The first time I encountered the potential application of machine learning was a project where I was helping a company protect itself from competitors infringing on their trademark. One key component of trademark infringement is whether there is the likelihood of confusion by a consumer. High-profile examples include Apple suing Samsung for copying their minimalist smartphone designs, or Tiffany & Co. suing Costco for selling Tiffany-inspired jewelry.
The rise of social networks means consumers are generating tremendous amount of content about brands. My project involved writing a prototype software program to analyze online user comments that mentioned two competing brands, then determine if the user was identifying the product with the wrong brand. An example could be someone sharing a photo of a Captain Morgan rum bottle and calling it by their competitor's name, Admiral Nelson.* The goal was to run a daily scan and gather examples as evidence of brand confusion over time. The field of analyzing human text using computers is a form of machine learning called Natural Language Processing (NLP).
In a world where physical goods are easily copied, the only competitive advantage left is your reputation.
Reputation is the way you expect a company will act - positively or negatively. A positive reputation develops when a customer starts trusting a company, and becomes loyal to the brand. The strongest extension of that trust is a recommendation from someone that you also trust. So, how are companies protecting their reputation?
Well, we've seen the rise of influencer marketing on social networks such as Instagram, YouTube, Twitter, and Facebook, where trusted individuals are recommending a product to their social friends. In the business-to-business market, it's called advocate marketing, where customers provide case studies, testimonials, and speak at your conference. All of this work is done to grow trust with customers, and strengthen the competitive advantage of your brand.
In terms of monitoring impact, there are tools that use NLP to monitor your brand's 'sentiment' across social media channels. They'll scan comments and determine whether the tone used to describe your brand is positive or negative, and how it trends over time. We've also seen companies increasingly enforce trademark infringement in the courts to protect their brand. But, I haven't seen a NLP tool developed that collects examples of trademark infringement at scale. Perhaps there's still an opportunity for a solution in that use-case.