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Big Data Analytics
Shaping the business of the future
• Big Data Analytics
• Accenture Point of View
Agenda
Copyright © 2013 Accenture. All rights reserved. 2
• Big Data Analytics
• Accenture Point of View
Agenda
Copyright © 2013 Accenture. All rights reserved. 3
What is Big Data?
Copyright © 2013 Accenture. All rights reserved. 4
• Unemployment • Interest Rate • Consumer Confidence Index • Inflation Rate • Income • Consumer Price Index
Inte
rnal
Traditional Enterprise Data
Exte
rnal
Machine Generated / Sensor Data
Social Media Data Macroeconomic / Public Data
• Customer information from CRM systems • Transactional ERP data • Web store transactions • General ledger data
• Call Detail Records • Weblogs • Smart meters • Manufacturing sensors • Equipment logs • Trading Systems data
• Customer feedback streams • Micro-blogging sites • Social Media platforms
Big data is defined by four key characteristics:
A smoothly integrated collection of diverse data-sources:
Volume Velocity Variety Value
Industry analysis shows that there is huge value to
be gained from Big Data Analytics
US Health Care
$300 billion value per year
~0.7% annual productivity
growth
Europe Public Sector
€250 billion value per year
~0.5% annual productivity
growth
Personal Location Data
€100 billion revenue for SPs
€700 billion for end users
US Retail
60+% increase in net
margin available
Manufacturing
50% decrease in prod dev and
assembly
7% reduction in working cap.
Copyright © 2013 Accenture. All rights reserved.
Dat
a V
elo
city
, Var
iety
, Vo
lum
e &
Co
mp
lexi
ty
Time
Reactive Business Intelligence
Predictive Business Intelligence
Current Generation of Big Data
Next Generation of Big Data
1
2
3
4 • Traditional structured
data (e.g. CDRs, CRM, transactions)
• Data age: Weeks to months
• Descriptive analytics (e.g. segmentations)
• Static reports
• Augmented structured data (e.g. CDRs, CRM, transactions, lifestyle, lifestage)
• Data age: Days to weeks
• Predictive analytics (e.g. forecasting, churn propensity)
• Dynamic reports
• Structured & unstructured data (e.g. location, social interaction)
• Data age: Days to weeks
• Big data analytics (e.g. social network analysis, social media analytics)
• Structured & unstructured data (e.g. clickstream data, multi-device usage data, mash-ups from multiple industries)
• Real-time big data analytics (e.g. real-time voice-to-text mining) • Next Generation underpinned by Technology innovations
Technology advances underpin how enormous data
volumes can now be processed in real-time
Copyright © 2013 Accenture. All rights reserved.
What is Big Data Analytics?
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Big Data Analytics is:
• a shift in the mindset of how we think about analytics as an internal component to the organization.
• a way to foster a culture around our organization that focuses on integrating data from a diverse variety of sources in such a way that drives meaningful insights in a rapid fashion.
• Hence, it leads to enhanced productivity, stronger competitive position and greater innovation.
… The way that these benefits are ensured is via simultaneously addressing:
- Enhanced productivity
- Stronger competitive position
- Greater innovation
Mana-gement Comple
xity
Missed Opportunities
Latency requirements
Missed Opportunities:
Lack of sufficient computing power
often prevent traditional analytics
tools from analyzing all data that is
available.
Latency requirements:
Lack of an effective computing
model may hinder traditional
analytics tools from taking into
account data this is dynamically
updated from a multitude of different
data-sources.
Management complexity:
Lack of a successful integration
between different organizational
departments’ data-sources typically
impedes traditional analytics tools to
infer meaningful inter-departmental
insights.
