smoking
– Pool view
– Ground floor
▪ Personal preferences
– Welcome drink
– Entertainment
▪ Usage history
– Internet usage
– Fitness usage
– Restaurant meals
The hospitality industry captures and acts on customer preferences on
a multi-national basis
3
▪ Provide the same of personalized service
– Cloud based architecture
– Traditional architecture
▪ The data is not different from what was in
a paper based system
SOURCE: McKinsey Marketing practice
9. McKinsey & Company | 8
Deposit pricing based on statistical estimates of sensitivity allows
smart pricing at scale
4
Business as Usual
▪ Prices are set regionally
▪ Promotional pricing offered on new term
deposits
▪ When promotional pricing lapses
– Some customers leave
– Other roll-over their deposits
▪ Both promotional prices and go-to prices
have varied significantly
– Across regions
– Over time
– Relative to competition
With 1-2-1 Pricing approach
▪ Statistically predict customer’s sensitivity
to the price reduction
▪ Target the right price for each customer
LowHigh Price sensitivity
Fund for retention
offers, if needed
Interestrates
1-2-1 pricing
Traditional price
DISGUISED EXAMPLE
SOURCE: McKinsey Banking CVM Service Line
10. McKinsey & Company | 9
Applying individual level price elasticities can yield significant impact
-200
+800
+1.000
+200
Evolution of price
list interest rate
and cost of fund-
ing vs. competi-
tors’ price list
Index
Yearly TD volume
growth (Million €)
After 3 months:
1st step of differentiation
with 2-5 ratesBefore 1-2-1
100
100100
101
99
96
93
9292
Competitors’ top
interest rate
Bank’s top interest rate
Bank’s average booked
interest rate1
Growth vs. market
After 6 months:
2nd step of differentiation
with 10 rates
-15% - +15%
Δ=40bps
Δ=90bps
1 Average of contracts opened or renewed in the period
SOURCE: McKinsey Banking CVM Service Line
4
DISGUISED EXAMPLE
11. McKinsey & Company | 10
Big Data and Advanced Analytics Pyramid
Make your own data,
for the problem at hand
Unfamiliar, unstructured
data. Acted upon
directly. Sometimes at
scale
Unfamiliar, unstructured
data. Converted into
structured data. Acted
upon at scale
Familiar, structured
data. Acted upon at
scale
D
C
B
A
12. McKinsey & Company | 11
-25
-20
-15
-10
-5
0
5
10
Print + Online
Net of price (change)
impact
Competi-
tors Online
Competitors TV
Price
Announcements
Price Gap
Retention
Losses
Advanced Marketing Mix Modeling identifies the impact
of marketing actions on sales/ churn
Churn (retention) model
Thousands of customers per month
TV
4
5
3
2
6
7
1
DISGUISED EXAMPLE
5
SOURCE: McKinsey Marketing Practice
13. McKinsey & Company | 12
This approach captures social media “buzz”, such as
comments on facebook and twitter, as marketing inputs
Breakdown of drivers of customer acquisitions by marketing activity
Percent
Print
special
Print
general
Base,
incl.
price
Negative
Social Media
TVSearchDisplayAffiliate
7.3
8.3
3.6
-7.88.5
9.0
8.4
EUR -36 million profit
loss, can be fixed with
EUR 0.8 million
investment
5 DISGUISED EXAMPLE
SOURCE: McKinsey Marketing Practice
14. McKinsey & Company | 13
Supermarket purchase data
(captured through loyalty programs)
Mobile phone usage data
(pre-paid or post-paid)
SME supplier data
(e.g., brewery supply to stores
and bars)
SME customer data
(e.g., eBay)
Utility data (e.g., electricity
consumption and payment)
Case example: A supermarket JV
in Central America
▪ 3 models built using only supermarket
transactions and age (as loyalty
program captures date of birth):
– Risk
– Income
– Need-based
segmentation
▪ Risk model is used for pre-screening
and selective pre-approval (GINI: 37)
▪ Income model is used to assign lines
(45% correlation with payroll income)
▪ Segmentation is used to target
customers for specific campaigns
(e.g., credit card vs. personal loan for
specific appliances on sale)
Shopping basket data provided a Latin American bank with rich
insights into credit risk in the unbanked segment
6
SOURCE: McKinsey Risk Practice
15. McKinsey & Company | 14
Advanced Next Product To Buy (NPTB) algorithms integrate long term
behavior with the most recent data to make smarter offers
7
Market Basket Analysis Bayesian Rules Engine
Classical market basket analysis is well known from
leading “bricks and mortar” retailers
“Market Basket Analysis” links conditions with product
uptake, e.g., IF affluent AND increased monthly salary
AND Family with <__> THEN x% probability of hiring
mortgage
Basket = single trip, i.e. customers buy A and B together
in one trip
Basket = all purchases of one customer within last
year(s),
i.e. customers who read A also read B
“Market basket” includes years of transaction history, as
well as the most recent web-browsing behavior
Next
recommendationPurchase history
Next
recommendationPurchase history
Next
recommendation
Product
portfolio
Transactional
