Is Social Listening Delivering on Its Promise?

This year (2018), an estimated 3.2 billion people will be using social media worldwide. With this magnitude there is no question every brand wants to know what is said about it over social media. Its common knowledge that that’s where consumers express themselves and following what your consumers are saying about you is good practice. Just to look at a couple of data points about the sheer volume of information on social media –

  • Every minute on Facebook: 510,000 comments are posted, 293,000 statuses are updated, and 136,000 photos are uploaded
  • Worldwide, 15,220,700 texts are sent every minute!

 

With lots of data comes lots of …. Noise! Apparently when brands look to leverage the social data and its massive amounts of information they typically find that the majority of it is either useless or shallow.

 

What are brands looking to learn?

With social listening brands are typically trying to decipher social content deep enough to be able to achieve the following goals

  • Reputation defense – Spot consumer emergencies and respond in real time
  • Brand sentiment – Understand overall sentiment trend and is the brand still lovable
  • Identify influencers – Key people that the crowds follow so they can be nurtured
  • Key trends – Keyword trends, what is hot, to develop high level market understanding

 

How easy is it?

Just so you know, there’s a lot of time and effort going into social listening, its not plug and play…You’d think with all this data available all you have to do is just mine it…but how would you? You could use text analytics software and teach it what keywords and terms to look for. Its challenging as you need to guess a lot:

  1. Guess ALL the different ways that different people talk about the same thing: If we take the example of a phone battery, you’d need to look for all positive and negative expressions about it such as “my phone lasts for days” or “mine doesn’t hold charge for more then 4 hours” or “my phone’s battery is weak”
  2. Guess the discussion topics that are not on your mind: How can you guess ALL the different topics consumers talk about? The short answer is you can’t, which means you are missing quite a bit
  3. Guess the next hot topic: Consumers interests shift all the time based on new products, features or change in flavors. You’d need to know something has come up in order for you to look for it, and then obviously you need to guess the different keywords used by the masses to describe it
  4. Mapping topics to products and services of yours: Once you actually mapped topics, how do you know which product or service of yours they relate to? There is no SKU. You can try to search for product names but you can’t really rely on your audience to accurately put it in

In summary on the effort part there is a lot of manual setup effort and at the end you can’t really get granular results, you just know basic sentiment and general popular topics, but without the deep (Product/Service) context associated with it.

 

To chase a moving target you need different methods

Training systems to look for rigid keyword patterns that humans guess in hope that we cover all that consumers talk about in social media seems futal. Lets try to spell out what type of systems can actually track a dynamic, moving target of topics:

  • It has to be self learning
  • Able to identify topics and interests
  • Able to understand that different terms talk about the same topic
  • Able to learn new terms that surface and their meaning

With Artificial Intelligence and Self Training algorithms you can skip the person-training-machine step which is the most limiting one.

 

Conclusion

With social listening the goals are pretty well defined, and most of them are shallow and easy to achieve –

  • Reputation defense
  • Brand sentiment
  • Identify influencers

The key challenge is to drive meaningful insights from the masses of social data that you have, and for this, most text analytics technologies that rely on humans are not enough. Consumer interests are too much of a fast moving target.

The good news is that the data is there for us to see and mine, assuming we can get to it.

Revuze is an innovative technology vendor that addresses just this with the first self training, low touch solution that can mine data automatically. This is why its much more granular and typically delivers 5-8X the data coverage compared to anything else, and it does it without humans helping.

 

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