How HP is learning to predict customer behavior by merging social media and company data

Introduction• The potential of social media is exciting, but it is not clearly known how much business value it provides beyond relatively basic ―buzz‖/sentiment monitoring and customer communications.• An internal pilot completed by HP Labs and HP Global Customer Intelligence shows that data from social media can be merged with company data to predict customer behavior with up to 90% accuracy. 2 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Big Data at HP Global • Operate in over 170 Countries Operations • ~2 Billion Customer Interactions a year Complexity • 50 Million voice contacts • 1 billion customers • Unstructured and structured data HP Business Product • Ship 1M printers a week Information • Ship 48M PC’s a year Line Complexity • Ship a server every 1 sec 1 Complexity3 © Copyright 2010 Hewlett-Packard Development Company, L.P.

CI Transformation Model – To Minimize Information Asymmetry 2008 to 2010 2010 to 2013 Transactions Demographic Syndicated Primary Social Dynamic Research Preferences Grow and Media Retain your Customers 360 Improve Marketing Information Asymmetry Infrastructure Customer View Effectiveness Build Brand Loyalty Beyond Reason User Unstructured Digital Bread InDirect Data Pyschographic Channel Survey Crumbs Touch History4 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Merger of Structured and Unstructured Data Structured Data Unstructured Data Improve existing knowledge Expose new knowledge Customer Sentiment & Customer Profile Perception What they Think What they Do Transactions Intentions & Suggestions Customer Preferences Responsiveness Influence & Evangelism6 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Business Problems Revenue Marketing Enhancement Automation Improve Increase Product Social Retention Call Center MKTG Launch Targeting Marketing Efficiency Effectiveness Ho do we optimize marketing spend and mix by measuring media buzz?  How do we optimize call center resources based on consumer sentiment?  Products Review Timeline Data Reviewed  Photosmart Premium TouchSmart Web All-in-  384 Consumer reviews from Amazon, CNET and One Printer - C309n 12 months HP Forums  Photosmart Premium All-in-One Printer - C309g  6 Professional reviews from CNET, PC World  515K purchases sales trends7 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Sentiment Understanding with Unstructured data ―I am not sure what people are complaining about with this HP software but it installed seamlessly and is intuitive in its operations. Even though I am really really dissatisfied with the paper tray all together I am happy with this printer.‖ Measures Attributes cartridge paper tray price printer Software Attributes 0 -1 0 +1 +1 Measures 8 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Minimizing the knowledge gap by mergingunstructured with structured data Product Profile # purchases # tickets # service product launch orders date Behavior 2,131,517 856 98 10/1/2010 • Sales STRUCTURED DB • Frequency Product ID • Value • Sales forecast • Service • Call Center Optimization Product ID Perception UNSTRUCTURED DB • Sentiment Cartridge Paper Tray Price Printer • Intentions 0 +1 -1 +1 • Suggestions • Influencers9 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Exemplary Findings I A strong media signal for a product lead to increased sales/registrations. Analysis Results 7000 30 r=0.58 6000 25 r=0.72 (excludes holiday season) 5000 20 • Medium to high level of Correlation 4000 • Media signal from consumer forums 15 tends to follow purchase trends 3000 10 2000 Business Application 1000 5 • Outbound Marketing Optimization (marketing mix – spend, audience, 0 0 channel) Purchases Registrations 2 per. Mov. Avg. (Weighted3) + Professional) Weighted Rating (Consumer10 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Exemplary Findings II A strong negative polarity in media signal for a product lead to increased support tickets. Analysis Results120 20 r =—0.62 (Overall product quality) r =—0.95 (Print Quality)100 15 • High Correlation 1080 5 • Correlation strength increases as the60 attribute granularity increases (e.g. 0 Print Quality)40 -520 -10 Business Application 0 -15 • Operational Triggers: i.e. -- 12 month time period -- managing the Customer support resources based on consumer 2 Tickets/Purchase(Tickets/Reg) per. Mov. Avg. 2Consumer negative sentiment per. Mov. Avg. (Sum(Csentiment-pq)) sentiments 11 © Copyright 2010 Hewlett-Packard Development Company, L.P.

Implementation Framework Data Infrastructure Analysis Capabilities Unstructured Data Cons Experts Blogs 360 consumer Reviews Reviews view Call center Forums Social News Media Primary Key Product ID Interactive Structured Data Registration Online Support Mobile Acxiom Experian12 © Copyright 2010 Hewlett-Packard Development Company, L.P.

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How HP is learning to predict customer behavior by merging social media and company data

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