Social Media Intelligence
As one would expect, social media intelligence (or just social intelligence) came up as a subject at the “Social Intelligence World” conference in London back in November 2018. More specifically, it came up in the context: how does it differ from social media listening?
This question took me back several years, when we published our first eBook about “web listening”, our label of choice at the time which was a buzzword; its most popular version was “social media monitoring”. Social media intelligence did not come up at all back then, albeit in hindsight it is odd that it didn’t. I am not sure how we missed it then, but now, when someone asks what is the difference between intelligence and listening, the answer seems quite obvious!
Social media listening or social media monitoring is simply about harvesting the online posts and maybe even annotating them with a topic and/or sentiment. If the annotation is accurate then it answers questions like ‘what are people talking about online’ or ‘how do they feel about my brand’? Social intelligence on the other hand, is about understanding the deeper meaning of what people choose to post - although sometimes there isn’t one - and link it to a business question; notice how the term ‘actionable insights’ has not come up yet? Another buzzword that is overused in the market research sector, and another one for which we published numerous blog posts with our own - very concrete - definition of what it really is!
When we say ‘social media’ in this context we don’t just mean social media platforms, but rather any public online source of text and images which might express consumer or editorial opinions and/or facts. A side note: things would be a lot easier if we meant what we say in a literal way. People who coin phrases or titles or headings tend to take a lot of freedoms on the altar of “crispness” or “snappy creativity”!
listening247 - an aspiring state-of-the-art DIY SaaS looks at the world of social intelligence via four lenses:
- Source (verb)
We would be remiss if we didn’t mention text and image analytics as a standalone discipline when the source is not social media or other online sources. In such a case the only difference is that the source is not the online web but any other source of text and images. Perhaps if the source is not the online web it should just be called Business Intelligence, which is an old and very familiar discipline within organisations.
Back to the 4 modules, they have the power to generate intelligence derived from unstructured data - which make up 80-90% of the human knowledge, produced since the beginning of time. Structured data which are effectively numbers in tables or graphs only account for 10-20% of all our knowledge as a species.
Unstructured data can be harvested from the web and if we want to stay out of jail we will stick to public data (as opposed to private conversations or personal data). They can be harvested through APIs that the sites which contain the data make available for pay or for free, and through scrapers which can crawl a website and find specific consumer or editorial posts. Responses to open ended questions in surveys, transcripts of focus groups or even call centre conversations are also great sources of opinions and facts (i.e. unstructured data).
In order to make sense of big unstructured data, machine learning is a good place to start. Supervised machine learning requires humans to annotate a big enough sample of the available data. The annotated data-set is then used to train a machine learning algorithm to create a model that does a specific job really well; the aim is to get over 80% relevance, precision and recall. Unsupervised machine learning is making great strides but cannot replace the supervised approach currently.
Once we have a trained model and our data-set we need to process the latter and annotate it in its entirety. The data can be filtered and navigated in many ways. Structured data can be produced in the form of tables, making the analysis of the data-set possible. The goal here is of course to enable human analysts to uncover actionable insights - since machines are not there yet.
Data visualisation is typically done on dashboards or PPT presentations. The most appropriate types are drill-down and query dashboards. There are multiple delivery mechanisms and use cases, e.g.
- Annotated data via an API
- Access to an online or offline dashboard to interrogate the data
- Executive summaries and periodic reports
- Email alerts
- Fixed dashboards for war-rooms
Social media intelligence has multiple use cases for multiple departments as shown in the list below, annotated as multipurpose ‘intelligence’ or specific ‘actions’:
- Market research: find out how customers think and feel about products and campaigns (intelligence)
- Advertising: use positive posts as testimonials or ideas for Ads (action)
- PR: amplify positives and appear to have good answers to negative comments, brand ambassador communities (action)
- Customer Care: respond to online comments (action)
- Operations: fix customer reported product issues (action)
- Product development: discover new product trends, missing product features (intelligence)
- Sales: identify sales leads based on expressed purchase intent (action)
The many departments involved and the many use cases ultimately create a confusion as to who the owner should be within an organisation. Maybe Social Intelligence should simply be part of the Business Intelligence or the Market Research department, offering custom user interfaces to the various action players with only the information they need specifically to take action.
Having a Business Intelligence or Market Research Department is a privilege reserved only for large organisations. For small and medium enterprises (SMEs or SMBs) that do not have a business intelligence department a different approach and possibly nomenclature should be employed; but this is the stuff for another blog post. In the meantime let us know where you stand on all this by emailing us or tweeting to @DigitalMR.
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