Preriit Souda 

Dear Diary

Imagine that you are at a networking event to talk about your product. Several people come to talk to you; some ask specific questions, some share their past experiences, good or bad, while some share their ideas to improve your product. At the end of the event, any good networker will make a list of how many and who came to talk but more importantly they will try to understand conversations, learn from them and take appropriate actions. Now let’s look at this example from a social media (SM) context. Social Media networks are like networking events where brands want people to come and talk to them about their experiences, likes, dislikes and give suggestions. If that is the case why do 95% of social media tools stop at just the volumes of how many people came and talked to you or about you but never delve into the details of what is being talked?

Tools can tell people are talking about you but not what they are talking about, to whom, how and why those conversations happened. Present tools are like robots, they are fed rules but have no brain. Had it been a real networking event would you have wanted such a robot? Would it not be more useful to analyse social media conversations with a context specific to the brand or campaign engrained, not a generic algorithm based on generic knowledge? Can you rely on a robot to decipher novel ideas suggested by your customers or would it not be useful to have a human assisted by machines to help understand hidden meanings? If you think so, keep on reading.

One of the biggest frustrations I have with present social media monitoring practices is excessive reliance on volumetric metrics like number of tweets, trends, impressions, no. of fans, likes etc. Some of these metrics are quite important but my disagreement lies with the over-emphasis (ad sometimes blind following) of these and often a complete disregard for bringing meaning to actual conversations. Recently I was talking to a SM analyst in New York and we almost came to blows on usage of a metric called impression. Impression tells the number of times an ad was seen [1] [2]. In the context of SM conversations (specifically on twitter) most analysts count based on the number of followers every tweeter talking about your brand has and assuming that whenever they tweet, all of their followers will be sitting in front of their twitter account to watch it! Andy White, a social media consultant has written about misuse of this metric in his insightful blog [3]. Impression can be used as an Ad (outdoor or online) metric because you possibly don’t have any other way to measure that, but with social media that’s not the case. You cannot stop and talk to a billboard you liked but on Social Media the consumer has more options, they can talk to it (comment), show it their friends (share) and possibly even carry it on their mobile device (save). There are a lot of metrics which are simply copied from either the outdoor advertising industry or web analytics. Several such metrics might be good for comparison of various media, but these are often misleading in understanding social media. Several social media tools are based on techniques rooted in web analytics. Web analytics are good for websites where people go from link to link but social media is not a mute web page, human interactions are involved.

A lot of social media analysts will say, “don’t we have sentiments and categorisation options available in some tools to understand conversations?”. For those, I sincerely ask them to spare one day of their busy life to read through actual data going into their analysis and related outputs. Recently I was reviewing a social media tool and was shown twitter search results based on a query around a brand. The vendor created a category map which looked quite cool thanks to the inbuilt visualisations. But when I drilled into the actual data, things turned out to be uncool. One of the categories was labeled CEO and we expected it to contain data around the CEO of that brand but actually only around 10% data contained related conversations. I won’t go into the technical reasons, but the point I am trying to make is that that often SM tools are simple machines with no brains, but why are we forgetting this fact? We need machines to help in the process not understand it for us.

This brings me to sentiment. Often I’ve looked at sentiment analysis given by most tools and I get 90%+ conversations marked as neutral. In most cases tools fail to understand context of the conversation. Most of the present day tools view language as a mathematical equation and process it accordingly, while in reality language is complex and formed of structures and intricacies which go unnoticed. Some people suggest human coders for such cases but I feel that a dual approach of using human analysts & reviewers aided by powerful NLP enabled machine learning can help.

Before I close this blog, I would like to say that social media analysis is in its infancy and we need to constantly debate on what is right and what can be done better. I have often seen people either overhyping capabilities of social media while some undermine the power of social media analysis and view it with an eye of disdain. I feel that both camps are wrong. Social media analysis can show insights that can never be hypothesised or deciphered by traditional survey research. It can help get opinion from hard to reach people expressing REAL feelings in real time which is impossible via a survey. Yet there are lots of areas where SM analysis falls short; especially in understanding the WHY part and that’s where surveys can be important. The depth and volume of insights that can be gathered via social media analysis varies by industry and brands making traditional research necessary as a supplement.

I can go on and on but ending this blog, I will say that next time you see a social media analysis try to understand the conversations and not simply the numbers. It’s a new field so let’s be open minded but with an inquisitive eye.

Like always, these are my observations but feel free to agree or disagree.

Controversially yours,

Preriit Souda is Senior Analyst at TNS Global

The views expressed are solely personal and do not reflect views of TNS or associated companies.