What is the talk in social media when it comes to “organic food”?
Table 1 shows how often a set of words typically used in connection with Organic food were used. (All data relate to 90 days prior to March 18th 2007).
According to Table 1 the word “healthy” had a share-of-voice of 25% compared with the other 4 words analysed.
Table 2 shows the Share-of-Impact for the same words.
Notice how the share of “healthy”, “sustainable” and “nutritious” drops while “environment” grows.
The difference between Share-of-Voice and Share-of-Impact is that the latter factors in the topical influence of each voice. This provides a better model of the effects of the conversation.
One conclusion that can be drawn from comparing tables 1 and 2 is that the online media that uses words such as “healthy”, “sustainable” and “nutritious” in the context of Organic food tend to be those with less influence on the topic.
Perhaps this is because there is little actual evidence to suggest that Organic food is healthier, more nutritious and more sustainable than non-organic food.
Table 3 and 4 slices the data from Table 1 and to into months. Table 3 shows the raw voices whereas Table 4 shows the impact.
From looking at the raw buzz (Table 3) we could easily come to the conclusion that “environment” is on the decline and “nutritious” is on the rise.
However, when looking at Table 4 we can see that “environment” is pretty stable and the growth of “nutritious” is nowhere near what Table 3 suggests.
All pictures are from the Onalytica InfluenceMonitor and © Onalytica
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
hereAll pictures are from the Onalytica InfluenceMonitor and © Onalytica