Last Fall, Michael Osofsky, co-founder and Chief Innovation Officer at NetBase, walked into my office and said, “What if I told you we can identify the number one thing women want?” I replied that the number one thing this woman wants is a nap, but I was intrigued.
Michael explained that ConsumerBase, our social intelligence warehouse filled with billions social web conversations, could be queried to find all the sentences in the database that take the form “I want (object).” He could then use the functionality we’d recently added to NetBase that allows us to identify whether a post comes from a man or a woman, and we’d have the answer to the eternal question, “What do women want?”
How could I say no?
Explaining the Methodology
We decided to pursue Michael’s idea because we thought it would be fun and interesting, but also because it would help us illustrate all the possibilities that exist for understanding conversations in social media. We proceeded to identify categories of wants, pinpointed the most talked-about and most-loved brands, and found themes associated with each of the brands. We finally had to stop and admit that there are endless possibilities for what you can do with this kind of data. To establish our credibility upfront, check out a semi-in-depth explanation of our methodology, at the bottom of this post*.
But for now – let’s get to the good stuff!
Presenting (Ta Da) … the Infographic
We think the results are fascinating. We had so many interesting insights that it was hard to decide what to put into our infographic, but see the entire finished product below! You can also find it on Mashable here, and read comments from viewers. What do you think, did we get it right?
Stay tuned for even more fun insights from the clipping room’s floor!
*NetBase has created a cloud-based natural language processing (NLP) engine that reads social media and web content using deep parsing, English language grammatical analysis, industry lexicons, and filters that determine sentiment and gender voice. We queried our social intelligence warehouse (ConsumerBase) for all online conversations including the phrase “I want X” within 27 billion social media sources over the past year. Next, we determined the Top Ten wants for both men and women according to mentions of “I want X” online. Then we queried ConsumerBase for mentions of each of the Top Ten wants for men and for women to determine the Top Ten brands that were surfaced (based on amount of chatter) in each category. With our NLP, we were able to determine the most-buzzed, the sentiment of the brand mentions, and the most-loved brand in each of the categories within men’s and women’s Top Ten wants.