Winter park social analytics bootcamp workshop marshall sponder - webmetricsguru inc final

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This presentation will be given at the first Social Analytics Forum in WinterPark Florida on June 5th and 6th, right across the street from Rollins University.

https://www.eventbrite.com/e/social-analytics-forum-tickets-11521578351

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Winter park social analytics bootcamp workshop marshall sponder - webmetricsguru inc final

  1. 1. WinterPark Social Analytic Boot camp Marshall Sponder WebMetricsGuru Inc. June 5th & June 6th 2014
  2. 2. Teach: Rutgers University: http://www.masongross.rutgers.edu/content/marshall Baruch College : http://www.baruch.cuny.edu/directory/detail.jsp?oid=204506620 FIT: https://epay.fitnyc.edu/C20737_ustores/web/product_detail.jsp?PRODUCTID=3706&SIN GLESTORE=true Write: CMSWire: http://www.cmswire.com/author/marshall- sponder/ ClickZ: http://www.clickz.com/author/profile/2854/marshall- sponder Blog: http://webmetricsguru.com/ Book: http://www.amazon.com/S ocial-Media-Analytics- Effective- Interpreting/dp/007182449 9/ref=reader_auth_dp
  3. 3. Brands / Former Clients / Coworkers IT/Unix SEO /Web Analytics CORPORATE MKG /PR ANALYTICS
  4. 4. Learning about Measurement & Insights that we can derive from Social & Mobile Analytics
  5. 5. We spend almost all our waking hours creating and consuming Digital Data, and we must now learn to understand this data using Analytics
  6. 6. What Is Data?
  7. 7. 1950’s 1960’s 1970’s 1980’s 1990’s 2000-2010 2011-2020 Unstructured Data is much harder to work with – more than 75% of the web is made up of unstructured data
  8. 8. Different Data ends up with different tools and analytics platforms Log based Web Analytics, FeedBurner Various Widget Analytics Platforms Various Chat Room Analytics Various Message Board Analytics Log based Web Analytics, FeedBurner Various Video Analytics Platforms Various Photo Sharing Analytics Platforms Twitter Analytics and Awe.sm Web Analytics, Google Analytics Mobile – Flurry, MixPanel, Adobe SC Social Medias
  9. 9. Analytics informs Many things Care of Gary Angel – E&Y
  10. 10. Finding the right tools for the Job
  11. 11. Tip of the Iceberg: There are many platform tools
  12. 12. Which Step to take Next?
  13. 13. Social Analytics Landscape is constantly shifting …. understand your business needs before choosing http://www.trustradius.com/downloads/sm mslandscape.jpg
  14. 14. Consumer Research NEED - Rich Categorization NEED - Machine learning NEED - Social Media Coverage Adapted from Gary Angel – E&Y • What do Key Influencers Think? • How have we shaped their Message? • Can we talk to them directly?
  15. 15. Consumer Research Platforms
  16. 16. PR Monitoring & Support NEED -Traditional Media Coverage NEED -Influencer Identification NEED - Topic Categorization Adapted from Gary Angel – E&Y • How do our customers perceive us via Social Media? • What are our brand strengths from a customer perspective? • What drives consumer choice towards our products / services?
  17. 17. PR – Monitoring and Influencer Scoring
  18. 18. Social Engagement NEED – Workflow Solution NEED - Operational Metrics NEED - Low-Latency Adapted from Gary Angel – E&Y • Are our customers engaged? • Have we sold products? • Did we drive Higher Awareness and Site Traffic?
  19. 19. Some Social Engagement Platforms
  20. 20. Social Analytics can’t answer all these questions need a “Converged” Approach to Data
  21. 21. The Social Analytics tools tend to work better for one or two things, but not so well for everything you might want to do with them.
