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Big data and customer analytics examples
1.
Big Data &
Big Data Analytics Experiences in building a 360°Integrated Customer View Pietro Leo Executive Architect Member of IBM Academy of Technology Leadership Team @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
2.
IBM Institute for
Business Value What are the main Factors impacting marketing & CMO? Big Data related dimensions! Marketing Priority Matrix 1 Data explosion Underpreparedness Percent of CMOs reporting 2 Social media underpreparedness 1 3 Growth of channel and device choices 70 4 Shifting consumer demographics 2 3 5 Financial constraints 4 6 Decreasing brand loyalty 60 5 7 Growth market opportunities Mean 6 8 ROI accountability 10 7 8 9 11 9 Customer collaboration and influence 50 12 10 Privacy considerations 13 11 Global outsourcing Factors impacting marketing 12 Regulatory considerations 40 Percent of CMOs selecting as “Top five factors” 13 Corporate transparency 0 20 40 60 Mean IBM @pieroleo www.linkedin.com/in/pieroleo 2 © 2012 IBM Corporation
3.
IBM Institute for
Business Value A new paradigm targeting a 360°Integrated Customer View in order to leverage the Customer Empowerment The new profession paints a predictive picture of each customer by harnessing data on a massive scale • Instruments all key touch points to gather the right data about each customer. • Connects social media data, transaction data and other information to paint a more vivid picture of each customer. • Runs the right analytics at the right time on the right customer to generate new ideas about whom to serve and how best to serve that person. • Generates insights that are predictive, not just historical. • Builds capabilities to do this on a massive scale. @pieroleo www.linkedin.com/in/pieroleo 3 © 2012 IBM Corporation
4.
How Big Data
Analytics can help? Create an integrated view of Customers Data & Content from ALL enterprise contact points including internal and external sources... social business! Enterprise Contact Points Customer Branch office Call Center Self Service IVR Social Agent Web Unstructured Structured Structured Data & Content Agent Data Call logs, Transcripts Customer/Product Emails, Surveys Transaction Data Big Data Enterprise Business Integrate and Analyze Structured and Unstructured Data products Analytics and services Insights Customer Intelligence Agent Performance Dissatisfaction Drivers Distribution Process Understanding Social Drivers Sales Drivers & Utilization @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
5.
IBM Institute for
Business Value A new paradigm targeting a 360°Integrated Customer View in order to leverage the Customer Empowerment Customer Virtual Ex2. Rebuild measuring marketing campaign Profiles & Ex1. Collect customers longitudinal point of views And correlete them with internal data @pieroleo www.linkedin.com/in/pieroleo 5 © 2012 IBM Corporation
6.
Example 1: Big
Data Analytics to collect Customer longitudinal point of views from Web 2.0 and correlate them with internal data Business challenge Social media is considered a new and relevant source to understand the consumer and improve service levels; measure its own as well as the competition’s brands and “Big Data is a great products; and compare results with traditional TV research data for better market awareness. Particularly, the company needed a social media analytics solution that opportunity for TV could: accurately measure the echo on Social Media about the efficacy of its products innovation in the next and campaigns; provide insight into competitors; years. TV viewing is Solution transforming into a multiplatform and IBM helped the client to analyze unstructured data across a number of social media channels and assess the company’s corporate brands, with respect to its competitors, participative experience: as well as to discover and statistical measure signals and alerts about viewer the better we know and preferences and experiences about TV contents. Specifically, among others, included: understand our viewers, detect hot words and design attitudinal indicators about company products and services the better we can serve discover new trends and hot words on Social Media in order to compare them and weight them with respect them." – Valerio Motti, to internal information streams Head of Marketing analyze findings and evaluate their significance with respect to business priorities Innovation, Mediaset correlate customer attitudinal attributes results of unstructured data analysis with internal data S.p.A. Benefits • Measure as well as discover a number of signals referred to TV viewers that were expressed in web 2.0 comments and referred to several kind of TV contents and TV ads campaigns. • A number of interesting correlations were discovered and evaluated among those signals and with respect to internal customer loyalty parameters that will contribute refine the company marketing strategy. @pieroleo www.linkedin.com/in/pieroleo 6 Nov 14, 2012 © 2012 IBM Corporation
7.
Example 1: Big
Data Analytics to collect Customer longitudinal point of views from Web 2.0 and correlate them with internal data Information Sources Working Environment OUTPUT STRONG WEAK SIGNALS SIGNALS Content Provider / Aggregators Social Intelligence Workplace Regular Reporting & Monitoring Model taxonomy Hotwords Taxonomy RSS Sorgenti FeedRSS TAXONOMIES RELTATIONS REPORTING SENTIMENT Specialized ADVANCED SEARCH: CONCEPT Analysis & DISCOVERY Studies DISCOVERY and ANLYSYS OF ALERTS INFLUENCERS @pieroleo www.linkedin.com/in/pieroleo 7 © 2012 IBM Corporation
8.
