The Mindset, Skillset, Dataset Approach to Social Media

A new approach called Mindset, Skillset, Dataset can help marketers make sense of complex social media data.


Social media is known to be an addictive medium and can be a performance deflator at work. But it also represents a big area of opportunity for marketers who want to target market based on social media data. The problem? Social media is comprised primarily of unstructured data, which is difficult to analyze.

A new approach called Mindset, Skillset, Dataset can help marketers make sense of complex social media data. Mindset refers to the ability to think beyond a certain frame of reference and look at the bigger ‘Why’ of solving a particular problem. Skillset refers to analytical techniques and tools that can be used to solve a particular problem. And Dataset refers to the copious amount of data generated from social media.

The availability of data and skillsets/technique is almost never an issue. Typically, it is the right mindset that’s lacking. Here are some tips for getting in the right frame of mind:


Mindset 1: Unearth Patterns – Look for deeply enmeshed patterns from an apparently innocuous dataset

Social media, due to its inherent nature of people and chatter, often contains deeply enmeshed underlying patterns that might not be immediately visible. These patterns can evolve to become treasure troves of predictive knowledge about a particular event. Google’s Flu Trends is a perfect example of identifying patterns and using them to predict the intensity of a particular event.

Flu Trends aggregates Google search queries on influenza for more than 25 countries based on user searches. As it turns out, when aggregated, individual searches can be a powerful indicator of flu occurrence in a given place.

From a skillset point of view, some of the analyses that help unearth these patterns are as follows:

  • Social networks analysis/mining
  • Tagging/links/graphs analysis and mining
  • Community detection and evolution
  • Influence, trust and privacy analysis
  • Social media monitoring/analysis

Mindset 2: Understand Opinions – Analyze opinions and categorize user sentiments

People used to share their secrets and sorrows with their best friends prior to the onset of social media. Now, social media listens to everything. This enormous amount of emotico-temporal data yields insights into how a particular set of users feel about a new/existing product. It can help validate and supplement the findings on problem areas for a specific product/brand done using surveys or other analytical models.

One of the leading South American personal healthcare products company adopted a new business model and wanted to understand market reaction to the change. With only a shoestring budget for analytics, they decided to study consumer sentiment on social media. It turned out to be a superior and accurate tactic, partly owing to the volume of data on the web from affected customers. The analysis helped the company understand that suppliers and middle agents were feeling very negative about this new model because it lowered margins for them, while end consumers were delighted due to the drop in overall prices.

From a skillset point of view, some of the analyses that can help understand sentiment are as follows:

  • Opinion extraction/classification/summarization/visualization
  • Temporal sentiment analysis
  • Cross-lingual/cross-domain sentiment analysis
  • Irony detection in opinion mining
  • Wish analysis
  • Product review analysis

Mindset 3: Establish Relationships – Define, measure relationships and potential connections across users

Establishing connections and similarities across user groups serves to solve two unique problems – one, how to find small, cohesive groups whose actions are of the same nature; and two, how to identify the underlying factors for these visibly unexplainable relationships. Unlike structured data analysis where similar people are associated based on a set of continuous/categorical variables, in the social media world, the data is unstructured and undefined. Establishing connections and causal linkages is an enormous challenge.

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