My Digital FootPrint

Tony Fish @My Digital Footprint When we were chatting about a collaborative digital business model, my nephew Chris challenged me with this statement “ I can't understand what the consumer gets for helping others though! ” I had just shared with him a business that raised $10m which allows people to help people for free.  I was rather hoping that his digital youthfulness was about to surprise me and provide me with a deep native understanding of being-digital and offer me some insights to the future; but his mind has already been corrupted by out of date, but classical business/ marketing, education. As we continued to debate digital first as I structured out an update to classical marketing, as is set out below. There is an awful lot written and taught about the traditional 1960’s marketing mix tool which is commonly known as the 4, 5, 6, 7 and even 8 P’s of marketing. A quick read of Wikipedia gives all the basics.  A re-write that never really caught on in the 1990...

Image source The current estimates are that there are over 500 tools that will listen, monitor, track or analyse your business, product, service, brand, PR, reach, influence or customer digital interactions and deliver a dashboard of “data analysis”. The question is now not should we do it or what to listen for but how to read the analysis and decipher what customers are saying or trying to tell you; but not going as far an assuming customer know what they want!? Hence the interest in “sentiment analysis” which aims to give better output analysis delivering better marketing, detection of opportunities and threats, protection of reputation and brand, and maintain or improve margin. We know that analyzing natural language is difficult (even without accents) however sarcasm and other forms of derisive language adds additional complexity and we cannot assume that customer statements are true, as we know that  context has a great bearing on meaning . Given I...

When Big Data says "Happy Christmas", what is the sentiment? I always say "Happy Christmas," however, this year as I write my chosen Christmas messages, I am forced to consider what someone else's algorithm will imply about me, based on my use of digital words. I want to explore in this ViewPoint, through the use of a "Happy Christmas" message, the level of TRUST already granted to something we cannot touch in a digital world. Scene setting - Trust and Sentiment Let's consider the word happy and what it could imply.  If we think about it, we know that taking the use of the word 'happy' out of context from Happy Christmas, we could imply wrongly that from its current abundance of use that everyone is now more happy.  It would not only be misleading but could lead to personalisation errors later. The same principle applies for the word 'merry', it would be wrong to assume that the current use of it means that we have all drunk more. T...

This visualization explores the ups and downs of the Bible narrative, using sentiment analysis to quantify when positive and negative events are happening: Source: http://www.openbible.info/blog/2011/10/applying-sentiment-analysis-to-the-bible/

Simplify360 has been exploring the relationship between the number of Facebook fans and engagement level to reveal that on an average, each new post generates 826 likes and 309 comments. The starting point was 50 Facebook fan pages with a random mix of brands from all over the world from consumer brands, to sports teams, to celebrities. What it tells us that there is some engagement but not a lot.  I would like to see the coloration of the noisy ones to see if it is many people or just a few. This also misses sentiment and they miss a lot by defining Liking Rate and Commenting Rate as the average ‘likes’ and ‘comments’ a post would generate if the number of fans for the page is normalized to one million. Only posts by the page admin are considered for the study. Overall – it says that the content was not written to generate comment, engagement, conversation or relationship….

"I love being able to pay bills"  doesn't mean I like actually paying them. I am on the search for phrases and word sets that allow me to test a number of algorithms to see if there is actually understanding/ correct interpretation.  The point is to look at the data (metadata) and determine insight and not facts.  The trick to engagement based on what the data tells your; is how the insight is presented back All ideas welcome.

  This is about social media using engineering terms to try and define/ categorise patterns being seen or looked for in your data. For the purposes of this blog I am currently defining the following:- Social signal (physical) - think physical behavioural signals you give off when interacting. A seemingly erratic behaviour that routine, regular, repeatable and actually has a defined pattern irrespective of who you are. Social signal (digital) - think digital behavioural signals that are a continual feed from digital interactions. Social spike - think spike from a crowd doing the same thing for a short time and then moving on. Social pulse - think regular pattern or behaviour from a crowd when stimulated. Social wave - think growing sentiment of change from a crowd doing something different and moving to a new normal. Social trend - think underlying slow change in the crowd.

I wrote that Social filtering is deeply human at the beginning of November and I knew that there was more to the topic/ theme/ thought then but I could not articulate it. Since then I have been juggling with various ideas, these have often been driven by my necessity to justify Twitter. Twitter, get it or not, provides a function called “follow” – you can follow who you like, and you get updates/ insight/ information/ attention from them. However, can you turn “follow” into value and is following your social filter based on those you trust. Follow has an obvious value to the person who follows the leader. You gain free insights/ selection/ value/ updates/. This social filter is based on trust and it is different from curators and editors who have specific agenda’s and income/ profit requirements. In the original post I quoted David Armano   “Often times the quality of links and information I get on Twitter is better than what I would have gotten from Googl...

Original Article is from Fast Company “Asur and Huberman started by monitoring movie mentions in 2.9 million tweets from 1.2 million users over three months. These included 24 movies in all, ranging from Avatar to Twilight: New Moon. Then they took two different approaches, dealing with two very different performance metrics: the first weekend performance, which is largely built on buzz and the second weekend performance, which is largely built whether people actually like the movie. To predict first weekend performance, they built a computer model, which factored in two variables: the rate of tweets around the release date and the number of theaters its released in. Lo and behold, that model was 97.3% accurate in predicting opening weekend box office. By contrast, the Hollywood Stock Exchange, which has been the gold standard for opening box-office predictions, had a 96.5% accuracy. “ But what should be even more alluring to marketers: As Tech Review points out , Twi...

Harvesting the data we submit to the social web and using it to make a judgement about how “we” feel.  We Feel Fine has been harvesting human feelings from a large number of weblogs and every few minutes, the system searches the world's newly posted blog entries for occurrences of the phrases "I feel" and "I am feeling". When it finds such a phrase, it records the full sentence, up to the period, and identifies the "feeling" expressed in that sentence (e.g. sad, happy, depressed, etc.). Because blogs are structured in largely standard ways, the age, gender, and geographical location of the author can often be extracted and saved along with the sentence, as can the local weather conditions at the time the sentence was written. All of this information is saved. I wonder how it changes with the announcement of a Royal Engagement? Have a play at their web site You can buy the Book  An Almanac of Human Emotion: Sep Kamvar and Jonathn ...

  Just been reviewing  @ PeerIndex   http://www.peerindex.net/ - this is a web site that looks to rate " Who are the authorities on the web?"   Their claim is that PeerIndex helps you discover the authorities and opinion formers on a given topic.   I cannot work out how much is based on what you say about yourself vs how much it is biased towards how much others say about you, nor how sentiment is taken into account.  Obvious is that it is only that material that is public....