[Slides] Social Data Intelligence Webinar, By Susan Etlinger

1. Social Data IntelligenceAn Altimeter Group Webinar Susan Etlinger, Industry Analyst September 5, 2013

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

3. The State of Social Analytics

4. Social data is not an island 4

5. It is used across the organization

6. Organizations want context

7. Source: Altimeter Group It has a large and diffuse ecosystem 7

8. Social data turned up the heat for Manny’s Steakhouse, prompting action 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. 8

9. 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 Parasole and Manny’s quickly identified 6 suspect samples, lined them up, tasted them, and immediately discovered the problem. 9

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. Challenges of integrating social data

12. Characteristics to consider

13. Making Social Data Actionable

14. 1. Identify your business goals

15. 2. Define core social media metrics

16. 3. Prioritize Your Metrics 16

17. Prioritization Process • List the core set of metrics you would like to evaluate • Score them as follows, on a scale of 1-5, where 1 is the lowest, and 5 is the highest 17

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 • Marketing • Customer Support • Engineering • PR • Product Management • Legal 18

19. Results across the enterprise Customer Experience Numerous support cases resolved Converted many ‘ranters’ to ‘ravers’ Risk Mitigation Uncovered hundreds of fraudulent product pilots Lead Generation & Nurturing Generated hundreds of business & consumer leads Product Improvement Rapidly identifies key areas to prioritize R&D

20. Building A Data-Driven Organization

21. Aspire to a (more) holistic strategy 21

22. Define what you’ll do and what you won’t do. 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. Inventory Documented methodology Documented success criteria

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? SCOPE What success looks like 23

24. Demonstrate the connection to the outcomes the C-Suite cares about. Strategy: The extent to which social data — and metrics — is in alignment with strategic business objectives across the organization. Brand reputation, revenue generation, operational savings, customer satisfaction, etc.

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. Learn what “normal” looks like. 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. How social data changes over TIME Look at existing metrics Consider the competition– but not too much Multiple outliers gain significance

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. Identify the areas where you have inadequate processes or policies. Governance: The extent to which the organization has developed, socialized, and formalized processes related to workflow, collaboration, and data sharing. Data sharing Executive support

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. Define, contextualize, and prioritize core metrics. 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 Ability to articulate all criteria and process by which metric is evaluated Benchmarks & KPIs: decision-making vs. performance

31. 5. METRICS What success looks like The keys to metrics maturity: Definition Prioritization 31

32. Know thy social data, platforms, and roadmap. Data: A strategic approach to the data and platforms at your disposal Understand social action vs. social text Warehouse social data Know your platforms (capabilities, limitations, TOS, APIs, etc.)

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. 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. Aggregate, then analyze “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 Caesar’s is undergoing a mass integration project, aggregating data across offline and online advertising channels, such as display, email, organic, search, and affiliate.

36. The goal: understand the customer journey Gaining insights Aggregating behavioral preference data informs more efficient, strategic, and timely investments, at customer and organizational level Building preference models Using previous purchase data + engagement history (online and offline) Driving loyalty Tying pre-purchase + rewards data Online + offline behavior earns customers points towards rooms, shows, discounts, etc. 36

37. Final Thoughts

38. Implications and Trends • View from the customer in, not the organization out • Holistic view of customer drives ‘real-time’ and ‘right-time’ engagement • Social data is “big data” • Embracing volumes, variety, and velocity of social data will help prepare organizations for other data streams to come • Big data disrupts organizations • Consider the HiPPO phenomenon and democratization of decision-making based on data (vs. intuition) • The real-time enterprise is getting more real • Demand for data at the point of action 38

39. "Everything should be made as simple as possible, but not simpler." − Albert Einstein 39

40. THANK YOU Susan Etlinger susan@altimetergroup.com susanetlinger.com Twitter: setlinger 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.