SlideShare a Scribd company logo
1 of 30
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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:

More Related Content

What's hot

Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogDATAVERSITY
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
 
DAS Slides: Enterprise Architecture vs. Data Architecture
DAS Slides: Enterprise Architecture vs. Data ArchitectureDAS Slides: Enterprise Architecture vs. Data Architecture
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
 
Data Analytics Strategy
Data Analytics StrategyData Analytics Strategy
Data Analytics StrategyeHealthCareers
 
Business Intelligence & Data Analytics– An Architected Approach
Business Intelligence & Data Analytics– An Architected ApproachBusiness Intelligence & Data Analytics– An Architected Approach
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
 
Best Practices in Metadata Management
Best Practices in Metadata ManagementBest Practices in Metadata Management
Best Practices in Metadata ManagementDATAVERSITY
 
Business Data Lake Best Practices
Business Data Lake Best PracticesBusiness Data Lake Best Practices
Business Data Lake Best PracticesCapgemini
 
Customer-Centric Data Management for Better Customer Experiences
Customer-Centric Data Management for Better Customer ExperiencesCustomer-Centric Data Management for Better Customer Experiences
Customer-Centric Data Management for Better Customer ExperiencesInformatica
 
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
 
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing
Emerging Trends in Data Architecture – What’s the Next Big ThingEmerging Trends in Data Architecture – What’s the Next Big Thing
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
 
Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
 
Future of Data and AI in Retail - NRF 2023
Future of Data and AI in Retail - NRF 2023Future of Data and AI in Retail - NRF 2023
Future of Data and AI in Retail - NRF 2023Rob Saker
 
Data Lake Architecture
Data Lake ArchitectureData Lake Architecture
Data Lake ArchitectureDATAVERSITY
 
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...Smart City
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise AnalyticsDATAVERSITY
 
Improving Data Literacy Around Data Architecture
Improving Data Literacy Around Data ArchitectureImproving Data Literacy Around Data Architecture
Improving Data Literacy Around Data ArchitectureDATAVERSITY
 

What's hot (20)

Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data Catalog
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
 
DAS Slides: Enterprise Architecture vs. Data Architecture
DAS Slides: Enterprise Architecture vs. Data ArchitectureDAS Slides: Enterprise Architecture vs. Data Architecture
DAS Slides: Enterprise Architecture vs. Data Architecture
 
Data Analytics Strategy
Data Analytics StrategyData Analytics Strategy
Data Analytics Strategy
 
Business Intelligence & Data Analytics– An Architected Approach
Business Intelligence & Data Analytics– An Architected ApproachBusiness Intelligence & Data Analytics– An Architected Approach
Business Intelligence & Data Analytics– An Architected Approach
 
Best Practices in Metadata Management
Best Practices in Metadata ManagementBest Practices in Metadata Management
Best Practices in Metadata Management
 
Business Data Lake Best Practices
Business Data Lake Best PracticesBusiness Data Lake Best Practices
Business Data Lake Best Practices
 
Customer-Centric Data Management for Better Customer Experiences
Customer-Centric Data Management for Better Customer ExperiencesCustomer-Centric Data Management for Better Customer Experiences
Customer-Centric Data Management for Better Customer Experiences
 
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...
 
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
 
Emerging Trends in Data Architecture – What’s the Next Big Thing
Emerging Trends in Data Architecture – What’s the Next Big ThingEmerging Trends in Data Architecture – What’s the Next Big Thing
Emerging Trends in Data Architecture – What’s the Next Big Thing
 
8 Steps to Creating a Data Strategy
8 Steps to Creating a Data Strategy8 Steps to Creating a Data Strategy
8 Steps to Creating a Data Strategy
 
Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)
 
Future of Data and AI in Retail - NRF 2023
Future of Data and AI in Retail - NRF 2023Future of Data and AI in Retail - NRF 2023
Future of Data and AI in Retail - NRF 2023
 
Data Lake Architecture
Data Lake ArchitectureData Lake Architecture
Data Lake Architecture
 
Digital Transformation Frameworks
Digital Transformation FrameworksDigital Transformation Frameworks
Digital Transformation Frameworks
 
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...
Digital and Innovation Strategies for the Infrastructure Industry: Tim McManu...
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics
 
