The document discusses how several large companies like Amazon, Mastercard, Walmart, and Caesars are successfully using big data analytics. It provides examples of how these companies analyze purchasing patterns, social media posts, and other customer data to improve recommendations, inventory management, and personalized customer experiences. The document also outlines how big data can be applied across various industries like retail, healthcare, telecommunications, and more.

1. Big Data Done Right by
Successful Organizations

2. Amazon
Amazon has obtained a patent to ship us goods before we have even made a
decision to buy them, purely based on their predictive big data analytics
• the first retailer that used extensively algorithms to provide recommendations to customers
• Anticipatory Shipping – some retailers already use predictive analytics to ensure the right items are
in stock, based on past buying patterns, social media analytics and weather predictions. Amazon is
taking it to a personal level, predicting items each user might buy using item-to-item collaborative
filtering on many data points (eg: what users have bought before, what they have in their virtual
shopping card or wish list, the items they have rated and reviewed, as well as what other similar
users have bought)
• ‘One Click Buy’ feature
• Predictive big data analytics

3. Mastercard
They sell data to retailers, banks and governments on spending
patterns found in the payments it processes
MasterCard handles payments for 2 billion cardholders and tens of millions of
merchants. It uses that information to generate real-time data on consumer trends,
available more quickly that regular government statistics. "Retailers are fantastic at
using the data they have available about how people shop in their store, how their
inventory turns over, but what they don't know is what happens outside their store," she
said. "The data we've got is ubiquitous across the whole market. We can help retailers
see what they need to do to capture more sales.“ Ann Cairns, Head of MasterCard's
business outside North America in Reuters interview

4. Walmart
“Our ability to pull data together is unmatched”
Walmart CEO Bill Simon
• Walmart’s attempt to use data to predict customer behavior date back to at least 2004
• WalmartLabs (2011)
• The Social Genome project - aims to increase the efficiency of advertising on social
networks by guessing what products people are likely to want to buy, based on their
conversations with friends.
• Shoppycat service suggests gifts that people might like to buy for their friends, based
on their interests and Likes, and they also experiment with crowd-sourcing new
products with Get On The Shelf.
• Polaris - their own search engine - uses sophisticated semantic analysis to work out
what a customer wants based on their search terms.
• Smart lightbulbs containing Apple’s iBeacon technology to prepare to monitor
shoppers in their stores
• Walmart made a move from the experiential 10 node Hadoop cluster to a 250 node
Hadoop cluster. They combined 10 different websites into a single one. Their analysis
now covers millions of products and 100’s of millions customers from different sources.
The analytics systems at Walmart analyse close to 100 million keywords on daily basis
to optimize the bidding of each keyword.

5. eHarmony Dating Site
Personalized matches and search results for millions of subscribers to
improve the chances of relationship success

6. Cerner Healthcare Company
Detect potentially fatal infections
Cerner has built an enterprise data hub to create a more comprehensive view of any
patient, condition or trends for over 1 million patients. Among other things, it is
helping determine the likelihood that a patient has the potentially fatal bloodstream
infection with a much greater accuracy than what was previously possible.

7. Nippon Paint
Domain: One of the largest paint manufacturers in the world
Nippon Paint uses SAP HANA to understand consumer behaviors,
optimize its supply chain and improve its marketing campaigns.
They can quickly transform massive data from iColor website into valuable market insight for
business actions. They built an analytics platform to capture consumer behaviors (eg: preferences on
colors, design styles and designers, by demographic segments and geographic regions). They can
quickly identify popular designers and decoration companies, allowing sales and marketing team to
proactively build connections with them and to promote Nippon paint products. They can identify,
on regional level, popular 3rd party portals which lead web visits to Nippon’s iColor website enabling
them to place advertisements and push banners at the right portal to achieve maximum exposure.
The first hand information on color trends and customer requirements on product features helps the
R & D team to develop quality products to response to the market unique demands.

8. Caesars – Luxurious Hotels and Casinos
“Big Data is even more important than a gaming license”
Joshua Kanter, VP of Total Rewards
Caesar collects massive amounts of data and uses it to cultivate customer loyalty and surprise guests
with gifts after, for example, a bad day at the casino. Their data-driven reward program has more
than 45 million members, which are tracked from the moment they book until the moment they
leave the hotel or casino. Due to the data-drive strategy, Caesars has been able to trace 58 percent of
all costs spent to the customers in the company in 2004 to 85 percent today. Caesar tries to
determine each guest’s profile. Cameras record action in the casinos and the choices the gamblers
make while playing and combines this data with booking data, travel arrangements, dining, gaming
and enjoying other activities at the company’s properties. All this information is stored, analyzed and
used to increase customer satisfaction.

