Euro It Group teams have delivered dozens of eCommerce platforms. We cover the complete spectrum of eCommerce services from software architecture and development to additional plug ins development, modules extensions, user experience design, testing, maintenance and support, integration with other business systems (eg CRM, ERP, inventory), SEO, marketing, store migration or mobile responsive versions of your eCommerce platform.

1. Big Data in eCommerce

2. Why big data?
• 73% higher sales for companies which use Predictive
Analytics than those who have never done it?
• 65% of customers feel completely frustrated and annoyed
when you’re sending them incoherent offers and experiences
through different channels
• 60% increase in business margins and a 1% improvement in
labor productivity for retailers who started using Big Data
(McKinsey)
• 45% of online shoppers are more likely to shop on a site that
offers personalized recommendation (invesp Consulting)

3. Big data benefits in eCommerce
product portfolio
pricing
online/in store experience
advertising/marketing budget
customer service
inventory
OPTIMIZED

4. Big data benefits in eCommerce
By tailoring offers to each individual customer, retailers are seeing an increase in
returning clients. Customers nowadays are looking for the easiest and most convenient
way to shop and Big Data allows retailers to understand their customers’ needs before
they even enter a store
Best
personal
shopping
experience
Most effective
merchandising
and stocking
Enhanced
customer
loyalty
Real-time, targeted
promotions
broadcasted directly
to customers’ smart
phones while they
shop by examining
purchase history,
online “travel”, likes
via social networks,
geo-location, retailers
can now create
Supported by data
coming from online
sources, retailers can
now pinpoint which
merchandise should
be stocked at specific
locations and where
items should be
placed throughout
the store (eg:
pregnant woman
seeing baby products
at the entrance in a
shop)

5. Bikeberry
Bikeberry online store collects numerous customer data, including browsing patters, login
counts, past purchases and more and uses that information to create various individually
tailored offers (eg: free shipping, 15% off, $30 off new products, etc) relevant to their
preferences and past behavior.
“We wanted to maximize existing customer spending and avoid giving away offers to
customers who would convert without needing additional incentives." Looking to incentivize
customers with the right offers, BikeBerry sought a more targeted approach to retention and
re-engagement.
Results:
• 133% sales increase
• ~200% user on-site engagement increase
• Money saved by not offering irrelevant/not needed discounts
• Doubled the number of return customers
• Return customers spending over 30% more
Targeting Customers with the Right Offers Retention Science's targeted marketing
technology has BikeBerry's customers coming back—and spending more.

6. Walmart
“Our ability to pull data together is unmatched”
Walmart CEO Bill Simon
• In 2011 they made clear that Big Data will become of their DNA; they purchased Kosmix, a
social media start-up focused on e-commerce, they developed a software application which
had the ability to search and analyze social media applications in real-time
• 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.

7. Alibaba
Can big data help fight fake goods? Will big data be
their competitive advantage in online banking?
Alibaba Group includes 25 Internet-based businesses that cover business-to-business online marketplaces,
retail payment platforms, shopping search engine and data-centric cloud computing services.
Huge data volumes -> Q4 2014: $127 billion gross merchandise volume, a 49% jump from the same period
last year. They need a system in place that samples and analyzes thousands of transactions beyond
keyword search and strive to:
• ensure data security and quality (e.g. protection of privacy and individual businesses, remove false data)
• provide uniform data across various departments
• access to external data (such as cooperation with other platforms)
Alibaba’s efforts:
• working on building the underlying architecture for data collection and sharing, than performing deep analyzing
and data mining.
• image recognition technology (since 2013) to scan photos of merchandise in real-time and detect the items’
brands by logos. Today, the company has more than 1 million images of fake goods in its database and processes
300 million visual checks a day on its system, according to Chinese media reports
• They now strive to go beyond detecting counterfeits to tracking the sources of fake products; supported by data-
mining technology, Alibaba can map out the locations to identify potential suppliers of fake goods
• Will use big data to gain market share quickly into China’s online banking sector (it is already known in the
financial market within China
Credit Scoring in eCommerce?
Yes, it can go beyond finance, its credit scoring model taps over 1,000 data sets within the group, including
Revenue growth, Transaction data, Position/rating of the client in their own industry, User ratings and purchases;
Changes in rating within industry, How many users rate the seller as a favorite, etc