Improving Data Literacy Around Data Architecture
Improving Data Literacy Around Data ArchitectureImproving Data Literacy Around Data Architecture
Improving Data Literacy Around Data Architecture
 

Viewers also liked

Mckinsey presentation template
Mckinsey presentation templateMckinsey presentation template
Mckinsey presentation templatetriphos
 
Big Data in Retail - Examples in Action
Big Data in Retail - Examples in ActionBig Data in Retail - Examples in Action
Big Data in Retail - Examples in ActionDavid Pittman
 
Big Data Use Cases
Big Data Use CasesBig Data Use Cases
Big Data Use Casesboorad
 
Consulting toolkit structuring the problem
Consulting toolkit   structuring the problemConsulting toolkit   structuring the problem
Consulting toolkit structuring the problemchrisdoran
 
What MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and SurveysWhat MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and SurveysBusiness Over Broadway
 
Manu carricano gpes berlin 2013 ppt
Manu carricano gpes berlin 2013 pptManu carricano gpes berlin 2013 ppt
Manu carricano gpes berlin 2013 pptManu Carricano, PhD
 
Open data for startups Manifesto 2016
Open data for startups Manifesto 2016Open data for startups Manifesto 2016
Open data for startups Manifesto 2016Manu Carricano, PhD
 
Implementing Semantic Search
Implementing Semantic SearchImplementing Semantic Search
Implementing Semantic SearchPaul Wlodarczyk
 
Mapping the customer experience: innovate using customer experience journey maps
Mapping the customer experience: innovate using customer experience journey mapsMapping the customer experience: innovate using customer experience journey maps
Mapping the customer experience: innovate using customer experience journey mapsJoyce Hostyn
 
Slide guide for consulting-style presentations
Slide guide for consulting-style presentationsSlide guide for consulting-style presentations
Slide guide for consulting-style presentationsreallygoodppts
 
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | Edureka
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | EdurekaDocker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | Edureka
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | EdurekaEdureka!
 
Transforming Customer Experience: From Moments to Journeys
Transforming Customer Experience: From Moments to JourneysTransforming Customer Experience: From Moments to Journeys
Transforming Customer Experience: From Moments to JourneysMcKinsey on Marketing & Sales
 
Bcg Consultants Love Life
Bcg  Consultants Love LifeBcg  Consultants Love Life
Bcg Consultants Love Lifenitinagarwalin
 

Viewers also liked (19)

Customer Journey Analytics and Big Data
Customer Journey Analytics and Big DataCustomer Journey Analytics and Big Data
Customer Journey Analytics and Big Data
 
McKinsey presentation
McKinsey presentationMcKinsey presentation
McKinsey presentation
 
Mckinsey presentation template
Mckinsey presentation templateMckinsey presentation template
Mckinsey presentation template
 
Big Data in Retail - Examples in Action
Big Data in Retail - Examples in ActionBig Data in Retail - Examples in Action
Big Data in Retail - Examples in Action
 
Big Data & Analytics Client Examples
Big Data & Analytics Client ExamplesBig Data & Analytics Client Examples
Big Data & Analytics Client Examples
 
Big Data Use Cases
Big Data Use CasesBig Data Use Cases
Big Data Use Cases
 
Problem Solving
Problem SolvingProblem Solving
Problem Solving
 
Consulting toolkit structuring the problem
Consulting toolkit   structuring the problemConsulting toolkit   structuring the problem
Consulting toolkit structuring the problem
 
Ibm ddg 2015
Ibm   ddg 2015Ibm   ddg 2015
Ibm ddg 2015
 
What MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and SurveysWhat MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and Surveys
 
Manu carricano gpes berlin 2013 ppt
Manu carricano gpes berlin 2013 pptManu carricano gpes berlin 2013 ppt
Manu carricano gpes berlin 2013 ppt
 
Open data for startups Manifesto 2016
Open data for startups Manifesto 2016Open data for startups Manifesto 2016
Open data for startups Manifesto 2016
 
Implementing Semantic Search
Implementing Semantic SearchImplementing Semantic Search
Implementing Semantic Search
 
B2B Digital Sales - Sell the buyer’s way
B2B Digital Sales - Sell the buyer’s wayB2B Digital Sales - Sell the buyer’s way
B2B Digital Sales - Sell the buyer’s way
 
