Social Data Intelligence: Webinar with Susan Etlinger

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This webinar covers the findings from the Altimeter Group report, Social Data Intelligence, which lays out the imperative for organizations to integrate social data with other data streams in the enterprise. Includes best practices and frameworks, as well as a maturity map to enable organizations to make the best and most strategic use of social data.

Published in: Data & Analytics

Social Data Intelligence: Webinar with Susan Etlinger

  1. 1. Social Data Intelligence An Altimeter Group Webinar Susan Etlinger, Industry Analyst September 5, 2013
  2. 2. Agenda I. The State of Social Analytics II. Making Social Data Actionable III. Building A Data-Driven Organization IV. Six Dimensions of Analytics Maturity V. What’s Next 2
  3. 3. The State of Social Analytics
  4. 4. Social data is not an island 4
  5. 5. It is used across the organization
  6. 6. Organizations want context
  7. 7. Source: Altimeter Group It has a large and diffuse ecosystem 7 Publishers (Social Networks, Community, Enterprise Collaboration) Social Data Platforms Social Applications Listening/Moni toring Engagement SMMS) Publishing Analytics Enterprise Applications CRM BI Market Research Email Marketing Fraud Detection/Risk Mgt Supply Chain
  8. 8. Manny’s steakhouse is celebrated for its quality steaks, but when a sudden change in sentiment related to its meat quality surfaced via social media, the company was able to pinpoint the precise dates, times, and incidents of faulty product. Social data turned up the heat for Manny’s Steakhouse, prompting action 8
  9. 9. Parasole and Manny’s quickly identified 6 suspect samples, lined them up, tasted them, and immediately discovered the problem. Parasole uses social data opportunistically, to protect product (and brand) quality Using social data to optimize supply  Cut ties with the meat supplier  Provided employee training to smooth the transition  Updated employee incentive programs to incorporate social ratings and reviews 9
  10. 10. So…what is social data intelligence? Social data intelligence is insight derived from social data that organizations can use confidently, at scale and in conjunction with other data sources to make strategic decisions.
  11. 11. Challenges of integrating social data Multiple internal constituents and interests • Community managers & customer service • Marketing and digital • Risk, compliance, legal, HR • Market research Requires new analytical approaches • It’s big data! • Variety • Velocity • Volume Social Data (and sometimes analysts) lack enterprise credibility • Social data is new • It lacks standards • Analyst roles are new
  12. 12. Characteristics to consider
  13. 13. Making Social Data Actionable
  14. 14. 1. Identify your business goals
  15. 15. 2. Define core social media metrics Business Goal Social Media Metric Brand Health Brand sentiment over time Marketing Optimization Impact of campaign X on awareness Revenue Generation Impact of social media on conversion Operational Efficiency Impact of social media on call deflection Customer Experience Impact of social media on NPS Innovation Impact of social media on speed to market
  16. 16. 3. Prioritize Your Metrics 16
  17. 17. Prioritization Process 1. List the core set of metrics you would like to evaluate 2. Score them as follows, on a scale of 1-5, where 1 is the lowest, and 5 is the highest • How useful this metric is to your organizationValue • Your organization’s ability to deliver this metricCapability • The time and staff power it will take to deliver this metric Resource • The degree to which other metrics or future decisions rely on this metric Dependency 17
  18. 18. Symantec has operationalized social data Symantec harvests social data from across the web. They route data to the central social business team, where they determine the business function best equipped to serve the customer. They classify Actionable Internet Mentions (AIMs) into seven categories comprising different business functions. The seven classifications are: 1. Case: Request for help resolving real-time issue 2. Query: Question that doesn’t require support resource 3. Rant: Criticism that merits brand management consideration 4. Rave: Praise from Symantec brand advocate 5. Lead: Pronouncement of near-term purchase decision 6. RFE: Request to enhance a product with a new feature 7. Fraud: Communication from an unauthorized provider of Symantec products 18 • Marketing • Customer Support • Engineering • PR • Product Management • Legal
  19. 19. Results across the enterprise Customer Experience Numerous support cases resolved Converted many ‘ranters’ to ‘ravers’ Product Improvement Rapidly identifies key areas to prioritize R&D Lead Generation & Nurturing Generated hundreds of business & consumer leads Risk Mitigation Uncovered hundreds of fraudulent product pilots
  20. 20. Building A Data-Driven Organization
  21. 21. Aspire to a (more) holistic strategy 21
  22. 22. Scope: The number of internal groups that work with social data and the scope of data to be measured: which platforms, which data points, and why. Define what you’ll do and what you won’t do. Inventory Documented methodology Documented success criteria
  23. 23. Mastery means you can easily answer questions such as: • What social data do we have at our disposal? • What do we track? What is our methodology for social data? • What are the critical success factors to scale this across the organization? 1. SCOPE What success looks like 23