8. Ebay – focus on the customer journey
• The world’s largest online marketplace: 100+ million customers who list items in 30.000+
categories, thousands of dollars in transactions per second, the system handles extreme data
velocity with 6+ billion writes and 5+ billion reads daily
• Recording everything every customer does generates 100 million hours of customer interaction
(per month), creating an unmanageable amount of customer data
• Asking a simple business question such as “what were the top items that showed up in searches
yesterday? “involves processing five billion page views
• The site wants to run sentiment analysis, network analysis and image analysis, all of which cannot
be run in a traditional transactional database
• eBay focused on analytics since it started targeting major retailers (in Europe: El Corte Ingles,
House of Fraser, etc) – not via the IT organization but through finance, covering search and listing
optimization, marketing, inventory management, shipping, economics, fulfillment strategy,
improving the buyer experience, risk management, fraud, trust and safety policies, negative
account actions, and other strategic and operational needs
• Third side analytics triangle: Hadoop along with an Enterprise Data Warehouse and singularity
custom data analytics to follow the customer journey
• “The system can’t go down. Every day we process 50TB of data, accessed by 7.000+ analysts with
700 concurrent users”
The web can offer the same experience as a local shop […] We can learn customer’s intentions

9. Amazon
29% of sales through their recommendations engine
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
• item-to-item collaborative filtering- determine what a customer has placed
inside their virtual shopping cart and which items they’ve recently viewed and
purchased in the past. Amazon calls this technique,
• Virtual customer service - within seconds of entering Amazon.com, consumers
are presented with merchandise options that they have already considering
purchasing.

10. • 800,000+ sellers and 40+ million monthly visitors, $2 billion in goods sold last year, and roughly $200
million in revenues, the world’s most vibrant handmade marketplace
• Focus on building confidence in known tools, master them, and then use them to solve big problems.
• Relying on machine learning and Big Data processing to store, sort and analyze data (how users
navigate web pages, how long they check a product, etc)
• Conjecture, Hadoop (for large scale data mining, analytics and behavioral analysis) Scalding (helps
developers write easily programs for MapReduce, Hadoop’s main processing engine),and Apache Kafka
(operational insights, especially from use of the company's mobile app and load information to
Hadoop’s storage system), HP Vertica (good analytic query performance, rich analytics, runs on
commodity hardware, enabled inhouse replication solution, cost effective, good support)
Benefits:
• personalized shoppers’ experiences and search results as visitors move throughout the site, ability to
discern individual tastes and provide recommendations in real time, based on online behaviour
• improved ability to make complex predictions about user behavior and find commonalities in product
features
• By using machine learning algorithms to find common characteristics between individuals and their
networks of fellow shoppers, “we can make some reasonably good predictions that you might like the
same products that someone from your network might like”
Etsy
Not what's cool, but rather what's optimal for a given problem
“Because of the uniqueness, we have to do a lot more work to figure out what
you’re looking for”

11. Nordstrom
Innovation with Big Data experiments
• One the first big retailers to adopt a full-scale online channel
• $1 billion investment in its e-commerce channel over the next five years announced in 2011
• Manages huge volumes of Big Data coming from their website, in-store sales, as well as through their
social channels (Facebook , Pinterest, Instagram or Twitter)
• Nordstrom Innovation Lab - they allocate 30% of their capital budget on technology and
focus on creating retail trends and on gaining insights into customer shopping trends and patterns
• Modern features they already use:
• new technologies like sensors and Wi-Fi signals to track who comes to their stores, which parts
of the store they visit, for how long, and other customer behaviors inside their stores
• modern website and mobile apps
• integrated online and in store inventory –> it shows customers in real time where a product is
available and when they can expect to receive it; interactive touch screens in fitting rooms
• tracking Pinterest pins to identify trending products and to use that social data to promote
popular products in their shops and to induce shoppers to buy them
• some stores allow customers to see Instagram images and reviews of specific products that
they like on a large screen
• “co-shopping” concept where customers and sales staff "shop together”
• by heavily incentivizing customers to use Nordstrom’s credit card and sign up for their Fashion
Rewards Program they’re able to collect extensive data on customer purchases and
preferences that helps them improve marketing and product design, and optimize ad spend.
• Experiments can be also risky -> privacy concerns after a pilot program for a special technology
that tracks shoppers’ in-store foot traffic and behavior via cell phone signals