Mapping the customer experience: innovate using customer experience journey maps
Mapping the customer experience: innovate using customer experience journey mapsMapping the customer experience: innovate using customer experience journey maps
Mapping the customer experience: innovate using customer experience journey maps
 
Slide guide for consulting-style presentations
Slide guide for consulting-style presentationsSlide guide for consulting-style presentations
Slide guide for consulting-style presentations
 
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | Edureka
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | EdurekaDocker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | Edureka
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | Edureka
 
Transforming Customer Experience: From Moments to Journeys
Transforming Customer Experience: From Moments to JourneysTransforming Customer Experience: From Moments to Journeys
Transforming Customer Experience: From Moments to Journeys
 
Bcg Consultants Love Life
Bcg  Consultants Love LifeBcg  Consultants Love Life
Bcg Consultants Love Life
 

Similar to Big Data and Advanced Analytics

Mikaili Lilly
Mikaili LillyMikaili Lilly
Mikaili LillyFNian
 
Big Data World presentation - Sep. 2014
Big Data World presentation - Sep. 2014Big Data World presentation - Sep. 2014
Big Data World presentation - Sep. 2014Wing Yuen Loon
 
How to monetize and generate revenues from data services in a competitive market
How to monetize and generate revenues from data services in a competitive marketHow to monetize and generate revenues from data services in a competitive market
How to monetize and generate revenues from data services in a competitive marketcVidya Networks
 
Guide To Segmentation 1231969935172574 1
Guide To Segmentation 1231969935172574 1Guide To Segmentation 1231969935172574 1
Guide To Segmentation 1231969935172574 1bdereus
 
2014 Customer Loyalty ASEAN Conference: Loyalty Prime
2014 Customer Loyalty ASEAN Conference: Loyalty Prime2014 Customer Loyalty ASEAN Conference: Loyalty Prime
2014 Customer Loyalty ASEAN Conference: Loyalty PrimeJim D Griffin
 
W.UP Sales.UP digital sales and engagement tool for banks
W.UP Sales.UP digital sales and engagement tool for banksW.UP Sales.UP digital sales and engagement tool for banks
W.UP Sales.UP digital sales and engagement tool for banksW.UP
 
Banalytics - Monetizing corporate big data | Instarea
Banalytics - Monetizing corporate big data | InstareaBanalytics - Monetizing corporate big data | Instarea
Banalytics - Monetizing corporate big data | InstareaMatej Misik
 
Delivering Smarter Customer Interactions
Delivering Smarter Customer Interactions Delivering Smarter Customer Interactions
Delivering Smarter Customer Interactions Amy Cross
 
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”InfoCision Management Corporation
 
The Age Of New Reality Marketing V5.1 Final
The Age Of New Reality Marketing V5.1 FinalThe Age Of New Reality Marketing V5.1 Final
The Age Of New Reality Marketing V5.1 FinalTony Mooney
 
IBM Transforming Customer Relationships Through Predictive Analytics
IBM Transforming Customer Relationships Through Predictive AnalyticsIBM Transforming Customer Relationships Through Predictive Analytics
IBM Transforming Customer Relationships Through Predictive AnalyticsSFIMA
 
Data Science Use cases in Banking
Data Science Use cases in BankingData Science Use cases in Banking
Data Science Use cases in BankingArul Bharathi
 
Aegon Americas: Leveraging leading positions in workplace and individual solu...
Aegon Americas: Leveraging leading positions in workplace and individual solu...Aegon Americas: Leveraging leading positions in workplace and individual solu...
Aegon Americas: Leveraging leading positions in workplace and individual solu...Aegon
 
Multichannel Retention Strategies: A Steady Diet of Low Hanging Fruit
Multichannel Retention Strategies: A Steady Diet of Low Hanging FruitMultichannel Retention Strategies: A Steady Diet of Low Hanging Fruit
Multichannel Retention Strategies: A Steady Diet of Low Hanging FruitVivastream
 
Innovations in marketing effectiveness measurement
Innovations in marketing effectiveness measurement  Innovations in marketing effectiveness measurement
Innovations in marketing effectiveness measurement Michael Wolfe
 