behavior
Contact
history
and other
Socio-demogr
aphics
Basket = collection of customer
specific data including …
iPhone
SOURCE: McKinsey Marketing Practice
16. McKinsey & Company | 15
Next-Product-to-Buy probabilities guide in branch/ store sales efforts,
promotions and product recommendations
SOURCE: McKinsey Marketing Practice
Customer
Likelihood of buying in the next month
% by product
Long term
loans
32%
Owns
15%
Savings
account
Owns
89%
Owns
Pensions
39%
22%
15%
Short
term
loans
Owns
10%
21%
Cards
Owns
12%
40%
Current
account
87%
64%
60%
Invest-
ment
funds
Owns
97%
Owns
Product Probability
87%
Investment fund 97%
Current account 95%
Recommendation – Customer View
Current account
Customer Probability
40%
32%
97%
Recommendation – CLV view
Recommendation
engines (next
product/service/
application) to buy can
deliver 3-5% revenue
uplift
Long term loans
Investment fund
Credit cards
7
DISGUISED EXAMPLE
17. McKinsey & Company | 16
Cross channel data integration tools like Click Fox* now allow firms to
see customers’ experiences across channels
Business as Usual
Multiple customer touch
points, each with its own
infrastructure and data
Click Fox Integration*
Brings the diverse data
sources together
Organizes into meaningful
customer journeys
Customer Journeys
Product feature search
Specific non-standard business
process
Service or dispute resolution
Intuitive use case
Manage customer
experience and
satisfaction
Emerging use cases
Estimate credit risk
Estimate churn
likelihood
Target preferred
channels
8
SOURCE: McKinsey Marketing Practice; * McKinsey has invested in Click Fox
18. McKinsey & Company | 17
Big Data and Advanced Analytics Pyramid
Make your own data,
for the problem at hand
Unfamiliar, unstructured
data. Acted upon
directly. Sometimes at
scale
Unfamiliar, unstructured
data. Converted into
structured data. Acted
upon at scale
Familiar, structured
data. Acted upon at
scale
D
C
B
A
19. McKinsey & Company | 18
C. Unfamiliar or unstructured data, acted upon at scale, directly
Selected examples
Discount targeting – using location data to offer discount
coupons redeemable to the nearest store
10
Discount targeting – using transactional spending data from
banks or networks
12
Fraud prevention – by matching the location of mobile
phone with a credit or debit card transaction
9
Discount targeting – using speech analytics to identify
customers who are most likely to attrite
11
13 Advertising targeting – using browsing history to target web
site visitors with the most relevant adverts
20. McKinsey & Company | 19
Joint venture between a telecom operator and credit card issuer uses
location information to reduce fraud
How banks use telco data to fight fraud…
▪ A large EU bank is leveraging data from a Telco
company to identify fraud by crossing Card
transactions and mobile data
The Bank sees a
transaction in Spain
from your Card
The Telco operator
knows you are in
Norway
Fraud?
9
…but are still trying to forge a
workable governance approach
Does the telco have the
right to sell a customer’s
location to a bank?
Is the bank obliged to
take the data from the
telco? Preventing that
fraud, help protect other
banks and customers?
Should the customer
opt-in and instruct the
bank to get this data
from the telco?
What does it cost? Who
should pay?
SOURCE: McKinsey Banking Practice, Interviews with industry experts
21. McKinsey & Company | 20
A leading payment network’s joint venture with a retailer is an early
example of using real-time, location data to target customer offers
SOURCE: Mobile Marketing Watch
A payment network and a retailer use real-time
transaction data to make time and location sensitive
offers to customers…
… made possible by information
management capabilities
ability to process and
analyze transactions
in real-time…goes well
beyond processing
purchases to delivering
critical information that
benefits
consumers, merchants, a
nd financial institutions.
▪ Other payment providers and
start-ups looking to provide
similar offerings
▪ Role banks will play in this trend
is not yet clear
SMS coupon for closest
retail outlet is pushed
immediately to
customer
Customer makes
purchase at juice bar
while shopping using
the network’s card
Customer can use
coupon in the nearest
retail location
The network matches
customer, purchase
location, and qualifying
offer in
real-time
1 2
4 3
10
22. McKinsey & Company | 21
Speech analytics enables Telco operators to analyze phone calls to
help provide more tailored offers to customers
11
The privacy and legal questions are typically harder than the analytics
Some companies are testing technology to
analyze telephone calls on their network
(electronically)…
… and trigger responses or
actions based on key
words
…and gets a long-duration
retention offer?
…and gets a promotion on
a Caribbean cruise?
