1. 01/10/2010 Extracting meaning from Social Media Monitoring ….OR

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3. Is they often present you with this... 01/10/2010 3

4. Understanding the meaning of results and how to use monitoring tools so they work for you Filtering the information Using a top down approach to filtering often wastes time, money and effort Try a bottom up approach to define filters - it’s quicker and it will help understand the nature of the social media and how your brand interacts with it better - particularly useful when you need to ‘expand’ the search Use as many specifics as you can ( that may be none, but usually isn’t) Be prepared to spend some time planning how you will use filters to categorize and quantify data (every hour you spend at the planning stage can save you days in execution - and can be especially valuable when you are briefing vendors) Make sure the filters make sense to your business and monitoring objectives 01/10/2010 4

5. 01/10/2010 5 The perfect tool?

6. How much do you already know about your audience? Take the time to establish which social media channels it makes sense for your audience to use Better still, choose a tool which allows you to check your assumptions Experiment and test your assumptions before you go setting up a whole lot of details in specifications and filtering Use ‘exceptions’ to exclude unwanted audiences Make sure you understand the vocabulary your audiences use 01/10/2010 6

7. 01/10/2010 7 Data Cleanup The problem with social media is that finding 20,000 ‘hits’ is a lot easier than finding the 50 that matter Filters and exclusions can screen out obvious mismatches but can also cause you to ‘miss’ important items A successful strategy for cleaning data without losing integrity should include: Contextual matches rather than keywords Non-linguistic cues and vocabulary Links and associations (especially where you can set limits/minimum values) Use of active terms Use of specialized tags and terms to allow easier identification of who is responding to your brands content Incorporation of SEO terms used in materials designed for audiences Links outside social media groups for source and destination tagging

8. Customizing the sample The first rule of data sampling should be: design backwards from the intended result/audience for your results” Establish what you are going to do with results Who are the ultimate recipients Do results have be consistent with other business functions’ results Don’t generate information no one can use ( i.e. if you are monitoring in real time - make sure there are real time processes which uses these results Question outside your own immediate needs – are you monitoring for research, evaluation, discovery, actionable insight, immediate response? Make sure your ‘cleaned data’ can be transformed into all the outlets and reports you require 01/10/2010 8

9. A matter of Perspective Perspectives are a quick and easy way of overcoming one of the most persistent problems with analyzers - making the answer relevant to you. In data cleanup they can be used to eliminate a lot of irrelevant content In research they can be used to shift the view between , say, competing brands or topics Most analysers use people to add the perspective.... ...but there is an alternative Automated perspectives can be specified as part of the filtering and cleanup process ‘stored perspectives’ allow a rapid comparison of data from different viewpoints which multiplies the business/media intelligence in the sample Anything automated can be switched on or off and can be modified Consistency in results will go up 01/10/2010 9

10. Context Most sentiment analysers do not allow or imply context in their results which is why the results often don’t look right Context uses perspectives and filters to provide a means of matching the results to the brand or person relating to the monitoring objective Put simply - the results should make sense Context also allows the focus of monitoring to reflect actual process in a business e.g. A ‘buy’ decision or an expression of discontent about a service The role of context is to make ‘You’ the focus of the answer 01/10/2010 10

11. Framing Questions The most important rule is to make sure the answers are answering YOUR questions Either make sure you test ‘logical’ questions ahead of time or...... 01/10/2010 11

12. An end to Keywords? Look at the new generation of analyzers which combine natural language and context to generate results which ‘mean’ what you ‘said’ in your question The benefit is that missed ‘hits’ are minimised simply because your vocabulary missed a word or variation, or the time tense is different, or one or more of the key elements isn’t language (such as an emoticon, or slang expressions This approach allows you to work in a more natural way in both framing questions and evaluating outcomes 01/10/2010 12

13. The trouble with automated sentiment analysis Is often what it is expected to accomplish If you have designed in the filtering, contextual mapping and the end user of the information it is perfectly practical to expect a result in the 90-95% accuracy range Beware of claims over 95% (in a ‘live’ language, subject to fashion and the re-use of terms in new ways there are very real and tangible barriers But don’t make the mistake of assuming a human reader can do any better - a 2009 study of 2,000 people saw them score an average of 84% - and 88% was the top mark There is a ‘semantic’ trap’ in most analysers in that they use language as it is defined - not as it is actually used - and most rely on words (social media posters sometimes don’t) - a lot of language isn’t traditional linguistics! Black box solutions, however good they appear, are hiding things from you - insist on transparency 01/10/2010 13

14. Death to all humans? 01/10/2010 14

15. Why automated analysis is NOT about replacing people The role of automation is not about replacing people from the process – rather is should be about allowing you more time to think about what is important rather than speed reading Take the Apple iPhone 4 - social media DISCUSSION THREADS not posts were ruining at 30/second during the launch day - leaving anyone trying to read and make sense of the sentiment in a passive, or reactive state. This is an extreme, but most social media sites can easily overwhelm a human-based system allowing people to do little more than skim content Context- based analysis can reduce the results to actions and insights which are both manageable and insightful - without increasing the errors from volume - without getting tired and it will read every blog or post all the way through. 01/10/2010 15

16. Making results count AVE? ROI? If you are going to be subject to externally generated measures (often for historical or consistency reasons) make sure you can measure something meaningful for the purpose Better still develop a metric which actually makes sense for what you are doing and communicate its benefits Make sure your results are not siloed to just social media - there are often links to other media forms (including your own materials) – make sure your solution integrates them all and can show you these relationships Dashboards: Employ ways of making your results and their context stand out – dashboards are both an excellent way of compressing complex information and adding impact to results (but too often are generalized templates presented by vendors – make sure you get what you want – how you want it) Can be used to extend your information into other parts of the business Can create synergy with other business processes - make sure your social media dashboard can take on information from other functions Don’t misuse dashboards to create meaningless measurements or introduce concepts which make no sense to anyone other than the graphics/metrics person who designed it - stick to what you know and can understand 01/10/2010 16

17. And finally.... Feedback loops – both in the human and automated sense are a valuable mechanism to ‘shrink wrap’ results ever closer to you brand objectives - use them! 01/10/2010 17