9. Big Data Applied in Retail/eCommerce
• Anticipate demand based on customer’s online activity, their
geographical location, period of the year, weather, etc
• Recommendations, advertisements or real time offers based on
advanced customer segmentation and shopping patterns
• Smarter shopping experience and smarter merchandising
• Predictive analytics that enable you to optimize pricing, inventory
levels, check your competition pricing, improve your customer
service, increase customer satisfaction and your margins

10. Big Data Applied in Digital Publishing
• Use social media behavior patterns as “production line”
• Relevant content delivered to the appropriate customer segment at
the right time
• Personalized content delivered in real time
• Measure the effectiveness of your digital strategies and determine
the most effective methods to deliver content to your targets
• Transform big data into actionable insights

11. Big Data Applied in Telecom
• Products and services tailored based on customer behavior
• Network infrastructure optimization
• ARPU and up sell maximization by offering the right products based
on customer behavior, context and service availability
• Reaction time improvements in case of incidents
• Detailed insights and maps showing the distribution and dynamics
of revenue and churn versus the network type, quality or coverage
• Upsell 4G
• Telecom product recommendation
• Users Location Profiling
• Network Quality vs. Revenue and
Churn

12. Big Data Applied in Healthcare
• Real-time analysis based on web search and social media or
messaging contexts
• Improved efficiency by integrating health care data and using them
in the most optimum way
• Customer care improvement based on better collaboration,
customer knowledge and prevention strategies

13. Big Data Applied in Distribution
• Detailed customer information such as shopping patterns based on
web logs
• Inventory improvement based on social media analysis
• Loads integrity, transit times
• Fuel consumption and route optimization

14. Big Data Applied in Financial Services
• Contextual strategies and predictive analytics based on social
media or messaging contexts
• Contextual targeting - the right customer, at the right time and
through the right channel based on cross channel data
• Fraud detection based on analysis of historical data
• Operational efficiency by consolidating data from different systems
and multiple sources
• Real-time alerting and reporting

15. Euro IT Group Big Data Quick Wins with Hadoop
&
Understanding Benefits
of Implementing
Big Data with Hadoop
Immediate Business Results
• Generate new insights
• Enabling short term business
benefits
• Measurable results
• Test Hadoop Technologies
• Complement traditional BI / DWH
infrastructure with innovative
solutions.
• Expertise and Know How
Approaching big data in small steps

16. Euro IT Group Triple Expertise

17. Domain Specialists
Big Data Professionals
• Ensures results are matching
expectations.
• Peers with operator marketing
team.
• Integrates multiple data sources
• Develops or customize real-time
and batch processing big data
jobs.
• Complex statistical data analysis
requested by the marketing
specialist.
Big Data Specialized Delivery Team
Data Scientist
Software Engineers
Marketing Specialist

18. Technologies We Master
JAVA: Apache Tomcat, JBoss AS, Jetty,
IBM WebShere, Oracle, Application
Server, WebLogic, Windows Server
IIS, Nginx, NetWeaver)
PHP: CodeIgniter, CakePHP, Zend,
Yii, Kohana, Wordpress, Joomla,
Drupal, MODX, Magento,
Prestashop, IPBoard, Smarty
ASP.NET, Visual Basic, ASP.NET AJAX,
ASP.NET MVC, Remoting, Reflection,
ADO.NET, Entity Framework
MICROSOFT: C++,
C#, ASP.NET,
ASP.NET MVC,
Silverlight
MOBILE: Android, IOS,
Windows 8, iPhone SDK,
Android SDK, JQuery
Mobile, Flash Lite, J2ME,
Symbian, XMPP, SMS,
WAP
BIG DATA: Hadoop, Hadoop Map
reduce, Spark, Storm, Mahout,
Apache Pig, Apache Hive, Elastic
Search, Cassandra, Apache HBase
CLOUD: Amazon web
services, Amazon EC2,
Windows Azure

19. Technologies We Master
OTHERS:
• Web Services: Apache CXF, Axis, SOAP, WSDL, JAXB, JAX-WS
• Web technologies: XHTML, HTML5, XML, XSL, XSL-FO,XSLT, CSS, XPath, XQuery, SAX,
DOM, StAX, Xerces, Flash, Flex
• Content Management Systems: Stellent
• Messaging Middleware: ActiveMQ, IBM MQ Series, Fiorano, MQSonic, TIBCO
rendezvous
WEB: HTML5, XML, XHTML, XSLT,
DHTML, CSS, XSLT, Javascript, jQuery,
PHP
BUSINESS INTELLIGENCE:
Pentaho BI, crystal
Reports, Microsoft BI
Microsoft Visual Studio, Windows
API, ActiveX, XCode, wxWidgets,
STL, WinDDK, Qt Framework,
Microsoft CRM
AJAX & JAVASCRIPT: JQuery, YUI,
ExtJS, JSON,MooTools,
Prototype JS, Dojo, YUI,
Scriptacoulous, ASP.NET Ajax
control Toolkit, etc.

20. Want to talk to one of our data scientists?
www.euroitgroup.com
Contact us: office@euroitgroup.com
Talk to us NOW!