Similar to Big Data and Advanced Analytics (20)

10. FMCG Analytics.pdf
10. FMCG Analytics.pdf10. FMCG Analytics.pdf
10. FMCG Analytics.pdf
 
Mikaili Lilly
Mikaili LillyMikaili Lilly
Mikaili Lilly
 
Big Data World presentation - Sep. 2014
Big Data World presentation - Sep. 2014Big Data World presentation - Sep. 2014
Big Data World presentation - Sep. 2014
 
How to monetize and generate revenues from data services in a competitive market
How to monetize and generate revenues from data services in a competitive marketHow to monetize and generate revenues from data services in a competitive market
How to monetize and generate revenues from data services in a competitive market
 
Guide To Segmentation 1231969935172574 1
Guide To Segmentation 1231969935172574 1Guide To Segmentation 1231969935172574 1
Guide To Segmentation 1231969935172574 1
 
Are your digital channels driving growth?
Are your digital channels driving growth?Are your digital channels driving growth?
Are your digital channels driving growth?
 
2014 Customer Loyalty ASEAN Conference: Loyalty Prime
2014 Customer Loyalty ASEAN Conference: Loyalty Prime2014 Customer Loyalty ASEAN Conference: Loyalty Prime
2014 Customer Loyalty ASEAN Conference: Loyalty Prime
 
W.UP Sales.UP digital sales and engagement tool for banks
W.UP Sales.UP digital sales and engagement tool for banksW.UP Sales.UP digital sales and engagement tool for banks
W.UP Sales.UP digital sales and engagement tool for banks
 
Presentation
PresentationPresentation
Presentation
 
Banalytics - Monetizing corporate big data | Instarea
Banalytics - Monetizing corporate big data | InstareaBanalytics - Monetizing corporate big data | Instarea
Banalytics - Monetizing corporate big data | Instarea
 
Delivering Smarter Customer Interactions
Delivering Smarter Customer Interactions Delivering Smarter Customer Interactions
Delivering Smarter Customer Interactions
 
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”
“Who's Calling? Customizing the Caller Experience to Feed the Bottom Line”
 
The Age Of New Reality Marketing V5.1 Final
The Age Of New Reality Marketing V5.1 FinalThe Age Of New Reality Marketing V5.1 Final
The Age Of New Reality Marketing V5.1 Final
 
IBM Transforming Customer Relationships Through Predictive Analytics
IBM Transforming Customer Relationships Through Predictive AnalyticsIBM Transforming Customer Relationships Through Predictive Analytics
IBM Transforming Customer Relationships Through Predictive Analytics
 
Data science vs real world: friends or foes - Pavle Kecman
Data science vs real world: friends or foes - Pavle KecmanData science vs real world: friends or foes - Pavle Kecman
Data science vs real world: friends or foes - Pavle Kecman
 
Data Science Use cases in Banking
Data Science Use cases in BankingData Science Use cases in Banking
Data Science Use cases in Banking
 
Aegon Americas: Leveraging leading positions in workplace and individual solu...
Aegon Americas: Leveraging leading positions in workplace and individual solu...Aegon Americas: Leveraging leading positions in workplace and individual solu...
Aegon Americas: Leveraging leading positions in workplace and individual solu...
 
Equitec consumer dynamics mba case study
Equitec consumer dynamics mba case studyEquitec consumer dynamics mba case study
Equitec consumer dynamics mba case study
 
Multichannel Retention Strategies: A Steady Diet of Low Hanging Fruit
Multichannel Retention Strategies: A Steady Diet of Low Hanging FruitMultichannel Retention Strategies: A Steady Diet of Low Hanging Fruit
Multichannel Retention Strategies: A Steady Diet of Low Hanging Fruit
 
Innovations in marketing effectiveness measurement
Innovations in marketing effectiveness measurement  Innovations in marketing effectiveness measurement
Innovations in marketing effectiveness measurement
 

More from McKinsey on Marketing & Sales

McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey on Marketing & Sales
 
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisis
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisisMcKinsey Survey: Japanese consumer sentiment during the coronavirus crisis
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisisMcKinsey on Marketing & Sales
 
