I like Klout – the influencer score, report and analysis. But I often feel the need of a more concrete analytics which is lacking as of now, detailed brand analysis and finally, some quantitative analysis on how web marketers could use an individual’s Klout information (obtained from his twitter handle) for running focused geo-targeted campaigns.
Let us take a simple case study.
I will take my Klout Influence Report screenshot. As the report shows in detail, I can see my Score Analysis, Network Influence, Amplification Probability and True Reach. All this information is fine, but it misses out one question which most brand and product marketers ask and are most concerned about – from all this noise, if we wish to extract the signal and wish to know – “your degree of interaction with a brand in question” or “quantitatively estimating your brand affinity towards a brand” – is where the problem lies in.
For example, take my case as an illustration. Which will show how and where the analysis of data downloaded from social profiles become extremely murky and often, enters into the dark. A web marketer from a big MNC has downloaded my social profile data from the net based on my twitter handle, and now wishes to create and target focused brand campaigns to me via facebook, twitter, interacting with me in blogs etc. What does he conclude from the data which is currently available:
– from ”Influential about” – we see 3 entries:
If the person in question is doing a retail product research, he/she can conclude – I have a closer brand affinity towards both Apple & Samsung products. And thus can include me in web campaigns for Apple and Samsung in the near future
Where it goes wrong is this – Yes, I am extremely passionate about anything remotely related to ‘Apple’. But am extremely dispassionate to anything remotely related to ‘Samsung’ as well. The reason why Klout has included ‘Samsung’ in my ‘Influential about‘ list is because I’ve been tweeting and retweeting a lot recently owing to the dozen law suits of Apple against Samsung and vice-versa. Thus, Klout has assumed I’ve positive affinity towards Samsung whereas in real it is ‘negative’.
Thus, the only way out of this type of dilemmas is Klout having a “positive” index and a “negative” index. So, influential list could comprise of top3 positive brands + top3 negative brands. How the Klout algo will figure this out is best left to developers tweaking the back-end algo.
But as this small case study shows – drawing conclusions from social media profile data and to include it in web marketing campaigns without quantitative and qualitative analysis to verify the data can be extremely dangerous and also harm to the campaign in the long run.