Gradual Progression to Big Data Analytics
Copyright © 2013 Accenture. All rights reserved. 8
• Transactions
• Call detail records
• Location
• Click-streams
• Social data
• Channel interaction
• Device usage
• Structured
• Unstructured
• Real-time
• Immense
• Real-time predictive
• Transactions
• Call detail records
• Structured
• Days to weeks
• Low to medium
• Predictive
• Transactions
• Call detail records
• Structured
• Weeks to months
• Low
• Descriptive
Last Generation Analytics
Current Generation Analytics
Next- Generation Analytics/ Big Data Analytics
Data format
Data type
Data volume
Data age
Analytics
Data +
Technology
People +
Culture
Process +
Organization Next–Gen Customer Profile
Agile Segmentation with Predictive Analytics
Social Media Analytics
Channel Usage Analytics
Social Network Analytics
Location Based Analytics
Real Time Decision Analytics
Next-Gen Analytics Capability
Accenture
Proposition
Next-Gen
Customer
Service
Next-Gen
Business
Innovation
Next-Gen
Business
Service
Typical Big Data Analytic Architecture
Copyright © 2013 Accenture. All rights reserved. 9
Aggregations
Pattern Mining
Transformations
Big Data Analytics
New
Features Feature
Repository
Ads
Offers
Recommendations
Propensity
Big Data
Structured Silo #1
Structured Silo #2
Structured Silo #3
Unstructured Data
• Big Data Analytics
• Accenture Point of View
Agenda
Copyright © 2013 Accenture. All rights reserved. 10
Analysis shows that the industries that would most benefit from Big Data
analytics are Retail, Media, Financial Services and Healthcare
Value Potential of Big Data and Ease of Value Capture, Europe, 2011
Scale of
Data
Usage
High
Low
Value Potential Generated by Big Data Low High
Accommodation
& Food
Manufacturing Utilities
Health Care
Mining
Comms,
Media &
Information
Finance & Insurance
Transport & Storage
Real Estate
Government /
Public Services
Retail (Trade / Distribution)
Construction
Scientific & Tech
activity
Size of bubble represents potential economic value added by industry sector
Source: Accenture analysis based on IDC, Eurostat, Gartner and McKinsey estimates
Industry Prioritisation
1. Retail & FMCG
2. Advertising/Comms/ Media
3. Financial Service
4. Government & Transport
5. Healthcare
Copyright © 2013 Accenture. All rights reserved.
Building products for these industries based on geo-location use cases,
gives significant potential market revenues
• Real-time footfall analysis products can
be created from mobile network events
(e.g. connecting to a cell tower, voice,
texts), Wi-Fi data, GPS coordinates and
other forms of geo-location big data.
• Such products allow accurate and
frequent tracking of population
movements, especially with regards to
footfalls in retail catchment areas
• Retailers can use this information to
devise targeted marketing campaigns
and optimise store locations
Retail – Footfall and Segmentation
• Real-time footfall analysis products can
be created from mobile network events
(e.g. connecting to a cell tower, voice,
texts), Wi-Fi data, GPS coordinates and
other forms of geo-location big data
• This can be used for the advertising
industry to more accurately calculate
marketing ROI for the out-of-home media
channel (e.g. billboards, transport
signage)
Media – Outdoor Media Planning
• Banks are also partnering with mobile
network operators to improve their real-
time fraud detection and credit scoring
• Fraud detection algorithms can be
augmented by the location of a
customer’s mobile phone when making
online transactions
Financial Services - Fraud
• Smart Cities topic utilises multiple forms
of Big Data (e.g. M2M, street light
sensors, telematics sensors) to aid
governments in building a real-time
dynamic view of an urban population
• Big Data from telematics devices can
also enable real-time optimisation of
traffic flows in a city
Smart Cities / Traffic Optimisation
Copyright © 2013 Accenture. All rights reserved.
Mobile Usage Tracking
Offer Definition
Provide aggregated mobile Internet,
App, Device and Ad usage metrics
based on actual consumer usage on
the Telco network
Target Customers
Outdoor Media Platform
Offer Definition
Provide direct outdoor audience
measurement and expand into digital
signage infrastructure
Target Customers
The three “beachhead” products for Marketing and Media Sector
CPS / Sales Conversion
Offer Definition
Generate customers for advertisers
by providing “customer value” insight
and running multi-channel
campaigns
Target Customers
Outdoor Publishers
Event Venue / Properties
Outdoor & Event Marketers
Outdoor & Event Marketing Agencies
Mobile Device OEMs
Digital Publishers
Digital Advertising Agencies
Digital Marketers Digital Marketers
Copyright © 2013 Accenture. All rights reserved.
Big Data Applications and Solutions
14
1.Predictive Modeling
2.Data Visualization
3.Cluster Partitioning
4.Outlier Analysis
5.AB Testing
6.Markov Chains
1.Modeling true risk
2.Customer churn analysis
3.Recommendation engine
4.Ad targeting
5.PoS transaction analysis
6.Failure Prediction
7.Threat analysis
8.Trade promotion effectiveness
Indicative Applications Solution Patterns
Copyright © 2013 Accenture. All rights reserved.
Big Data Solution Overview
15
SENTIMENT ANALYSIS, TEXT MINING and PREDICTIVE ANALYTICS
PERSONALISED OFFERINGS
Transactional
Machine-generated / Sensor-data
Social Media
TERADATA
Macroeconomic
Social Media & External Data Internal Customer Data
BIG DATA
DATA INTEGRATION
ETL ETL
Decision Tree Customer
Micro-Segments
1. Acquire
2. Organize
3. Analyze
4. Evaluate
Web Mobile Internal dashboards and analytics
Cosine Similarity Method
Neural Networks
Predictive Analytics
Copyright © 2013 Accenture. All rights reserved.