McKinsey Survey: Australian consumer sentiment during the coronavirus crisis
McKinsey Survey: Australian consumer sentiment during the coronavirus crisisMcKinsey Survey: Australian consumer sentiment during the coronavirus crisis
McKinsey Survey: Australian consumer sentiment during the coronavirus crisisMcKinsey on Marketing & Sales
 
McKinsey Survey: Indian consumer sentiment during the coronavirus crisis
McKinsey Survey: Indian consumer sentiment during the coronavirus crisisMcKinsey Survey: Indian consumer sentiment during the coronavirus crisis
McKinsey Survey: Indian consumer sentiment during the coronavirus crisisMcKinsey on Marketing & Sales
 
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisis
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisisMcKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisis
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisisMcKinsey on Marketing & Sales
 
McKinsey Survey: Korean consumer sentiment during the coronavirus crisis
McKinsey Survey: Korean consumer sentiment during the coronavirus crisisMcKinsey Survey: Korean consumer sentiment during the coronavirus crisis
McKinsey Survey: Korean consumer sentiment during the coronavirus crisisMcKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events in Europe are...
McKinsey European consumer sentiment survey: How current events in Europe are...McKinsey European consumer sentiment survey: How current events in Europe are...
McKinsey European consumer sentiment survey: How current events in Europe are...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey on Marketing & Sales
 
McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey on Marketing & Sales
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey on Marketing & Sales
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey on Marketing & Sales
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey on Marketing & Sales
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey on Marketing & Sales
 

More from McKinsey on Marketing & Sales (20)

McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...
 
McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...
 
McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...
 
McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...
 
McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...
 
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisis
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisisMcKinsey Survey: Japanese consumer sentiment during the coronavirus crisis
McKinsey Survey: Japanese consumer sentiment during the coronavirus crisis
 
McKinsey Survey: Australian consumer sentiment during the coronavirus crisis
McKinsey Survey: Australian consumer sentiment during the coronavirus crisisMcKinsey Survey: Australian consumer sentiment during the coronavirus crisis
McKinsey Survey: Australian consumer sentiment during the coronavirus crisis
 
McKinsey Survey: Indian consumer sentiment during the coronavirus crisis
McKinsey Survey: Indian consumer sentiment during the coronavirus crisisMcKinsey Survey: Indian consumer sentiment during the coronavirus crisis
McKinsey Survey: Indian consumer sentiment during the coronavirus crisis
 
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisis
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisisMcKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisis
McKinsey: Survey: Indonesian consumer sentiment during the coronavirus crisis
 
McKinsey Survey: Korean consumer sentiment during the coronavirus crisis
McKinsey Survey: Korean consumer sentiment during the coronavirus crisisMcKinsey Survey: Korean consumer sentiment during the coronavirus crisis
McKinsey Survey: Korean consumer sentiment during the coronavirus crisis
 
McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...McKinsey European consumer sentiment survey: How current events are shaping F...
McKinsey European consumer sentiment survey: How current events are shaping F...
 
McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...McKinsey European consumer sentiment survey: How current events are shaping U...
McKinsey European consumer sentiment survey: How current events are shaping U...
 
McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...McKinsey European consumer sentiment survey: How current events are shaping G...
McKinsey European consumer sentiment survey: How current events are shaping G...
 
McKinsey European consumer sentiment survey: How current events in Europe are...
McKinsey European consumer sentiment survey: How current events in Europe are...McKinsey European consumer sentiment survey: How current events in Europe are...
McKinsey European consumer sentiment survey: How current events in Europe are...
 
McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...McKinsey European consumer sentiment survey: How current events are shaping S...
McKinsey European consumer sentiment survey: How current events are shaping S...
 
McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...McKinsey European consumer sentiment survey: How current events are shaping I...
McKinsey European consumer sentiment survey: How current events are shaping I...
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...
 
McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...McKinsey survey: European consumer sentiment survey: How current events are s...
McKinsey survey: European consumer sentiment survey: How current events are s...
 