  22. 22. Digital Story Telling requires Convergence • Site-side/app analytics • E-mail analytics What happened? • Audience MeasurementWho? • Voice of Customer • Customer Experience Replay Did visitors find what they were looking for? • Social Media Listening/Analytics What are Customers saying about your Brand? • Attribution/Ad Serving • SEM/Search Tags/Floodlight Did digital advertising help drive business? • E-commerce/Affiliate MarketingAre partners impacting business? • A/B and Multi-variate TestingHow else can we improve conversions? 23
  23. 23. Converged media is similar to converged analytics, and is multi- channeled approach http://www.titan-seo.com/NewsArticles/trifecta.html
  24. 24. Ellen’s Selfie was purported to be worth 1 Billion dollars of “earned media” to Samsung, an Academy Awards 2014 Sponsor. By taking a “converged” approach the Selfie became more potent http://www.odwyerpr.com/sto ry/public/2220/2014-04- 09/samsung-won-1b-earned- media-from-ellens-selfie.html
  25. 25. The more channels your business critically operates in, the more your analytics needs to “Converge”
  26. 26. Reporting tools differ based on Job Role and language constructs
  27. 27. Lost in the Sea of Data!
  28. 28. Studies Suggests Tool evolution and use evolved with Language Development http://www.wired.com/wiredscience/2013/09/tools-and-language/ The way we talk and describe our business goals ends up determining the kinds of tools we want to work with and find useful
  29. 29. Different business terminologies created to make specialized, industry vertical tasks easier to do are the biggest obstacle to a “holistic solution”, as each lexicon ends up needing its own set of tools – often these are largely redundant.
  30. 30. Exercise: Drop your issue, brand, organization name in the center and pick out the orbiting topics.
  31. 31. Example: Triple A (AAA) – try to understand what your business is about – then maybe, we can figure out what tools/platforms are needed using social analytics and other analytics platforms, to better capture the data and act on it.
  32. 32. Online Surveys Journals/ Reviews Social Listening Geo- Social data - Mapping
  33. 33. Organizational level of Maturity with Social Analytics
  34. 34. First Level: Monitoring Radian6 / Sysomos good examples of “monitoring Function” in SMM.
  35. 35. Example of monitoring – River of News Manual, labor intensive, subjective, not scalable, mostly a “news feed”.
  36. 36. Second Level: Online Research
  37. 37. Example of Market Research OS Style Use Hardware Connect Media Price Integration Form Factor Speed Screen Size Apps Screen Res. Gameplay Reliability -1 -0.5 0 0.5 1 0 500 1,000 1,500 2,000 Care of Gary Angel – E&Y
  38. 38. Third Level: Social Targeting
  39. 39. Example of Social Targeting Care of The New York Times
  40. 40. Programmatic is the future of Paid Advertising
  41. 41. Madison Avenue is becoming more like Wall Street http://venturebeat.com/2014/04/20/why-madison-avenue-is-becoming-more-like-wall-street/
  42. 42. Walmart's New Cost-Cutting Target: The Ad World Media Veteran Monahan Wants Giant Retailer To Cut Waste for Suppliers Too "Media planning today is beyond human comprehension," Mr. Monahan said. "There are so many choices on where you can put your precious investment. It's a software problem." http://adage.com/article/special-report-digital-conference/walmart-s-cost-cutting-target-advertising- world/292436/
  43. 43. Fourth Level- Collaboration
  44. 44. Example of Collaboration / Social CRM
  45. 45. Actualizing Data is where the best returns (ROI) is
  46. 46. Some industries can benefit from social Data more than others
  47. 47. Platform Challenges
  48. 48. Inadequate query length size limits
  49. 49. Brandwatch max char = 4096 Radian6 max char = 972 Sysomos max char = 2040 Most of the queries that people want to write end up being to long to run, and even if they could run, would take forever to execute. They also don’t necessary run the query in the most effective order, nor is there any way to control that.