12. Big Data Applied in Retail/eCommerce
• Identify customers who are likely to buy specific products and anticipate demand
based on their social media activity, their browsing patterns, their purchase history, blogs
and forums activity, your customer data, demographic data, weather data, geopolitical
situation, etc
• Recommendations, advertisements or real time offers based on advanced customer
segmentation and shopping patterns, thus influencing purchase decisions and upsell
• Smarter shopping experience, smarter merchandising and marketing
• Predictive analytics that enable you to optimize pricing, inventory levels, check your
competition pricing, predict the hot items of the season, improve your customer service,
increase customer satisfaction and your margins
• Contact your customers and prospects when they are ready to buy, on their
preferred channel (social media, SMS, email) in the most appropriate location (driving, at
work, at the mall)
• Concrete use case:
you arrive at the local mall -> you check in via Facebook ->
within a matter of minutes, you get 30% coupon for purchases today at X
retailer via email or SMS
•

13. Big data for small retailers
Big Data threatens to create a deep divide between the have-datas and the have-no datas
From intuition-driven to analytics-driven companies
What if?
…you were able to analyze your site's visitors behavior,
understand what engages them and detect best promotional
and cross-selling opportunities?
…you could analyze the relationship between social-media
conversations and buying trends?
Best practices
• Take a holistic view of your business and define the goals which you
want to achieve – observe, learn but don’t copy competitors’
techniques and strategies because your goals are unique to your
business and what worked for others many not work for you
• Identify problems which you aim to solve -> see how Big Data can
help
• Start small – once you see results plan bigger gradually
• Solution for a small retailer:
• Flexibility: must allow you to choose only the functionalities
you need and leverage the systems already in place
• Simplicity: easy to deploy and to start using it, easy to use

14. Customer privacy controversy
• 1 of 3 Internet users say they have stopped using a company's
website or have stopped doing business with a company
altogether because of privacy concerns (2013 Truste study)
• Some real life scenarios that involved privacy concerns:
– Nordstrom: customer privacy concerns after using sensors from an analytics
vendor to get shopping information from customers' smartphones each time
they connected to a store's Wi-Fi service – due to widespread criticism from
privacy advocates, Nordstrom is no longer using the service
– Urban Outfitters hip clothing retailer is facing a lawsuit for allegedly violating
consumer protection laws; they told shoppers who pay by credit card that
they had to provide their ZIP codes. The purpose was to obtain the shopper’s
address.
– Facebook is often at the center of a data privacy controversy, whether it's
defending its own enigmatic privacy policies or responding to reports that it
gave private user data to the National Security Agency (NSA)
– Target was able to deduce that a teenage girl was pregnant even before her
father even knew (read the full story)

15. Euro IT Group Triple Expertise

16. Euro IT Group e-Commerce Expertise
• 60+ ecommerce projects covering:
• Complex ecommerce platforms development from scratch
• Integration through APIs and XML with existing business systems (eg:
finance, CRM, ERP, inventory management, call center system, etc
• Additional plugins/modules extensions
• Smooth store migrations to mobile responsive versions of the
ecommerce platform
• We cover the full spectrum of services from consultancy,
prototypes, architecture design, UX design,
implementation/integration, testing, maintenance and support
of multi-platform applications
• Experienced in most open source ecommerce platforms
• Proven track record in developing and customizing cross
border e-commerce platforms (localization, multi-lingual,
multi-currency, integration with all major payment platforms)

17. 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

18. 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

19. 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

20. 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.

21. Want to talk to one of our data scientists or
our e-Commerce experts?
www.euroitgroup.com
Contact us: office@euroitgroup.com
Talk to us NOW!