Recently uploaded

Meet Raj Shamani: A Trailblazing Entrepreneur
Meet Raj Shamani: A Trailblazing EntrepreneurMeet Raj Shamani: A Trailblazing Entrepreneur
Meet Raj Shamani: A Trailblazing Entrepreneurramya202104
 
Young Woman Entrepreneur - Kaviya Cherian
Young Woman Entrepreneur - Kaviya CherianYoung Woman Entrepreneur - Kaviya Cherian
Young Woman Entrepreneur - Kaviya CherianCDEEPANVITA
 
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptx
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptxStreamlining Your Accounting A Guide to QuickBooks Migration Tools.pptx
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptxPaulBryant58
 
How The Hustle Milestone Referral Program Got 300K Subscribers
How The Hustle Milestone Referral Program Got 300K SubscribersHow The Hustle Milestone Referral Program Got 300K Subscribers
How The Hustle Milestone Referral Program Got 300K SubscribersFlyyx Tech
 
A Case Study On SQUARE GROUP Bangladesh.pdf
A Case Study On SQUARE GROUP Bangladesh.pdfA Case Study On SQUARE GROUP Bangladesh.pdf
A Case Study On SQUARE GROUP Bangladesh.pdfmeftaul987
 
Dashboards y paneles - CP Home - Area de Operaciones
Dashboards y paneles - CP Home - Area de OperacionesDashboards y paneles - CP Home - Area de Operaciones
Dashboards y paneles - CP Home - Area de OperacionesLPI ONG
 
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...Reframing Requirements: A Strategic Approach to Requirement Definition, with ...
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...Jake Truemper
 
Olympus 38DL Plus Ultrasonic Thickness Gauge
Olympus 38DL Plus Ultrasonic Thickness GaugeOlympus 38DL Plus Ultrasonic Thickness Gauge
Olympus 38DL Plus Ultrasonic Thickness GaugeStephenKim86
 
AirOxi - Pioneering Aquaculture Advancements Through NFDB Empanelment.pptx
AirOxi -  Pioneering Aquaculture Advancements Through NFDB Empanelment.pptxAirOxi -  Pioneering Aquaculture Advancements Through NFDB Empanelment.pptx
AirOxi - Pioneering Aquaculture Advancements Through NFDB Empanelment.pptxAirOxi Tube
 
L-1 VISA Business (Plan Sample) - Plan Writers
L-1 VISA Business (Plan Sample) - Plan WritersL-1 VISA Business (Plan Sample) - Plan Writers
L-1 VISA Business (Plan Sample) - Plan WritersPlan Writers
 
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024The Vietnam Believer_Newsletter_Vol.001_Mar12 2024
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024believeminhh
 
Mist Cooling & Fogging System Company in Egypt
Mist Cooling & Fogging System Company in EgyptMist Cooling & Fogging System Company in Egypt
Mist Cooling & Fogging System Company in Egyptopstechsanjanasingh
 
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdf
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdfCORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdf
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdfLouis Malaybalay
 
PHX Corporate Presentation March 2024 Final
PHX Corporate Presentation March 2024 FinalPHX Corporate Presentation March 2024 Final
PHX Corporate Presentation March 2024 FinalPanhandleOilandGas
 
0311 National Accounts Online Giving Trends.pdf
0311 National Accounts Online Giving Trends.pdf0311 National Accounts Online Giving Trends.pdf
0311 National Accounts Online Giving Trends.pdfBloomerang
 
Benihana of Tokyo case study11111111.pdf
Benihana of Tokyo case study11111111.pdfBenihana of Tokyo case study11111111.pdf
Benihana of Tokyo case study11111111.pdfjavenxxx01
 
Business Models and Business Model Innovation
Business Models and Business Model InnovationBusiness Models and Business Model Innovation
Business Models and Business Model InnovationMichal Hron
 
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...BilalAhmed717
 
Pitch Deck Teardown: SuperScale's $5.4M Series A deck
Pitch Deck Teardown: SuperScale's $5.4M Series A deckPitch Deck Teardown: SuperScale's $5.4M Series A deck
Pitch Deck Teardown: SuperScale's $5.4M Series A deckHajeJanKamps
 

Recently uploaded (20)

Meet Raj Shamani: A Trailblazing Entrepreneur
Meet Raj Shamani: A Trailblazing EntrepreneurMeet Raj Shamani: A Trailblazing Entrepreneur
Meet Raj Shamani: A Trailblazing Entrepreneur
 