  50. 50. http://www.youtube.com /watch?v=4Y-SVxnVOv8 "housing solution"~2 AND "rhode island" AND "foreclosure", "road home program"~3 AND "foreclosure", "home loan modification"~4 AND "foreclosure", "jobless rate"~3 AND "foreclosure", "bankrupcy" AND "foreclosure" AND "housing" AND "obama", "rhode island housing"~3 AND "forclosure", "foreclosure prevention funds"~5, "bank foreclosures rhode island"~4 AND "obama", "selling house"~4 AND "foreclosure" AND "obama", "hardest hit fund"~4, "national foreclosure mitigation"~6, "homeowner stability initiative"~5 AND "obama", "roadhome program"~2, "hud homes rhode island"~3 AND "obama", "foreclosure settlement"~4 AND "25 billion"~2 AND "obama", "fannie mae freddie mac"~10 AND "foreclosure", "keeping people in their homes"~4 Radian6 Query on Foreclosures in Rhode Island Hard to write a query that gets you the results you want
  51. 51. Limited options to sub segment Social data
  52. 52. Segmentation of data and audience is usually a complex, time consuming task that needs curation
  53. 53. Boolean Queries can’t capture most online conversations we ultimately want to track
  54. 54. The vast majority of interesting online conversation is very hard to isolate with Boolean logic, because conversations are complex, each person uses language that is slightly differently, making it much harder to write an effective query.
  55. 55. Geo-location almost useless (but changing with Geofeedia/iBeacon)
  56. 56. A fraction of conversations in social media are somewhat accurate to the country level, and some times to the city level, but rarely better than this, but that is changing for a few platforms such as iBeacon based and Geofeedia, as part of a new breed.
  57. 57. Geofeedia and iBeacon technologies fix the Local Data Gap
  58. 58. Even our notion of what a location is, changes by time of day and day of the week (device/network driven) 12 PM 7 PM 11 PM Source: Foursquare
  59. 59. Geofeedia and iBeacon technologies fix the Local Data Gap
  60. 60. What iBeacon look like – an iBeacon
  61. 61. Geofeedia and iBeacon technologies fix the Local Data Gap
  62. 62. What Retail Shopping is becoming There are few analytics platforms that are available as yet for these new shopping technologies, Adobe Site Catalyst just came up with one of them.
  63. 63. Language Support Issues
  64. 64. Interoperability issues
  65. 65. Who is winning the Cloud Wars? Which Cloud platform integrates its applications the best? http://www.thehubcomms.com/who- is-winning-the-marketing-cloud- wars/article/336854/
  66. 66. Immature Frameworks
  67. 67. Connect the dots into a workable Framework to get the most value from Data http://metricsman.wordpress.com/2013/06/12/a-new-framework-for-social-media-metrics-and-measurement/
  68. 68. Digital Marketing Framework 73
  69. 69. Further considerations are the “non-linear” ways we come to make a decision about purchasing a product or service
  70. 70. DEVICES MATTER TOO! Source: comScore Device Essentials, Monday 21st January 2013
  71. 71. http://www.therainman.in /Cross_Media_Attributio n.html
  72. 72. Few true Independent Aggregators of Social Data
  73. 73. Analytics is best used for planning and reporting, to optimize your marketing efforts
  74. 74. Example Optimization Metrics Plan
  75. 75. Typical Business Objectives 1. Increase Market Share by X% (this/next Y/Q) 2. Increase Business Income/Profit by X% (this/next Y/Q) 3. Save X% of our (monthly/quarterly) spent (this/next Y/Q) 4. Improve Productivity by X% (this/next Y/Q) 5. Improve Services provided by X% (this/next Y/Q) 6. Increase Company Profit by X% (this/next Y/Q) 7. Improve Team Productivity by X% (this/next Y/Q) 8. Improve/increase worker productivity by X% (this/next Y/Q) 9. Open New Offices (#) (this/next Y/Q) 10.Find/Create New Business Opportunities (#) (this/next Y/Q) 11.Increase Sales Bookings by (#) (this/next Y/Q) 12.What are the sales drivers & Incentives (this/next Y/Q) 13.What are your sales drivers & Incentives? (this/next Y/Q) 14.How and where to do we generate leads (#/%)? (this/next Y/Q) Business Goal Target
