Analytics, as known in the digital data parlance, is perceived to be associated with or rather dependent on tools and technologies. Undoubtedly, various software tools and technologies have significant roles to play in analytics but their contribution is limited to data extraction, organization, colleting, counting and to a limited extent, recognizing textual patterns.
Current technologies cannot replace humans in executing critical aspects of analytics; when it comes to managing ambiguity and complexity, human intelligence is indispensible. For instance, it is difficult for any software to define the problem, interpret results in the context of the problem, filter the relevant information from large chunks of data and derive actionable insights to mitigate business risks or take corrective action.
As compared to consumer goods, monitoring and analyzing social media information in healthcare and pharmaceutical industry is far more complex due to various reasons such as regulatory constraints, privacy and confidentiality issues, non-standard patient experiences, complexity in understanding symptoms, side effects, reaction to drugs, reasons for patients switching brands etc.
Broadly, social media analytics process involves converting information into intelligence, intelligence into insights and identifying initiatives based on these insights. As we traverse through the 4-I continuum of information-intelligence-insights-initiatives, it is critically important to understand the roles of human analysis.
The art lies in understanding what can be achieved using software and what should be done by humans. Here’s my take on that.
Before we start, I would like to mention that it is critically important to define the business problem and set the direction of research; needless to say, both have to be done by humans.
Information stage: Although low as compared to the advanced stages, the human involvement is needed in defining the objectives of the research, creating keyword taxonomy and feeding it correctly in the system using Boolean search logic. Several listening-software products such as Radian6 claim to provide real time social media data analytics, automatically and conveniently. In my experience, automated listening software do a fair job at discovering data, are good at extracting data but not so good at giving out meaningful analytics. They certainly do the counting part well. What the software gives you depends on what you ask. It is important that the right instructions are received by the software. Importance of defining the taxonomy that covers all aspects of the problem cannot be overemphasized.
Intelligence stage: Use of the term “intelligence” should be sufficiently indicative of the strong human involvement is required at this stage. The extracted data includes important information, some irrelevant data and some junk. Various methods are used to filter out the junk as well as separate the irrelevant material. Let me elaborate on “relevance” here; information may be relevant to the keywords, but not necessarily relevant to the objectives. For instance, if “skin cancer” is a keyword, then information around skin disorders (not necessarily cancer), is relevant to the keyword “skin” but not relevant to skin-cancer. Further, data around skin care products or animal-skin products would qualify as junk. Such distinctions can only be humanly made. Boolean search will allow data filtering based on such criteria, but high accuracy is rare.
Insights stage: Insight generation is interpreting the information in the context of the defined problem. At this advanced stage of analytics, human involvement becomes even stronger. At this stage, the relevant information is categorized, tagged, analyzed and consolidated. Combining insights helps solve the problem. At this stage, technology can play a diminished but nevertheless significant role by offering insights at one place. For example, a consolidated mash-up of charts in a dashboard can show sales trends in one chart, social media sentiment trend in another, a tag-cloud of side effects in a third and switch-over patterns in a fourth. All these put together may help in understanding the cause of the problem.
Initiative stage: This is a purely human activity since it involves taking decisions around the “now what” part i.e. action. The information has been extracted, intelligence gathered, insights derived, problems identified and now it is time for initiatives i.e. finding and implementing a solution to the problem.
For instance, if there is substantial noise by a social advocate around side effects of a particular drug on Twitter, the brand team can implement digital initiatives and engage the advocate for spreading information around managing side effects using (hypothetical example) a vegan diet.
In summary, for efficiently implementing social media analytics programs to solve business problems, managers have to optimize the use of technological and human resources in the process, as they traverse the continuum of information-intelligence-insights-initiatives.