Big Data requires separate and scalable technology infrastructure (e.g. Hadoop, EMR) to traditional BI to cope with scale, velocity and variations
Da
ta In
ge
stio
n
Data
Inte
gra
tion
(RE
ST
)
HBase
Hive
HDFS
CPU
Disk
Node
Rack
CPU
Disk
Node
Rack
CPU
Disk
Node
Rack
Compute/Storage
BI Analysis Advanced Analytics
Hadoop Ecosystem
NoSQL
Customer-Facing
Apps
Real-time, In-Memory
Analytics
MPP Agile
Data Marts
Map/Reduce
Illustrative Conceptual
Solution Architecture
Copyright © 2013 Accenture. All rights reserved.
Selection of appropriate predictive analytics techniques
Copyright © 2013 Accenture. All rights reserved. 17
Cosine Similarity Method
• Model customer behaviour using linear algebra
• For example: customers can be modelled as vectors, merchants as vector components, spending amounts as component magnitude
Artificial Neural Networks
• Neural networks are powerful machine learning algorithms that use complex, nonlinear mapping functions for estimation and classification.
• Models with more complex topologies may also include intermediate, hidden layers and neurons
Decision Trees
• Decision Trees are perhaps the most popular classification technique
• Through successive partitions of the initial population, their goal is to produce ‘‘pure’’ sub-segments, with homogeneous behavior in terms of the output
Customer Segmentation
• Multi-dimensional grouping of customers based on needs, behavior and value dimensions, according to pre-defined business objectives
• Segment profiling as input to customer strategy around value propositions, products, services , channels & experience
18
Accenture Point of View
Copyright © 2012 Accenture. All rights reserved.
360-degree view of customers
Benefits Client Examples Details
1. Improved agility to respond
to competitive threats
• European mobile operator
• South-East Asian operator
Drawing and combining data from real time
feeds and traditional historical data enables
generation of real time insights, inference of
“hot spots” (i.e. current interests, new location
patterns, reactions to competitor promotions),
presentation of the right product at the right
time to each customer.
2. Richer insights from social
media
• US-based network operator
Extracting and combining social characteristics
with existing mobile behavioral knowledge, Big
Data Analytics create deeper insights to engage
and retain existing customers.
3. Differentiated user
experience • US wireless operator
Integration of data coming from IPTV set-top
boxes and voice and data usage combined with
application of basket analysis generate:
• a channel affinity map that links channels
most likely to be viewed together,
• recommendation of appealing bundles
based on viewing patterns.
19
Accenture Point of View
Copyright © 2013 Accenture. All rights reserved.
360-degree view of customers
Benefits Client Examples Details
4. Identification of leaders and
followers • US – based wireless operator
Combination of social media information can
help to follow actions of super-influencers
during all stages of any product’s lifetime. This
functionality:
• adds value during early product adoption
and service take-up stages,
• promptly identifies all signs of contagious
churn,
• provides recommendations for customized
targeting (i.e. pricing, bundle of offers of
interest) to super-influencers.
5. Smarter business decisions • Spanish mobile operator Integration of call detail records, location data
and usage patterns enables location-based real
time marketing offers.
6. Tailor-made real time
recommendations for each
customer interaction
• UK mobile operator
Application of predictive analysis and historical
customer engagement rules on real time
customer interaction information enables a
client to offer appropriate real time
recommendations to each interacting customer.
Business Challenge
Client sought partner to:
• analyze customer patterns of behavior, • provide recommendations to customers for best actions that have
proved of help to similar customers, • provide recommendations to customers based on their upcoming key
life events, Project scoped to span from data collection and integration to modeling and
insight generation.
Integrated a wide variety of internal and external data sources in an
efficient way.
Developed sophisticated statistical modeling techniques to identify
groups of customers with similar behaviors.
Implemented cutting edge algorithms to infer insights for customers’
best courses of action.
Developed innovative approaches to provide suggestions to customers,
depending on their corresponding key life events.
Increased Sales to existing customers: Proactively contact customers based
on behavioural triggers and key life stages.
Increased retention of existing customers: Proactively target customers with
high risk of churn with specific high value services.
Increased acquisition of new customers: Provide Personalised pricing and
use social data indicators during interactions.
Accenture Contribution
Key Benefits
Illustrative Outputs
Correlation and Prediction
Analysis and Decision Making
Recommendations and Insights
20
A case study from a major financial institution
Decision Tree
Customer Micro-
Segments
Cosine Similarity Method
Neural Networks
Copyright © 2013 Accenture. All rights reserved.