Customer mentions the
name of a competitor…
Customer mentions a
forth- coming holiday…
SOURCE: McKinsey interviews with industry experts
23. McKinsey & Company | 22
Banks and card companies are looking to release the value in their
transaction data assets
Merchant-funded reward programs
Banks are monetizing behavioural data to
deliver highly-targeted offers to customers
Insight development and services
Card networks provide analytics services for
merchants, that range from pre-packaged
reports to customized consultations
Customers
opt in
Offer
Bank matches
relevant offers &
customers
Offer
Bank matches
relevant offers &
customers
Reinforcement
Response
tracked & shared
with merchants
and customers
Reinforcement
Response
tracked & shared
with merchants
and customers
Redemption
Offer redeemed
by customers
(e.g., mobile
download, real-
time at POS)
Merchants set
customer
aspiration
Presentment
Offer presented
to customers
Presentment
Offer presented
to customers
Full or pilot programs at multiple banks in
North America
Example services:
▪ Benchmark analytics: analyzes merchant
performance against the industry
category, or specific business competitors
▪ Portfolio Analytics: provides users access
to own transactional data through an
extensive set of reports in multiple
categories
▪ Macro-economic spend indicators:
reports consumer spending in multiple
vertical industries, based on aggregate
activity in the payments network, coupled
with estimates for cash and checks
12
SOURCE: McKinsey Banking Practice, Interviews with industry experts
24. McKinsey & Company | 23
A leading US based bank uses web browsing data to serve targeted
pages to prospects or visitors
Risk
models
Segment specific websitesInputs – information used
How they
enter the
site
Internet
specific
data
Surfing
history
▪ Use internet data
and score customers
before the website is
loaded
▪ Need to score
customers on
models in <0.5s
Low risk saver
Higher risk borrower
▪ Location
▪ Aggregated social
media data
▪ Cookie information on
past sites visited
▪ Some sites associated
with low risk
▪ Other sites with higher
risk e.g. social media
▪ Natural search
▪ Sponsored search –
Brand names, credit
terms
▪ Banner adverts
▪ Aggregators
13
SOURCE: McKinsey Banking Practice, Interviews with industry experts
25. McKinsey & Company | 24
Big Data and Advanced Analytics Pyramid
Make your own data,
for the problem at hand
Unfamiliar, unstructured
data. Acted upon
directly. Sometimes at
scale
Unfamiliar, unstructured
data. Converted into
structured data. Acted
upon at scale
Familiar, structured
data. Acted upon at
scale
D
C
B
A
26. McKinsey & Company | 25
D. Unfamiliar or unstructured data, acted upon at scale, directly
Selected examples
Pricing and Advertising targeting – learning the right price
(i.e. odds) and the right landing page to show each visitor to a
gaming website, using on-going experimentation
15
Advertising targeting – learning the right landing page to
show each visitor, using on-going experimentation
14
Credit line management – learning the right credit line to both
profitably and responsibly offer each account, with on-going
experimentation
16
27. McKinsey & Company | 26
A leading European Bank runs experiments on the bank’s web site to
find out which visitor should be served which page
14
Customer logs on to
bank website
Bank knows product
holding, segment, his
torical behavior
Plus recent product
enquiry/ browsing
behaviour
“Champion” offer suite
▪ Shown ~90% of the time
▪ Reflects, recent behavioural
history, long term product holdings
and customer segmentation
▪ Maximizes value, based on
current knowledge
“Challenger” screens #1-5
▪ Each shown ~2% of the time
▪ Learn if customer
preferences or market
conditions have changed
SOURCE: Bank investor day presentation
+
Behavioural targeting results in a 27% lift in banner click through,
and 12% increase in sales
28. McKinsey & Company | 27
Similarly, a gaming web site uses browsing history AND
experimentation to learn about the right offers
15
ILLUSTRATIVE
Customer logs on
to gaming website
Website knows his
historical behavior
“Champion” screen
▪ Shown 95% of the time
▪ Maximizes value, based on
current knowledge
“Challenger” screens #1-5
▪ Shown 1% of the time
▪ Learns of customer’s
preferences or market
conditions have changed
SOURCE: McKinsey Marketing practice, Industry interviews
29. McKinsey & Company | 28
Industry leaders invest making-your-own-data,
even in sensitive areas like credit line management
SOURCE: McKinsey Risk Practice, Industry interviews
1 2 3 4 5 6 7 8 9 10
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Low line test: 2.5% of applicants
Optimal credit line: 95% of credit applicants
High line test: 2.5% of applicants
ILLUSTRATIVE
Credit line allocation by risk band
Currency units ($, £ etc.)
Low risk applicantsHigh risk applicants
Industry leaders invest millions of $s in champion-challenger experiments,
that mature over 3 or more years, to learn how their strategies can be improved
16
30. McKinsey & Company | 29
Education BA from St. Stephen’s College, Delhi
MBA from University of Chicago, Graduate School of Business
Work
experience
McKinsey experience includes:
Consumer banking
B2B marketing
Functional focus
Advanced Analytics, Customer Lifecycle Management, Credit Risk
Sectors
Financial Services, Consumer Products
Prithvi Chandrasekhar
Senior Expert, Marketing, London Office
@McK_CMSOForum
www.youtube.com/McKinseyCMSOforumwww.cmsoforum.mckinsey.comWWW
http://www.slideshare.net/McK_CMSOForum
Stay Connected: