Social Media Club9/20/11<br />Social Media Analytics Webinar<br />Challenges and Solutions within Social Media Analytics<b...
Challenges to Getting it Right! <br />				CH 7 #smabook<br />
indentify the right firms and platforms (see chapter 3,7, 8, 10 of #smabook)<br />
4 types of platforms<br />
Self Serve<br />Hybrid<br />Internal<br />Full Service<br />
4 Levels of SMM Maturity<br />
First Level: Monitoring<br />
Second Level: <br />Online Research <br />
Third Level: <br />  Social Targeting &<br />    Data Management<br />
Fourth Level:<br />Social Business Collaboration <br />
What works Best?<br />Analyst being paired with internal marketing PR<br />
Hire people who know <br />Social Analytics well<br />Plus<br />+ <br />technical requirements <br />
Is all this work really worth <br />it?<br />Setting realistic time frames and <br />costs <br />for <br />Social Analytic...
What can we do with these platforms?<br />Brand Analysis  & <br />Brand Sentiment <br />International monitoring <br />Mar...
    Platform Limitations<br />
Query Length size <br />
Poor<br />Geo-location<br />capabilities<br />
Little ability to segment and sub-segment data <br />(covered in CH 7 and CH 8)<br />
Language<br />Little<br />Support<br />
Little  <br />Data Consistency <br />or interoperability <br />
Data is often bought from aggregators <br />For issues and limitations (read CH 10)<br />
Platforms are often unable to provide meaningful reports without a lot of extra manual work <br />
Future <br />Plays?<br />Read <br />CH 12<br />
http://www.smabook.com <br />Marshall Sponder<br />WebMetricsGuru  INC.<br />www.smabook.com<br />www.webmetricsguru.com<b...
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Social media club september 20th, 2011 - marshall sponder

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My presentation on Social Media Analytics to the Social Media Club, Internationally, as SMC Book of the month for September 2011

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  • Selecting the best platforms, processes and people are crucial to &quot;getting it right“ in Social Analytics; if you choose wrong, your projects will almost certainly fail partially, or entirely to deliver the desired results.
  • Self serve – Brandwatch, Radian6, Alterian SM2, etcFull service – Integrasco, Brandtology, Synthesio, Converseon, etc Hybrid – Additional Reporting included with Self Serve.Internal – Some companies build their own listening and analytics platforms for internal/private use.
  • Self serve – Brandwatch, Radian6, Alterian SM2, etcFull service – Integrasco, Brandtology, Synthesio, Converseon, etc Hybrid – Additional Reporting included with Self Serve.Internal – Some companies build their own listening and analytics platforms for internal/private use.
  • First Level: Monitoring - Listen to what customers are saying with mostly free tools (very labor intensive, struggle with volumes of data, use standard metrics such as likes, votes, retweets – usually not profitable or scalable efforts, little insight provided). [little investment with paid platforms yet]
  • Second Level: Online Research -Attempt to integrate social mediainto other aspects of their business, attempt to turn social data to insights through triangulating qualitative data (such as surveys) with online listening, often with very limited success. Ineffective semantic structuring of data and some attempts towards online engagement. [some investment with paid platforms, but often results are not productive]
  • Third Level: Social Targeting and Data Management- Integrate social CRM and analytics tracking, capture and measure leads effectively using social CRM. Set up business processes that tie in with measurement goals, such as social outreach and influencer identification. Target new and existing customers using listening systems and social CRM. [effective and productive results of investments in the right people, processes and technologies]
  • Fourth Level: Social Business Collaboration - Build a data cube (commonly called a data warehouse) and append social media data into existing CRM data. Surpass the first three maturity levels, following best practices to engage with customers, prospects, and influencers while measuring what is and is not working and changing what doesn’t work into what does. [use Analytics to forecast demand, use information to supercharge marketing and customer satisfaction]
  • Analyst being paired with internal marketing PR, perhaps embedding analysts into the internal marketing team of client companies
  • Hire people who know Social Analytics + the technical requirements, very well, are empowered by management, and can interface with external vendors effectively (this step is almost always usually missing, today)
  • Find out how long tasks and deliverables should really take? Is the time and deliverables properly documented, visualized, or have we left out crucial steps? What steps are actually involved? Do we know what they are?What is the realistic costs to do this work?How many people do we need to do it?Is all this work really worth it?
  • Brand Analysis / Brand Sentiment (yes – but often a lot of work)Sentiment Analysis by itself (difficult, people say the same things differently and also use the same terms to mean different things – context is important, but hard to capture using SMA).International monitoring – very difficult, costly (see CH 3)Influencer Identification – both easy and hard, depending on what you want to do – real influence is difficult to capture – (see CH 6)Market Research – Yes, but most people who use SMA tools do not understand how to conduct valid market research yet.
  • Query Length size (too small for effective work in B2B professional segmentations)
  • Poor Geo-location capabilities (most can not segment mentions beyond region, when they do, just offer a small sample, not reliable yet)
  • Little ability to segment and sub-segment data – must pull data for the way you need to use it (covered in CH 7 and CH 8
  • Language support is for mentions only, need to hire additional staff to read the mentions, report on them and act on them, which is fairly expensive to implement successfully. Only a few companies have even attempted to solve this problem (read CH 3).
  • Little Data Consistency or interoperability between platforms – each platform culls data and stores it in it’s own format, data from different vendors is often inconsistent.
  • There are no standards in this field yet.
  • Data is often bought from aggregators – pulled from an API or Federated Search, read from RSS feeds, or crawled directly – creating additional reporting issues and limitations (read CH 10).
  • The Platforms are often unable to provide meaningful reports without a lot of extra manual work – which is not properly budgeted for.
  • Social media club september 20th, 2011 - marshall sponder

    1. 1. Social Media Club9/20/11<br />Social Media Analytics Webinar<br />Challenges and Solutions within Social Media Analytics<br />Marshall Sponder <br />WebMetricsGuru.com<br />Smabook.com<br />@webmetricsguru<br />@smanalyticsbook<br />
    2. 2. Challenges to Getting it Right! <br /> CH 7 #smabook<br />
    3. 3. indentify the right firms and platforms (see chapter 3,7, 8, 10 of #smabook)<br />
    4. 4. 4 types of platforms<br />
    5. 5. Self Serve<br />Hybrid<br />Internal<br />Full Service<br />
    6. 6. 4 Levels of SMM Maturity<br />
    7. 7. First Level: Monitoring<br />
    8. 8. Second Level: <br />Online Research <br />
    9. 9. Third Level: <br /> Social Targeting &<br /> Data Management<br />
    10. 10. Fourth Level:<br />Social Business Collaboration <br />
    11. 11. What works Best?<br />Analyst being paired with internal marketing PR<br />
    12. 12. Hire people who know <br />Social Analytics well<br />Plus<br />+ <br />technical requirements <br />
    13. 13. Is all this work really worth <br />it?<br />Setting realistic time frames and <br />costs <br />for <br />Social Analytics<br />
    14. 14. What can we do with these platforms?<br />Brand Analysis & <br />Brand Sentiment <br />International monitoring <br />Market <br />Research <br />Influencer <br />Identification <br />Sentiment<br />Analysis<br />+ -<br />
    15. 15. Platform Limitations<br />
    16. 16. Query Length size <br />
    17. 17. Poor<br />Geo-location<br />capabilities<br />
    18. 18. Little ability to segment and sub-segment data <br />(covered in CH 7 and CH 8)<br />
    19. 19. Language<br />Little<br />Support<br />
    20. 20. Little <br />Data Consistency <br />or interoperability <br />
    21. 21.
    22. 22. Data is often bought from aggregators <br />For issues and limitations (read CH 10)<br />
    23. 23. Platforms are often unable to provide meaningful reports without a lot of extra manual work <br />
    24. 24. Future <br />Plays?<br />Read <br />CH 12<br />
    25. 25. http://www.smabook.com <br />Marshall Sponder<br />WebMetricsGuru INC.<br />www.smabook.com<br />www.webmetricsguru.com<br />now.seo@gmail.com<br />@webmetricsguru<br />@smanalyticsbook<br />WebMetricsGuru INC.<br />

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