  76. 76. Determine what drives your business and ways to measure it • What Measures do you want to track? • What Decisions would you make differently if the one of the measures was surprisingly high or low? • What is the threshold of the measure? In other words, at what point if the value was exceeded or dipped would an alternative action take place? Source: Douglas Hubbard - The Pulse
  77. 77. Once you are able to collect the right data, then you organize it and figure out its meanings
  78. 78. Business Metrics are much more useful than channel and program metrics (micro conversions) but are harder to formulate and customize http://metricsman.wordpress.com/2013/06/12/a-new-framework-for-social-media-metrics-and-measurement/
  79. 79. Determining Platform Investment TimeSpent–DataCleaning Previous Business Investment/ Size CustomizationRequired
  80. 80. 1950’s 1960’s 1970’s 1980’s 1990’s 2000-2010 2011-2020 Big Data is causing profound changes Structured Unstructured
  81. 81. Hybrid UV Data Web Intelligence Unified Information Social Data CRM POS email Search Offline WOM Intelligence is understanding how to collect information and what to do with it Survey
  82. 82. Twitter explains how Chris Hadfield went viral
  83. 83. Leveraging Viral Data in Social Media Analytics
  84. 84. Facegroup » Twitter Video Virality - Chris Hadfield Understanding how social media spreads has become a major interest as well as the subject of a new course @Rutgers
  85. 85. 35% of adults who post videos online (11% of all adult internet users) hope to see their video go viral. - Pew Report - Pew Internet http://www.reelseo.com/online-video-uploads-double-years-35-hope-viral-report/
  86. 86. Emotions are at the root of Viral Marketing - we do not share what we are not feeling strongly about"
  87. 87. Two reasons why people share and spread the word - psychological and Social motivations
  88. 88. VIDEOS WHICH PROVOKE A STRONG POSITIVE RESPONSE ARE 30% MORE LIKELY TO BE SHARED Positive High Arousal Low Arousal Hilarity Amusement Inspiration Calmness Astonishment Surprise Exhilaration Happiness Negative High Arousal Low Arousal Disgust Discomfort Sadness Boredom Shock Irritation Anger Frustration Shock or anger can drive shares but they are a risky bet Source: Karen Nelson-Field - Viral Marketing, The Science of Sharing, Table 3-2, page 24
  89. 89. Emotive Tracking for VIRAL Campaigns using Text Analytics
  90. 90. Telekinetic Coffee Shop Surprise #1 on Unruly Viral Video Chart as of 10/13/13 http://viralvideochart.unrulymedia.com/youtube?id=VlOxlSOr3_M The video was shared 1:21 times it was viewed
  91. 91. Text Analytics identifies the main actor and action most of the time
  92. 92. Encoding Emotions Opens up intriguing possibilities ...
  93. 93. Rutgers University & Unruly partnerto create a course about the details around Creating Viral Media (using Big Data) Launching early 2015
  94. 94. Workshop Exercises 1. Analyzing CongresoSM using Social Media Analytics – pick 3 metrics to track Metric 1 Metric 2 Metric 3 Based on the Metrics you choose – what do you think your measurement goal is? What platforms are best to use to track the conference based on the goal (s) expressed? Any special considerations that need to be taken into account with this situation? How long would you track the conference (days/weeks months before/after)?
  95. 95. Workshop Exercises 2. What would be a business metric for Exposure? Engagement? http://metricsman.wordpress.com/2013/06/12/a-new-framework-for-social-media-metrics-and-measurement/ ? ?
  96. 96. Workshop Exercises 3. How do you determine content has gone viral? Viral Metric 1 Viral Metric 2 Viral Metric 3
  97. 97. Common Mistakes •Failing to understand your business needs for digital tracking •Treating Social Analytics as an “after thought” •Failure to incorporate adequate time for ideation and testing •Making assumptions and underestimating the efficacy of analytics to improve your business. 102
  98. 98. Thank You! Marshall Sponder CEO, WebMetricsGuru INC www.webmetricsguru.com Baruch College, Rutgers University, FIT @WebMetricsGuru Email Me: now.seo@gmail.com Facebook.com - SocialMediaForTheArts

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