21 Copyright © 2013 Accenture. All rights reserved.
Improving the performance on a US online media content
provider
Business Challenge • The client is one of the leading online video streaming companies
in the US
• The available titles are currently over 8000 and are offered either
in SD or in HD format.
• The client offers titles from movie companies such as EPIX,
Lionsgate, NBCUniversal, Paramount Pictures, Relativity and
Sony Pictures
• The client was to become the leader of the market and obtain a
market share greater than that of Netflix
• The recommendations are currently static and not generated
though an automated process
• The client wanted to evaluate the increase in the market share
and bottom line an automated intelligent recommender system
can bring in.
How Accenture Helped • Accenture engaged in a PoC with the client to assess the sales
lift that ARE can achieve.
• Accenture used multiple data sources spanning different
dimensions of the customer DNA and applied user-based
collaborative filtering techniques to identify the best possible
user-movie matches
• The solution is hosted on the cloud and it operates in real time
offering targeted recommendations to the subscribers of the
customer
Outcomes • The recommendations of ARE were compared to those of the
client using Accenture’s offline validation approach and it was
found that they improve performance by 10% in terms of
recall and precision.
• Currently a live validation is performed via the email channel:
ARE’s recommendations are offered to clients and their
responses are recorded in order to assess the effect on the
performance. Most recent results present an increase of 10-
15% in views (rentals, purchases and subscriptions)
22 Copyright © 2013 Accenture. All rights reserved.
Helping the Leading Chinese online retailer improve their
recommender system
Business Challenge • leading B2C ecommerce portal (35% market share) in China.
The Client has expanded beyond their core IT and consumer
electronics products, into general merchandize and books, and
has also penetrated the C2B2C channel via their public open
platform. Its leading position has attracted investors, and they are
now planning a US IPO. Its aim is to be the Amazon.com of
China. Its annual revenue was approx. 6BN USD in 2011.
• The client is now exploring alternative growth areas, beyond
category expansion. As a key strategic thrust in 2012, they seek
to grow revenues via online product recommendations and web
page optimization. The current recommender system contribution
to sales is below industry benchmarks.
• Client is growing rapidly: Number of transactions double every 5
months and
• Long tail: 80% of their products account for only 5% of their sales
How Accenture Helped • Accenture is helping the client improve its recommendation
capability, with a focus on driving transactions via better use of
data and improved algorithms, leveraging Big Data analytics and
web page optimization.
• Accenture used a global team consisting of experts in machine
learning and big data from Adelaide, Athens, Beijing and San
Francisco to deliver results for Implementing our
recommendation engine algorithms (ARE)
Outcomes • Accenture developed approximately 40 machine learning
algorithm variants to give us the results.
• In online testing ARE outperformed the internal
recommendation engine of the client by up to an estimated
30%
• This is translated to revenue uptick for the client, which is
estimated to be initially up to ~$100M USD per year (we
expect this to increase over time).
• We have also applied out robust repeatable agile testing
platform to rapidly prototype algorithm performance. The test
platform logic is derived from the field of information retrieval
(IR) where we calibrated algorithm performance according to
their impact on precision and recall.
23 Copyright © 2012 Accenture. All rights reserved.
Leading US Discount Coupons Company
Business Challenge • The client is a leading coupons company in the US offering
discount coupons to consumers. The discount coupons are
issued by manufacturers who want to use this channel to
increase their sales.
• The customer is one of the leading players in the coupons
industry and they want to increase their market share by applying
intelligent, real time automated targeted methodologies
• The client is affiliated with many grocery stores chains all over
the country
• The challenge is to combine heterogeneous data from many
different sources and to generate insights and provide
personalised coupon recommendations to customers using
various means: web site, digital receipt, emails, etc.
How Accenture Helped • Developed an in-house big data platform that collects
transactional data from affiliated grocery stores , processes them
and generates automated personalised recommendations using
ARE
• Accenture used a global team consisting of experts in machine
learning and big data from Adelaide, Athens, Chicago and San
Francisco to deliver results for Implementing our
recommendation engine algorithms (ARE)
• ARE is estimated to process 2-3 TB on a daily basis in order to
deliver updated, real-time recommendations
Outcomes • ARE has been empowered with new algorithms that cater for
the peculiarities of the coupons industry. ARE’s algorithms
recommend not only coupons to customers, but also products
for which new coupons should be issued
• The site has been re-organised and coupons are presented
to customers based on the corresponding customer to
coupon estimated interest
• During the first months of operation the coupon activation
rate has increased up to 20%, depending on the product
category
• Identification of most desired/recommended products
contributes in the design of more targeted coupons.
Copyright © 2013 Accenture. All rights reserved. 24
Thank you!