Ready, Aim, Fire! Using Social Media Analytics to Hit Your Target Market
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The document outlines the agenda and key speakers for a social media analytics seminar held from October 11-13, 2012, featuring experts Naghamana Majed and Janki Vora from IBM. It discusses the importance of social media analytics in understanding consumer behavior and developing marketing strategies that respond to customer insights. Additionally, it highlights the significant amount of unstructured data generated through social interactions and its potential value for businesses.
Ready, Aim, Fire! Using Social Media Analytics to Hit Your Target Market
- 1. October 11–13, 2012 Social Media Analytics
- 2. Seminar Agenda Session Objectives + 10 mins Moderator and Speaker Introductions Each panelist shares content – 15 mins / panelist 30 mins Naghmana Majed Janki Vora Moderator/Audience panel Discussion 35 mins
- 3. Naghmana Majed Naghmana Majed is a Certified Sr. IT Architect with Global Solution Center (GSC) at IBM. Naghmana is one of the technical leaders at the center; she is currently responsible for leading high-value, complex solution design and development for our some of our major clients. Her current area of focus and expertise is Business Analytics and Optimization (BAO) including social media analytics and predictive analytics. She is also BAO lead at the GSC for Industrial sector. As a senior technical leader, Naghmana has successfully led many high impact Analytics initiatives for IBM. Over her more than 16 years of experience, she has extensively consulted with clients on developing cutting-edge solutions that address tactical as well as strategic business needs. She is currently leading multiple social media analytics and predictive analytic solutions for our clients.
- 4. Janki Vora is currently pursuing her second masters degree in Statistics and Predictive Analytics from Northwestern University, she graduated with bachelors in Computer Science from Nirma Institute in India and masters in Computer Science from Georgia State University (GSU), Atlanta, GA. Professionally, Janki has worked with IBM for over 8 years. She is currently working as a Senior IT Specialist with Global Solution Center Dallas at IBM. She has been trusted to handle several critical projects. Her significant contribution came when she was deployed in Pune, India to train software engineers and setup a support center for IBM in India. Janki, has extensively worked on high impact social media analytics project. Her experience working on BigData project along with IBM Research where twitter data was collected for months, analyzed for sentiment of the movies was an eye opener where she learnt about the challenges of harnessing BigData and the benefits of Social Media Analytics.
- 5. Social business drives an unprecedented need for insight from natural language conversations 500 billion impressions annually made about products and services ** 770 million people 44x information worldwide visited growth by 2020 *** a social networking site * Public Social Media Corporate Social Business Conversations about strategy, projects, issues, risks, outcomes … Conversations about quality, experience, price, value, service … In addition to conversations about quality, experience, price, value, service … • Forums and Newsgroups • Wikis, Blogs and Microblogs • Wikis, RSS and Forums • Social Networks • Email and Collaborative Content • Social Media News Aggregators • Call Center Notes and Recordings Sources: * comScore, Social Networking Phenomenon ** Empowered, a book by Josh Bernoff / Ted Schadler • Customer and Employee Surveys *** IDC Digital Universe Study, May 2010 • Reports, Minutes and Research 5
- 6. Social Media Analytics Extends Traditional Segmentation Econometric: Time until Real-estate & Repurchase in Unemployment Key Categories Age + Income + Most segmentation Annual Geography approaches only Transactions Annual Participation focus here Spend in Loyalty Level Program Use of In- Preferred House Credit Product Card Use of Categories Service Return / Programs Preferred Exchange Channel Behavior Breadth of Categories Social media analytics can provide an Length of Time as Shopped increased aperture of the consumer Customer Response and the ability to see new patterns to Media and opportunities Recency + Frequency + Value © Copyright IBM Corporation 2012
- 7. Align – Detect & Capture © Copyright IBM Corporation 2012
- 8. Anticipate – Analyze & Predict Segmentation Ability to find hidden clusters of people Example: Identify those likely to respond Association Finding things done in tandem Example: Identify products likely purchased together Classification Identify attributes causing something Example: cascading attributes of defection behavior © Copyright IBM Corporation 2012
- 9. Identify clusters of customers using real time insights Insights on the Customer Action Clusters Extreme Loyalist High Online Discounts Purchase Size Low High New Shopper Brand Focused Frequency Low Offer Marketing Channels Email Marketing Model Segmentation Website Event-Based Batch Zip Code Phone Apps Traditional Household Marketing Neighborhood Others… Location Process House Prices © Copyright IBM Corporation 2012 Source: IBM Research 9
- 10. Act – Embed & Engage © Copyright IBM Corporation 2012
- 11. Understand who your customers are, interact with them, drive them to your business and learn from them Twitter 1 Lisa Lisa qCrowd Environment posts a comment 2 qCrowd identifies Lisa as a prospect on Twitter 3 qCrowd Analytics finds Lisa’s location 4 Lisa’s info is sent to the qCrowd webpage qCrowd 6 Marketing Team posts a relevant message to Lisa Marketer's qCrowd UI Marketing Team 5 Marketing Team analyzes results © Copyright IBM Corporation 2012 and decides to take action Source: IBM Research
- 12. Use Case: Telco - Retail and Social Media Telco Location Analysis
- 13. Tesco Fan Page Tesco Customer Profile Product Catalog Customer Profile Telco 1 - Registers with Retailer, gives 2 – Follows a friend’s Permissions to Retailer and Telco post on FB and clicks the Like button on a camera she likes 4 Receives a 3 – Intelligent Advisor message with an platform offer reminding her processes Lisa’s 6 Lisa uses to stop by if she’s activity for promo code in the area Intelligent relevant actions using to purchase Advisor Platform Telco and Retailer information offer at POS Lisa 5 Receives promo code for offer while passing by the store Telco / Retailer Action Source: IBM Academy Study
- 14. “…today, 86% of US online adults interact with social media in some way“ – Forrester May 2012 Why is this important? Merrill Lynch in 1998 cited estimates that as much as 80% of all potentially usable business information Unstructured originates in unstructured form. 80% Electronic documents Source: Christopher C. Shilakes and Julie Tylman, ‘Enterprise Information Portals’, Merrill Lynch, 16 November, 1998. Images Video Blogs Voice Sensors Wikis Audio This volume of unstructured content was questioned and has since been validated. Further, for some companies the number is closer to 85%. 20% Instant messages E-mails Databases Structured
- 15. Entire new market of channels Old Paradigm New Entrants Awareness Intent Purchase Source: IBM Academy Study
- 16. Use Case: Insurance and Social Media Source: IBM Fellow, VP, WebSphere CTO – Jerry Cuomo
- 17. Use Case: Media and Entertainment Source: IBM Research
- 18. Use Case: Media and Entertainment Source: IBM Research