360-degree Consumer Profiles
from Social Media Personal Attributes Personal Attributes • Identifiers: name, address, age, gender, • Identifiers: occupation… name, address, age, gender, • occupation… Interests: sports, pets, cuisine… Timely Insights Timely Insights • Intent to buy various products • • Interests:Status: marital, parental Life Cycle sports, pets, cuisine… • Life Cycle Status: marital, parental • • Intent to buy various products Current Location • • Current Location Sentiment on products, services, campaigns • • Sentiment on products, services, campaigns Incidents damaging reputation • • Incidentssatisfaction/attrition Customer damaging reputation • Customer satisfaction/attrition Life Events Life Events • Life-changing events: relocation, having a • Life-changing events: relocation, having a baby, getting married, getting divorced, buying a baby, getting married, getting divorced, buying house… a house… Products Interests Products Interests • Personal preferences of products • • Personal preferences of products Product Purchase history Relationships • • Product Purchase historyservices Suggestions on products & Relationships • Suggestions on products & services • Personal relationships: family, friends and • Personal roommates…relationships: family, friends and • roommates… Business relationships: co-workers and • Business relationships: co-workers and work/interest network… work/interest network… Monetizable intent to buy products Life Events I need a new digital camera for my food pictures, any College: Off to Stanford for my MBA! Bbye chicago! recommendations around 300? my food pictures, any I need a new digital camera for College: Off to Stanford for my MBA! Bbye chicago! recommendations around 300? Looks like we'll be moving to New Orleans sooner than I thought. What should I buy?? A mini laptop with Windows 7 OR a Apple Looks like we'll be moving to New Orleans sooner than I thought. MacBook!??! I buy?? A mini laptop with Windows 7 OR a Apple What should MacBook!??! Intent to buy a house I'm thinking about buying a home in Buckingham Estates per a Location announcements I'm thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx #austinrealestate recommendation. Anyone have advice on that area? #atx #austinrealestate #austin I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj #austin I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
9.
Example 2: Big
Data Analytics to expand knowledge about customer profiles and measuring marketing campaign • Analysis of social media messages for large Media and Entertainment company to determine reaction to movie commercials aired during the SuperBowl • Insights based on 30M+ social media consumer profiles created by analyzing over a Billion messages • Real-time evolution of insights correlated with the airing of the commercial Key Business Questions: Consumer demographics by movie Battleship How many people are talking about the film ? Gender Female Male • Intention to see the movie, Impact of SuperBowl commercial Top 10 Markets Who are they ? The Dictator • Demographics, Influencers, avid movie goers Gender Female Male Top 10 Markets What is the reaction ? • Categorized sentiment (plot, characters, …) Gender Act of Valor Female Male • Comparison with competitive movies Top 10 Markets 10% 20% 30% 40% 50% 60% 70% 80% 90% Competitive Intelligence Comparing feedback by important microsegment (avid movie- Buzz Amongst Avid Movie-Goers Jan - goers) Amongst Avid Movie-Goers During Buzz Feb Super Bowl Think Like a Man Act of Valor 21 Jump Street The Lorax The Dictator Project X The Lorax Act of Valor John Carter Project X Battleship The John Carter Ghost Rider Avengers The Dictator 21 Jump Street The Dark Knight Rises The Dark Knight Rises Battleship G.I. Joe @pieroleo www.linkedin.com/in/pieroleo Spider-man The Avengers Spider-man © 2012 IBM Corporation Ghost Rider G.I. Joe
10.
IBM SDA: Social-media
Based Micro-segmentation and Real-time Correlation InfoSphere Streams Online Flow: Data-in-motion analysis Social Entity Predictive Media Data Ingest Text Analytics: Integration: Analytics: Timely Data & prep. Timely Insights Profile Action Resolution Decisions Determination Text Entity Social Media Entity Predictive Social Media Social Media Customer Customer Customer Social Media Analytics Integration Integration Analytics Consumer Consumer & Prospect & Prospect Models Data Data Profiles Profiles profiles profiles InfoSphere BigInsights Consumer Consumer Customer Customer Offline Flow: Data-at-rest analysis Lists Lists Database Database Large-scale data-at-rest analysis using InfoSphere BigInsights Large-scale data-at-rest analysis using InfoSphere BigInsights Large-scale data-in-motion analysis using InfoSphere Streams Large-scale data-in-motion analysis using InfoSphere Streams Advanced text analysis, entity integration, and predictive modeling using common analytics Advanced text analysis, entity integration, and predictive modeling using common analytics infrastructure across Streams and BigInsights infrastructure across Streams and BigInsights @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
11.
@pieroleo
www.linkedin.com/in/pieroleo © 2012 IBM Corporation
12.
Business Models based
on connecting Virtual and Real Words: the AMEX model A portal that collects special offers and discounts from retailers and detail about the customer segment that is target Marketing segmentation engine that evaluate customer profiles and select the best coupon to propose American Express Moble app and connection with Smart Offer Twitter, Facebook e Foursquare to communicate with the customers and enable viral effects Just virtual Coupons are managed! Customers activate the coupon and receive on montly basis on the credit card account the equivalent of the coupon discounts after that transactions were registred @pieroleo www.linkedin.com/in/pieroleo 12 © 2012 IBM Corporation
13.
Grazie!
Pietro Leo Executive Architect Member of IBM Academy of Technology @pieroleo www.linkedin.com/in/pieroleo @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
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