Young Woman Entrepreneur - Kaviya Cherian
Young Woman Entrepreneur - Kaviya CherianYoung Woman Entrepreneur - Kaviya Cherian
Young Woman Entrepreneur - Kaviya Cherian
 
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptx
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptxStreamlining Your Accounting A Guide to QuickBooks Migration Tools.pptx
Streamlining Your Accounting A Guide to QuickBooks Migration Tools.pptx
 
How The Hustle Milestone Referral Program Got 300K Subscribers
How The Hustle Milestone Referral Program Got 300K SubscribersHow The Hustle Milestone Referral Program Got 300K Subscribers
How The Hustle Milestone Referral Program Got 300K Subscribers
 
A Case Study On SQUARE GROUP Bangladesh.pdf
A Case Study On SQUARE GROUP Bangladesh.pdfA Case Study On SQUARE GROUP Bangladesh.pdf
A Case Study On SQUARE GROUP Bangladesh.pdf
 
WAM Corporate Presentation Mar 12 2024_Video.pdf
WAM Corporate Presentation Mar 12 2024_Video.pdfWAM Corporate Presentation Mar 12 2024_Video.pdf
WAM Corporate Presentation Mar 12 2024_Video.pdf
 
Dashboards y paneles - CP Home - Area de Operaciones
Dashboards y paneles - CP Home - Area de OperacionesDashboards y paneles - CP Home - Area de Operaciones
Dashboards y paneles - CP Home - Area de Operaciones
 
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...Reframing Requirements: A Strategic Approach to Requirement Definition, with ...
Reframing Requirements: A Strategic Approach to Requirement Definition, with ...
 
Olympus 38DL Plus Ultrasonic Thickness Gauge
Olympus 38DL Plus Ultrasonic Thickness GaugeOlympus 38DL Plus Ultrasonic Thickness Gauge
Olympus 38DL Plus Ultrasonic Thickness Gauge
 
AirOxi - Pioneering Aquaculture Advancements Through NFDB Empanelment.pptx
AirOxi -  Pioneering Aquaculture Advancements Through NFDB Empanelment.pptxAirOxi -  Pioneering Aquaculture Advancements Through NFDB Empanelment.pptx
AirOxi - Pioneering Aquaculture Advancements Through NFDB Empanelment.pptx
 
L-1 VISA Business (Plan Sample) - Plan Writers
L-1 VISA Business (Plan Sample) - Plan WritersL-1 VISA Business (Plan Sample) - Plan Writers
L-1 VISA Business (Plan Sample) - Plan Writers
 
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024The Vietnam Believer_Newsletter_Vol.001_Mar12 2024
The Vietnam Believer_Newsletter_Vol.001_Mar12 2024
 
Mist Cooling & Fogging System Company in Egypt
Mist Cooling & Fogging System Company in EgyptMist Cooling & Fogging System Company in Egypt
Mist Cooling & Fogging System Company in Egypt
 
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdf
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdfCORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdf
CORPORATE SOCIAL RESPONSIBILITY - FINAL REQUIREMENT.pdf
 
PHX Corporate Presentation March 2024 Final
PHX Corporate Presentation March 2024 FinalPHX Corporate Presentation March 2024 Final
PHX Corporate Presentation March 2024 Final
 
0311 National Accounts Online Giving Trends.pdf
0311 National Accounts Online Giving Trends.pdf0311 National Accounts Online Giving Trends.pdf
0311 National Accounts Online Giving Trends.pdf
 
Benihana of Tokyo case study11111111.pdf
Benihana of Tokyo case study11111111.pdfBenihana of Tokyo case study11111111.pdf
Benihana of Tokyo case study11111111.pdf
 
Business Models and Business Model Innovation
Business Models and Business Model InnovationBusiness Models and Business Model Innovation
Business Models and Business Model Innovation
 
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...
Project Work on Consumer Behavior in Fast Food Restaurants. Their behavior to...
 
Pitch Deck Teardown: SuperScale's $5.4M Series A deck
Pitch Deck Teardown: SuperScale's $5.4M Series A deckPitch Deck Teardown: SuperScale's $5.4M Series A deck
Pitch Deck Teardown: SuperScale's $5.4M Series A deck
 

Big Data and Advanced Analytics

  • 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: