Can Social Media be the next big lever for Business Intelligence? - Part 3
Guest post by
Karan Chadha, Associate Consultant, Infosys
In Part 1 of this blog, I had talked about the immense potential that Social Media data holds for getting transformed into business insights. In Part 2 I moved on to discuss Social Media Intelligence (SMI) tools and the key value adds that they provide to Organizations. I will now move on to the piece that is the most relevant for us; the piece about integrating SMI tools with conventional reporting tools like OBIEE and establishing an integrated reporting environment; an environment which leverages insights from structured enterprise data as well as fluid social data.
Let me touch upon two facets of what is now broadly known as Social BI:
- Transforming generic insights offered by SMI tools into actionable customer specific insights
- Richer customer insights by leveraging social data in conjunction with traditional data
Transforming generic insights offered by SMI tools into actionable customer specific ones
Insights offered by SMI tools are often generic in nature. A typical insight from an SMI tool will be like, '60% of people have a positive sentiment towards the brand and 40% have a neutral/negative one'. However, there is no way to figure out which customer holds a positive sentiment and which one holds a negative one. Neither is there a way to figure out if a sentiment actually belongs to our customer or it is just spurious one. Therefore, such insights are useful but incomplete.
Integration with the Data Warehouse can completed this half told story of an SMI tool. Assuming that an Organization has mapped its customers against their Social Media IDs, they will be able to track down each social conversation at a customer level. This will offer two major benefits:
- Determining Patterns across Sentiments
The ability to track down conversations at a customer level will be useful in determining patterns across conversations. For instance, an Organization might figure out that a large proportion of customers having a positive sentiment are old-time customers (say > 4 years) and a large proportion of customers holding a negative sentiment are newly acquired customers. This insight will send a clear message for an Organization to step up their service quality for newly acquired customers. If such a disparity in sentiments is found across geographic regions, it will send a message to probably have a relook at the customer service teams in negative sentiment regions. So, a generic insight gets transformed into a powerful actionable insight.
- Prioritizing addressing of Grievances
Social Media is a noisy space and the number of relevant conversations can be overwhelming. However, the priority that an Organization would like to attaches to each conversation may be different. A negative sentiment expressed by a decade old high-value customer may be viewed and acted upon different from a similar sentiment expressed by a newly acquired customer. Integrating SMI tools with the data warehouse can help Organizations put different customers into different priority buckets based on factors like years of association, customer life time value and social activeness score thus streamlining the way they manage and prioritize a huge pile of social conversations.
Richer customer insights by leveraging social data in conjunction with traditional data
Typical customer databases will include traditional fields like name, e-mail & address. This information can be enhanced by adding social information to it. Social attributes, fetched from the social media profiles of customers, can give an authentic reflection of the softer aspects of a customer. This can be precious information from the point of view of understanding the psychographic profile of customers.
If Amazon can benefit from this data by knowing the books that it should target a customer with (say based on the books that a customer has listed on his social profiles), Nike can base the development of its new series of shoes around these insights (say based on discussions where customers are talking about enjoying their jogs on dew laden grass and thus requiring suitable shoes).
Essentially, insights leveraging social attributes in conjunction with traditional data have the potential to help organizations prepare better product development and targeting strategies.
I will delve further into these aspects in Part 4 of the blog.
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