The below graph shows the Share-of-Influence for various election issues from 22nd April to 5th May.
The comparison issues (Hung Parliament, Expenses Scandal, and Electoral System) were also major stories during the election and selected here to provide a benchmark for measuring the scale of the Bigot-gate story. The chart shows us:
- Bigot-gate (and associated terms) were discussed more than any of the benchmark topics for two days, the 28th and 29th of April.
- Among the other issues selected, Hung Parliament was discussed the most.
- Bigot-gate declined rapidly in Share-of-Influence after the 29th of April but did continue to be widely discussed on May 5th.
I was looking at the debate on the problems surrounding the subprime lending crisis in our InfluenceMonitor service today.
The figure below shows the share-of-influence(1) of a number of well known investment banking brands has in the online “subprime”-debate.
Notice how the share-of-influence of Bear Stearns and UBS seems to be correlated: Initially UBS was the brand in focus. In June however, Bear Sterns announced problems with some of their hedge funds. For a while it took the heat of UBS.
However from July and onwards the focus on UBS has been steadily growing while the interest in Bear Stearns has dropped almost correspondingly.
Interestingly Citygroup, Merrill Lynch, Morgan Stanley, ABN AMRO, Credit Suisse and Deutsche Bank mostly escape attention.
The focus on Goldman Sachs however seems to be growing.
Share-of-influence is calculated by measuring the share of earned coverage a brand gets in a particular context and then factoring in the measured, topical influence of each voice that discusses the brand. So, when the topic is "subprime" a mention in NY Times will count for roughly 50% more than a mention in CNN.com’s money section.
Independent has a story
today about Nintendo quadrupling their profits in the most recent quarter; mostly due to Wii.
If you look at how the sentiment for a number of brands in the games console space has developed over the last 90 days you can see why Nintendo is doing well. (See below)
The Word-of-Mouth about Wii is just so much more positive than that of the competing brands.
Xbox followed Wii for the first 60 days, but couldn’t keep up. The others were left in the dust.
The sentiment above is adjusted for the topical influence of each voice to give a more correct representation.
See a previous post on Wii and positive sentiment here.
I was looking at some of the candidates in the US Presidential election through InfluenceMonitor.
John McCain has had a bit of a comeback while Barack Obama has slipped. Senator Clinton seems to be a clear leader in the Democratic field.
Figure 1 shows the 6 candidates’ relative share-of-voice of online media in the last 180 days. Notice how McCain and Clinton have an equal share of the coverage.
Figure 2 shows the 6 candidates’ relative share-of-impact for the same period. Here we can see that McCain’s share of impact is substantially bigger than Senator Clinton’s, reflecting the fact that although they had an almost equal amount of coverage, McCain’s coverage was mainly in more influential media.
When measuring share-of-impact the online voices are weighted according to their influence on the topic analysed. For example: When measured over the last year, NY Times has roughly 186% of the influence of the Washington Post when it comes to the debate on the US presidential election. When measuring share-of-impact a mention in NY Times will thus be counted with a relative weight of 1.86 over that of the Washington Post. Another way to look at it would be to say that a candidate would have to have coverage 19 times in the Washington Post to match the relative share-of-impact of a candidate getting coverage 10 times in the NY Times.
You can follow the graph in near real-time (4 hours delayed) on the Onalytica front page
When it comes to analysing online/social media, “buzz” has long been the buzzword.
“Buzz” is of course the industry slang for the online “talk” about a particular topic or brand.
There are several free online services that will show how much buzz there is about a topic or a brand. Some will even show how big a percentage of the buzz on any given day is about a particular topic.
These services are great for getting a general impression the buzz levels.
However, when it comes to making conclusions based on buzz levels, one has to be careful relying on “raw buzz”.
One of the problems with measuring the levels of raw buzz are that raw buzz treats all voices with equal weight: 1 mention in a random MySpace blog and 1 mention in The NY Times online edition both count as 1 mention and contribute equally to the raw buzz level about a topic.
However, the impact of a mention in the NY Times is likely to be greater than being mentioned on a random MySpace blog. Why? Because NY Times carries more influence on most (if not all) topics than your average MySpace blog.
So in order to make more sense of buzz one needs to factor in the weight (influence) of the “buzzer”.
The figure below shows the relative amounts of online buzz about 3 pharmaceutical brands got in the context of “Blood Pressure” in the 14 days prior to the 8th of March 2007.
So according to the chart, Pfizer was mentioned 2.7 times more often than Merck in the context of “Blood Pressure” during those 14 days.
During the period Merck and Pfizer was discussed in a variety of online media. Some discussed both brands; others just one of them.
And because they appeared in media with different influence on the topic of “blood pressure” the figure showing the distribution of Raw Buzz doesn’t give the correct picture about how much impact the brands made.
During the 14 days Pfizer appeared in the context of “blood pressure” in the NY Times, International Herald Tribune and Medgadget.com as well as a list of other online media. All of these 3 sites carry more influence on the topic of “blood pressure” than Businessweek.com – the most influential onlinepublication Merck was mentioned in during that period (1).
The figure below shows the relative impact of the 3 brands during the period. Because of the greater influence of the media Pfizer appeared in, they actually made an impact that was 4.6 times that of Merck for the period – not 2.7 as the raw buzz would suggest.
The two figures below shows the same distribution of raw buzz and impact – now monthly over 90 days.
Notice how Pfizer had a 73% share of Raw Buzz in December vs. Merck’s 26% - while the actual impact distribution was 46% vs. 53%. This is a good example of how the interpretation of the Raw Buzz could have led us to a wrong conclusion about the impact of the online media coverage.
Influence not only plays an important role as a way of converting buzz to impact. It is also crucial to get a correct impression of the sentiment surrounding a topic or brand.
Although some may argue that the next example deals with neither a topic nor a brand it illustrates the point.
The figure below shows the number of online blog post about British politician David Cameron for the last 30 days.
The orange graph shows the aggregated sentiment (Net Promoters Index) of the posts. Notice there is a steep dive on the 11th of February. On that day a number of news reports suggested that Mr. Cameron had smoked cannabis during his younger years.
This news, combined with Mr. Cameron’s media handling, prompted an increase in negative posts and hence the drop in sentiment.
However, looking at the graph one could easily come to the conclusion that the sentiment had now stabilised at a lower level. This would not be too bad for Mr. Cameron as the direction of the sentiment is normally more important than the absolute level.
However, if we take a look at the next graph, where the sentiment is adjusted with the relative influence of the “speaker” the picture is quite different and more positive for Mr. Cameron.
It shows that although he took a hit on the 11th of February he has not only recovered but is basically continuing on the very positive trend from before.
When comparing the two graphs we can conclude that those who are more influential on the topic of “David Cameron” are more positive than those who are less influential.
The take for Mr. Cameron’s team could be that the issue from February 11th is now largely over and Mr. Cameron’s PR people should consider leaving the matter alone. This might not be the conclusion they would arrive at had they just relied on Raw Sentiment rather than Impact Adjusted Sentiment.
(1) The relative influence of the 4 sites, on the topic of “blood pressure” was measured to 4.02, 1.62, 1.42 and 1.20 respectively – measured over 120 days
For more on how to measure influence, take a look at one of my previous posts here
All pictures are from the Onalytica InfluenceMonitor and © Onalytica