Business intelligence in social media – five strategies that could have helped BP

While many organisations understand the potential benefits to be gained from social media conversations, traditional business intelligence (BI) processes aren’t built to access and aggregate unstructured data from blogs, forums, Facebook, and Twitter.

Popular opinion on products, services, market trends, and competitors is predominantly found as unstructured text in social media sites. The difficult part is efficiently gathering the right data at the right time, transforming it into actionable intelligence, and loading it into BI tools for analysis. 

With more intelligent, more accurate real-time web data processing, new methods for working with business information are possible. Business analysts and decision makers can then spend their time extracting greater intelligence from the data and less time worrying about collecting or accessing it.

Five lessons for successfully incorporating social media data into BI

Two recent events where real-time web data would have been essential in improving strategic communication decisions are the UK general elections and the BP oil spill in the Gulf of Mexico. Both spurred massive amounts of sentiment data from all over the world, freely available on the Internet. If only they had known the following five key lessons for capitalizing on social media data:

1.   Data trumps gut feel. During May’s UK elections, Kapow Technologies monitored social media sentiment to predict the outcome of the closest UK election in recent history. The minority party, Liberal Democrats, got a huge boost following the first ever televised debate. Kapow’s UK Election Buzz accurately predicted that, although the Liberal Democrats got off to a great start, they were unable to capitalize and grow their share of voice. Social media provided a credible source of data to accurately predict the election results.

2.   Timing is critical. In this day of real-time web data, businesses can’t wait weeks for market research reports. If BP were to buy a full page ad in the Financial Times looking to improve public opinion, it is now a simple task to collect social media sentiment minutes after the advertisement runs.

3.   Eliminate the noise. It’s easy to understand trends, changes in momentum, traffic volume, and public sentiment. However, huge events generate lots of collateral “noise.” The bigger the event, product, issue, or scandal – the more noise. For companies like BP dealing with public backlash around the oil spill, they need to carefully evaluate the noise factor and establish filters to ensure they are building a strategy that accurately addresses the critical issues.

4.   All social media sentiment is not created equal. Depending on the source of data, not all public sentiment for politicians, political parties, or BP should be handled or weighed equally. A lengthy blog post by a respected author cannot be considered equal to a tweet or comment by an unknown user. Organisations need to understand their objective before gathering and analysing the data.

5.   Don’t look at data in a vacuum.Having knowledge of events and circumstances is critical to understanding and extracting intelligence in web data. Manual review of data ensures quality and consistency. A balance between automated sentiment analysis and manual review needs to be struck. When using an automated sentiment analysis tool, companies should also weigh keywords differently. Automated tools can’t distinguish sentiment as functional, emotional or behavioural yet.

By Stefan Andreasen, CTO and Founder, Kapow Technologies