Big data and customer analytics examples

1,741 views

Published on

Big data and customer analytics examples

Published in: Business
1 Comment
2 Likes
Statistics
Notes
No Downloads
Views
Total views
1,741
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
101
Comments
1
Likes
2
Embeds 0
No embeds

No notes for slide

Big data and customer analytics examples

  1. 1. Big Data & Big Data Analytics Experiences in building a 360°Integrated Customer ViewPietro LeoExecutive ArchitectMember of IBM Academy of Technology Leadership Team @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  2. 2. IBM Institute for Business ValueWhat 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 opportunitiesMean 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/pieroleo2 © 2012 IBM Corporation
  3. 3. IBM Institute for Business ValueA new paradigm targeting a 360°Integrated Customer View in orderto 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/pieroleo3 © 2012 IBM Corporation
  4. 4. How Big Data Analytics can help?Create an integrated view of Customers Data & Content from ALL enterprisecontact points including internal and external sources... social business! Enterprise Contact PointsCustomer 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. 5. IBM Institute for Business ValueA new paradigm targeting a 360°Integrated Customer View in orderto 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/pieroleo5 © 2012 IBM Corporation
  6. 6. Example 1: Big Data Analytics to collect Customer longitudinal point ofviews from Web 2.0 and correlate them with internal dataBusiness challengeSocial media is considered a new and relevant source to understand the consumerand improve service levels; measure its own as well as the competition’s brands and “Big Data is a greatproducts; and compare results with traditional TV research data for better marketawareness. Particularly, the company needed a social media analytics solution that opportunity for TVcould: accurately measure the echo on Social Media about the efficacy of its products innovation in the nextand campaigns; provide insight into competitors; years. TV viewing isSolution transforming into a multiplatform andIBM helped the client to analyze unstructured data across a number of social mediachannels 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 andpreferences 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 servediscover 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 Marketinganalyze findings and evaluate their significance with respect to business priorities Innovation, Mediasetcorrelate 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/pieroleo6 Nov 14, 2012 © 2012 IBM Corporation
  7. 7. Example 1: Big Data Analytics to collect Customer longitudinalpoint 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/pieroleo7 © 2012 IBM Corporation
  8. 8. 360-degree Consumer Profiles from Social MediaPersonal 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/attritionLife Events Life Events• Life-changing events: relocation, having a • Life-changing events: relocation, having ababy, getting married, getting divorced, buyinga baby, getting married, getting divorced, buying house… a house… Products Interests Products Interests • Personal preferences of products • • Personal preferences of products Product Purchase historyRelationships • • Product Purchase historyservices Suggestions on products & Relationships • Suggestions on products & services• Personal relationships: family, friends and • Personalroommates…relationships: family, friends and• roommates… Business relationships: co-workers and • Business relationships: co-workers andwork/interest network… work/interest network…Monetizable intent to buy products Life EventsI 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 well be moving to New Orleans sooner than I thought.What should I buy?? A mini laptop with Windows 7 OR a Apple Looks like well 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 Im thinking about buying a home in Buckingham Estates per aLocation announcements Im 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 Im at Starbucks Parque Tezontle http://4sq.com/fYReSj #austin Im at Starbucks Parque Tezontle http://4sq.com/fYReSj @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  9. 9. Example 2: Big Data Analytics to expand knowledge about customer profiles andmeasuring marketing campaign• Analysis of social media messages for large Media and Entertainment company to determine reactionto 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 commercialKey Business Questions: Consumer demographics by movie BattleshipHow many people are talking about the film ? Gender Female Male• Intention to see the movie, Impact of SuperBowlcommercial Top 10 MarketsWho are they ? The Dictator• Demographics, Influencers, avid movie goers Gender Female Male Top 10 MarketsWhat 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. 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 PredictiveSocial 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. 11. @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  12. 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 thecustomer segment that is targetMarketing segmentation enginethat evaluate customer profiles and select the best coupon to propose American ExpressMoble app and connection with Smart Offer Twitter, Facebook e Foursquare to communicatewith 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. 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

×