I have often said that companies are missing out on the real value of social media analysis. More often than not, even the big players don’t have the processes or models in place to really make use of the data gained from the analysis. As a result, social media analysis has a limited impact on the business, not to mention the budgets assigned to such projects.
I recently met with the head of customer experience at a well-known bank to discuss tools they would need to support social media analysis. I was prepared to give my usual pitch about how “sentiment analysis is useless but you should still mine social media” when this manager stopped me. Imagine my surprise when she asked if we could jump ahead to the part about what they really needed from us.
It’s nice to know that things are changing—and it’s a bonus to experience that change firsthand rather than reading about it on a blog or in an analyst report. This was the first time that a discussion around social media analysis tools was put into a broad, clearly defined perspective. The surprises didn’t stop there. Our customer was able to make very specific examples of the quantitative AND qualitative data she wanted to extract from these streams.
This was a natural and logical entry point for semantics—and made it easy to explain how semantics can bring value to their company. We focused on some of the core capabilities where semantics really distinguishes itself, especially how:
- Extracting relationships between monitored entities—for example people, brands, locations—and how they change over time, and how monitoring can adapt to changing business models.
- Brand attributes that indicate trust, appeal, consistency or relevance go much further than good or bad opinions in determining the health of a brand.
- The nuances behind the unstructured part of market research—such as the responses to open-ended questions, are extremely valuable in creating a feedback mechanism to accurately predict future trends.
- And especially how minimizing noise in social media content—through contextualization and deep analysis—can significantly improve the ability to visualize product and market trends.
I don’t think this will be the last time I make my ‘usual pitch’. Sidetracked by the buzzwords and confusing marketing messages, many organizations are still not clear on the strategic value of social media projects. But change is afoot. As in the example above, the clearer the strategic view, the easier it is to turn raw precision and recall data into business value. The trend toward a clearer understanding of the strategic value of social media data (and I hope it IS a trend), and the enhancement of predictive modeling present a unique opportunity for real semantic technology vendors… and we are here to take advantage of this trend.
Author, Luca Scagliarini.
