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What’s it all about?“Big Data
is about the technologies and practice of handling huge data sets that conventional database management systemscannot handle them efficiently, and sometimes cannot handle them at all. Often these data sets are fast-streaming too, meaningpractitioners don’t have lots of time to analyze them in a slow, deliberate manner, because the data just keeps coming.Sources for Big Data include financial markets, sensors in manufacturing or logistics environments, cell towers, or trafficcameras throughout a major metropolis. Another source is the Web, including Web server log data, social media material(tweets, status messages, likes, follows, etc.), e-commerce transactions and site crawling output, to list just a few examples.” (Andrew Brust from ZDNet)) In 2005, humankind created 150 exabytes of information. In 2011, 1.200 exabytes will be created. (The Economist) Volume The “V” drivers Worldwide digital content Velocity Variety 80% of enterprise data will be will double in 18 unstructured, spanning months, and every 18 traditional and non traditional months thereafter. (IDC) sources. (Gartner) 2
Big Data sources inside Social
Business Ecosystem Wholesale/Retail Outsourcer My relations My relations My world My world Myself Myself My relations My relations My relations My world My world My world Public authority Myself Myself Myself Customer My relations My relations My world Company My world Myself Myself Supplier Partner 3
Let’s get a Social CRM
definition “Social CRM is a philosophy and a business strategy, supported by a technology platform, business rules, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It is the companys programmatic response to the customers control of the conversation.“ Paul Greenberg CRM books author, speaker, consultant, analyst 4
The shift from CRM to
Social CRM CRM SOCIAL CRM Collaborative - Phone - Social Network - Email - Micro blogging site - Mail - Blog - Fax - Forum - Web form - Collaborative platform - Face2face Operational - Contact & Case Management - Social media monitoring - Trouble ticketing Management - Unified Agent Desktop - Marketing automation - Enterprise Collaboration - SFA - Collaborative KM - KM/BPM/ERP integration Analytical - VoC - Data Mining - Business Intelligence 5
The Big Data funnel for
Social CRM Real Life EXTENDED HUMAN EXPERIENCE Touchpoint Transactional data Traditional interaction data Web & social data Location-based data Data streams Information Insight 6
Now we are plenty of
“human” data Customer Myself My world My relations - Geographic: - Information gathering: - Conversation: Where I live How I compare Where I discuss Where I work What I compare What I discuss about Where I spent my holidays What drive my choice How I contribute - Socio-demographic: What I choose - Psychographic (outspoken): My age -Transaction: What I like My gender What I buy What I believe My family size How I buy What I think about My income Where I buy What I don’t endure My occupation When I buy - People: My education - Usage: What people I relate with My religion How I use Whom I’m influenced by My nationality How much I use Who I influence - Psychographic (formal): Where I use My lifestyle When I use My personality - Interaction: My values Information need Trouble/problem Claim Praise 7
How can we handle it?
“A wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it” Herbert Simon, Economist "We have free and ubiquitous data, so the complimentary scarce factor is the ability to understand that data and extract value from it.“ Hal Varian, Googles Chief Economist Human intervention is fundamental for decision making but we need help and support to process and understand data because of our cognitive and time limitations 10
What’s in it for me?
Proactive selling Lead generation Application Event/Trend detection Proactive routing Churn prediction Real-time question answering Fraud detection Location-based marketing Spatial information analysis Sentiment analysis DESCRIPTIVE & PREDICTIVE ANALYTICS Analysis History information analysis Semantic analysis Behavioral analysis Opinion extraction & summarization DOMAIN Optimization Natural Language Processing Classification Association rules learning Methods Clustering & Factoring Scoring Regression Ensemble learning Time Series Analysis Social Network Analysis Data Number Free Text Tag Audio Image Video 11
Can we trust Analytics? People
are quite confident about numbers but are suspicious of “unstructured data” algorithms’ output accuracy Tools can normally reach 80% accuracy but you have to express skepticism for >95% values (overfitting) High accuracy doesn’t always mean more positive business impacts The 4 “What” on accuracy What do you need to What scale and What accuracy measures What is the accuracy measure to accomplish measurement will help you fit your own business impact on business? your own business tasks? translate sentiment into needs? business decisions? You may want to analyze at You may prefer an explicit Most people confuse Not all inaccuracies have document level (tweet, class or a score. Or maybe accuracy with precision. But equal business impact. You email, etc.) or at feature level you need more mood than accuracy is a function of may focus your attention (named entity, concept, valence. precision and recall so only to some kind of errors topic, etc.) remember that results are and drop others depending relevant if they can help you on your business objective respond to a specific business challenge. 12
An example for Social Customer
Service Most probable issue-related contents Automatic routing to selected CSR retrieval High Churn Automatic response High LTV (real Q&A) Most frequent issue Most frequent Churn Score LTV Score (service request) issue (concept) Churn Prediction Customer LTV Customer Claim history Concept highlighting Polarity highlighting Behavioral Analysis / History Information Analysis Opinion extraction / Semantic Analysis / Sentiment Analysis Transaction / Billing / Payment / Usage / Interaction Conversation / Psychographic / Relations 13
Great opportunities but pay attention
to the issues Liability Data policies Security “Sharing” Privacy Data access obstacles “Sharing” incentive Main issues Analytical to address culture Change management Technology Distributed architectures Massive parallel processing Talent 14