Big Data in eCommerce

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)

Big data benefits in eCommerce

product portfolio

pricing

online/in store experience

advertising/marketing budget

customer service

inventory

OPTIMIZED

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)

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.

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.

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

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

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.

• 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”

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

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

•

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

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)

Euro IT Group Triple Expertise

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)

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

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

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

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.

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