Taking Social Media Analytics To The Next Level
One of our original design goals at PatternBuilders was to make the aggregation of data sources a simple and ongoing process.
We have recently become very interested in analytics that look at social media data, primarily because the streaming nature of social media makes it ideal for our streaming analytics engine. This seems to have been overlooked by most of the existing solutions in the space—this is probably due to the fact that the sheer amount of data and the opportunity to aggregate it with other large streaming data sources, such as POS, sell-through, and sell-in is very hard to do.
In other words, there seem to be a lot of solutions out there that can give you the social media basics, such as how many times a company was mentioned or scoring on how influential a particular Twitter member, or blogger, might be. However, there doesn’t seem to have been a lot of effort made to tie the social media world into non-social marketing, qualitative research, and actual sales. Or provide in-depth correlation features that can allow you to find the activities that have real ROI as opposed to those that don’t. We want to build a social media analytics system that can answer the hard questions, such as:
- How much sales uplift can be directly attributed to the September Facebook campaign?
- What is the multiplier effect of the social media response to local traditional media buys?
- What is the magic, or optimal, mix of social and traditional media to drive sales?
Social media is an undeniably powerful marketing tool, but its real power will only become evident when we use the same sort of aggressive analytics to explore ROI that have become standard for marketing in traditional media. The good news is that as a digital medium the data is out there; we just need systems to analyze it.
When I spoke at Strata West, I made a deliberately provocative statement about the multi-billion dollar market research firms which seemed to annoy a number of folks. I suggested that with crowdsourcing, if you have Facebook, Foursquare, and POS data, do you really need Nielsen and NPD? If you have an analytics system that can take this data, aggregate it with other sources and then bring it into an easy-to-use, high performing environment, the answer is no. Keep your eyes peeled as we are working with several partners to make this a reality and we’ll be previewing it shortly. We hope you will try it out.
If you have ideas for analytics you would like to see to bring true ROI to your social media efforts, let us know so that we can incorporate them into the product.
We think that data marts, like GNIP, will provide easy access to social media data. For product manufacturers in every consumer category, analytics that combine social media data with other sources to provide hard ROI have the potential to be the killer app for BigData and we are very excited about bringing our technology to bear on it.