The Fisheye Analytics media lens training deck


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A guide for users and customers of Fisheye Analytics.

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  • Comments: Software as a service – it will be important for investors… also important to use here cloud but for a PR and a Brand Manager… SAAS is most likely not adding anything. We should not claim governments if we have none. Btw… other might be discouraged to use us… maybe we supply a BAD government? We need a claim… WHAT DOES IT DO FOR ME… (see as well our whole discussion on sticky ideas). Radian6… Listen to the conversation… This is a nice claim, we should have one like: One tool for traditional and social media Understand what is said about you What is your Media image Understand how Journalists and your customers are building your image In terms of content… what does this platform do? worldwide – this is a competitive advantage… and some clients need it… thus a good value proposition make it easier (simple interface and one tool) …. State of the art reading platform.. Yes that is cool if we are… but what is it what the client needs EASE of USE… lets tell him this… ------------Other suggestion… (not sure, whether it is better – it is just a try for a dialogue) The world of media and media management has changed, daily are more online news, blogs, tweets, facebook etc. out. With Fisheye you can use those data for your business insights. Use one tool to understand what is said about you. Fisheye Analytics is a next generation media monitoring and measurement Platform. We combine social and traditional media.Our Services: Traditional and social media clippings – one tool for a simple usage Integrated analytics – a simple way to make sense out of the media data In-depth media research reports to investigate complex business issues-----We are working on this champagne line now way too long… lets just put the ones we had up in a ppt and lets ask our clients, friends etc. on what they think.----Last comment… we should not do picture overlays… pls. lets explain ruchi, that if she wants to create a view of a LOGO in the back, we should do this in the template and not on the slide.
  • I would say you have been talking to Ashwin quite a lot…  the “No – Log in required” …. It is great as we hope that people start using it… but this is not a value proposition.. It is a feature. A feature people do not even thing about. No one has so far complained about the log in.. They are just not using it… Thus lets not put this in a marketing presentation.I guess we should go with a brand name, but “Fisheye Reading Platform”? Rather then “Fisheye media lens” that makes at least as well our claim visible.Holistic platform… we came up with this claim since the Lexis Nexis integration… the word holistic however very often gets not real interest…How about “We marry traditional and Social Media” by offering one interface to access all articles?Analyze and filtering… again this is a technological detail… we are here to make your life easier in clipping. Thus… let state this. Also REACH and EMV… how is someone supposed to know what this is… I realized this as the editor of HBR reacted very aggressive against those terms… “we would need to explain them on a separate page”… right.. EMV is an algorithm we defined.. NO ONE in the world uses it. Let me give it a try:----------The dialy issue of any communication manager. The print clipping comes via email, your twitter account is analyzed via a web service. You follow on facebook several people and your intern is reading 77 google alerts daily. We will ease your life by marry all services into ONE Tool the Fisheye media lensE. EASY - All articles directly accessible FAST – Get the ghist of your media coverage by easy filtering using fisheye analytics SMART – monitor the crossover to see how your market message is spreading
  • We should do this as a clickable object… if there is internet it goes directly to the page of our SOCIAL MEDIA monitor….For this page, lets put out what is special about it…. Point to the features… our interface is not self explanatory.
  • Strongly feel that this explains it better than the homepage picture.We need to ensure SMI, Similarity and Influence are understood as 3 separate concepts.
  • Oh… good point… we should come up with facebook credibility…Hi Lyd… I guess this would have been a no go for many… NYT is twice as important as mashable… uhhh. Do not go to SM “gurus” with this statement. I also included tweets in the pic.I incl. as well Tweets… pls. come up with a low influential paper and a mid influential paper… tip it is not techcrunch…
  • Changed the color coding… RED POSITIVE sounded funny to me.Can you make the arrow etc. consistent with the color coding.The ANAPHORA algorithm identifies it, him, he, his etc… as well.. Thus pls include this funkyness into this slide.Good to have the explanation right here.We should however have a disclaimer on that page:Sentiment is only an index which works on large data sets. It does not work if the client messaging negative news which are not related to the client… UNICEF talking about the hunger in the world. Moreover, Sentiment has different correctness levels depending on the used language.-----There are two ways of judging sentiment. One is to manually read and code articles, one is to automatically code articles. The automatic coding is done in relation to keywords and concepts. Sentiment is computed relative to a pre-specified entity or "base word".  The coding works in two stages.1) The Fisheye Analytics Algorithm starts by looking only for sentences in a text which refer to this base word directly. Using anaphora resolution or, simply put, by seeking and mapping syntactic conventions, this algorithm identifies expressions relating to the objects and subjects of a text and associates these back to the pre-specified base word. Critical to the anaphora resolution is a 'Named Entity Recognition' program, which judges the nature of this base word to see for example whether it is a person, institution or other subject. When it has identified what exactly is being judged, the second stage of the analysis begins.2) Using the anaphora that have been found and contextualised, the programme then matches the text to relevant corpora (databases of text that share the same topic) which have been indexed by topic and pre-analysed by human coders. It then compares the text against these corpora. The Fisheye Analytics corpora are part of a proprietary database that houses pre-analysed texts on a wide range of topics such as: finance, politics, sports, education and general business. Those corpora will be "trained" to take on ever more complex reclassifications and interpretations using machine-learning techniques. Depending on the requirements, Fisheye Analytics will create new corpora to deal with different topics.The quality of the automated sentiment check is dependent on the integrity of grammatical or syntactical rules in the text. Thus the analysis is most accurate for grammatically-written articles in the English language. Any automated sentiment will be verified by a statistical check using human coding if requested. These statistical checks will in turn help to improve and refine the algorithm in all languages.
  • I would say combine 6,7,8 to one slide.
  • Not sure we need similarity in this context? No slide in the report deck indicates it?
  • Added similarityDid not follow your list as I felt that some items were not relevant (see those in bold below) and I wanted to have shorter explanations. Many things are self-explanatory.Would use pyramid of metricsMake sure we show that we have 21 metricsOverview of 21 Metrics in general Author - Who is the author? Source - Who is the source? Media Type - What kind of Media (TV, Print, Twitter, Blog, Facebook etc) is it? Sentiment – Anaphora Algorithm - specially designed to estimate sentiment towards a keyword within the text Importance – Is a source heard or unimportant? (isn’t this an amalgam of influence and SMI – how it is a metric on its own?)Reach – How many people will see a given article? Editorial Marketing Value – What is the equivalent monetary value of an article? Language – What is the language of the article? Geography – Where was the article published? Concepts – What are the main topics in the article? How is a MEME of an article spreading? Hype Index / Involvement Index – Predict the future of a given topic by observing trends in Twitter and blogsClicks from Sharing Sites - How often was this article "clicked on" coming from social media sharing sites? Comments on Articles - How many comments did this article get? Comments from Facebook on Articles - How often did someone comment on this article in facebook? "Likes" on Facebook - How many fans did "like" this article? Shares on Facebook - How often was the article distributed via Facebook? Re-Tweets of an Article - How often, did someone "re-tweet" the article Diggs on an article - How often "" someone this article? Google Buzz on an Article - How often, did someone "re-buzzed" the article Reddit on an Article - How often did the article get a "thump up" or "down"? Digital Fingerprint of any article to see social impact - How often were parts of this article copied? (the metrics you named above form SMI this SMI is not a metric on its own)
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