  24. 24. Strategy: The extent to which social data — and metrics — is in alignment with strategic business objectives across the organization. Demonstrate the connection to the outcomes the C-Suite cares about. Brand reputation, revenue generation, operational savings, customer satisfaction, etc.
  25. 25. Maturity means every social media initiative — however small or short-term — has a clear set of goals and metrics that define success. 2. STRATEGY What success looks like 25
  26. 26. Context: The extent to which the organization is able to view social data in various contexts to understand what is typical, what is unusual, and the drivers for each. Learn what “normal” looks like. How social data changes over TIME Multiple outliers gain significance Look at existing metrics Consider the competition– but not too much
  27. 27. 3. CONTEXT What success looks like The top maturity marker is the existence of clear benchmarks against: • Past history • Enterprise signals • The competition 27
  28. 28. Governance: The extent to which the organization has developed, socialized, and formalized processes related to workflow, collaboration, and data sharing. Identify the areas where you have inadequate processes or policies. Data sharing Executive support
  29. 29. 4. GOVERNANCE What success looks like Governance maturity means that: • Social data measurement processes are documented, socialized, and understood company-wide • Workflows are clear, automated, and scalable • Approach in context of organization’s cultural norms 29
  30. 30. Image by coreburn used with Attribution as directed by Creative Commons http://www.flickr.com/photos/coreburn/487357814 Metrics: The extent to which metrics have been defined and socialized throughout the business Define, contextualize, and prioritize core metrics. Ability to articulate all criteria and process by which metric is evaluated Benchmarks & KPIs: decision- making vs. performance
  31. 31. 5. METRICS What success looks like The keys to metrics maturity: • Definition • Prioritization 31
  32. 32. Data: A strategic approach to the data and platforms at your disposal Know thy social data, platforms, and roadmap. Understand social action vs. social text Know your platforms (capabilities, limitations, TOS, APIs, etc.) Warehouse social data
  33. 33. 6. DATA What success looks like Maturity in the data dimension requires: • Understanding of data types, sources, context, influence • Resources who understand and make best use of platforms, and conform to their terms of service • Approach to integrating social data into other business critical data streams, big and small 33
  34. 34. Caesar’s to integrate social data across 50+ casinos, hotels, and golf courses worldwide Across a vast empire of brands and locations, Caesar’s realizes the value of its data lies in its ability to inform the customer journey across channels and touchpoints.
  35. 35. Aggregate, then analyze Caesar’s is undergoing a mass integration project, aggregating data across offline and online advertising channels, such as display, email, organic, search, and affiliate. “The goal is to understand both online and offline touchpoints along the customer journey and how they vary across segments, media types, and brands.” –Chris Kahle, Manager of Web Analytics, Caesar’s
  36. 36. The goal: understand the customer journey Building preference models Using previous purchase data + engagement history (online and offline) Gaining insights Aggregating behavioral preference data informs more efficient, strategic, and timely investments, at customer and organizational level Driving loyalty Tying pre-purchase + rewards data Online + offline behavior earns customers points towards rooms, shows, discounts, etc.36
  37. 37. Final Thoughts
  38. 38. Implications and Trends 1. View from the customer in, not the organization out • Holistic view of customer drives ‘real-time’ and ‘right-time’ engagement 2. Social data is “big data” • Embracing volumes, variety, and velocity of social data will help prepare organizations for other data streams to come 3. Big data disrupts organizations • Consider the HiPPO phenomenon and democratization of decision- making based on data (vs. intuition) 4. The real-time enterprise is getting more real • Demand for data at the point of action 38
  39. 39. "Everything should be made as simple as possible, but not simpler." − Albert Einstein 39
  40. 40. Susan Etlinger susan@altimetergroup.com susanetlinger.com Twitter: setlinger THANK YOU Disclaimer: Although the information and data used in this report have been produced and processed from sources believed to be reliable, no warranty expressed or implied is made regarding the completeness, accuracy, adequacy or use of the information. The authors and contributors of the information and data shall have no liability for errors or omissions contained herein or for interpretations thereof. Reference herein to any specific product or vendor by trade name, trademark or otherwise does not constitute or imply its endorsement, recommendation or favoring by the authors or contributors and shall not be used for advertising or product endorsement purposes. The opinions expressed herein are subject to change without notice.

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