Cutting through the noise: Big data, AI & Human Psychology
Though influencer marketing is a concept that dates back centuries, it isn’t until now that it is considered its own area of marketing and a key part of the media mix. There have been celebrities and product endorsements as long as anyone can remember so why is influencer marketing just becoming a thing now?
Within the past decade or so, web-based social platforms like YouTube and Blog really opened the door for anyone to create and share content and develop a following as a result. The emergence of the smartphone really put content creation into hyper-drive, leading to mobile native platforms that were ubiquitous and significantly lowered the barrier to creating and sharing content. As a result, a truly social economy emerged with millions of people share content, consumer content, and influence others.
Advertisers are leveraging influencers as an organic conduit for connecting with their target customers. In a world where people have learned to block out advertisements, influencers are a clear means to cut through the noise and deliver a brands’ message to a receptive audience. Though with so many influencers emerging every day and so many social platforms out there, cutting through the noise isn’t easy as advertisers struggle to identify the right influencers to work with. Historically (relatively speaking) this has been a very manual process, relying on industry experts who had a ‘feeling’ for which influencers were effective for a given vertical. However, there’s only so much information anyone person can retain and as humans, we can be pretty bad at leaving our biases at the door.
A.I. is changing all of this. Innovative companies like Open Influence are actively leveraging A.I. to do the kind of analysis that was only possible by humans, at scale without any of the human biases. For example, Open Influence’s technology runs each piece of content generated by its over 500,000 influencers through its image recognition toolset- identifying what’s in the content that influencers are posting about and mapping that to the performance of each of those posts.
Why is this important? It allows advertisers to find the most effective relative influencer for a given message rather than taking a shot in the dark with an influencer. This method is very different from how many other firms are surprisingly still categorizing influencers under arbitrary categories like fashion, food, or fitness for example. Though at first glance these categories seem to make sense, they’re too vague and quickly lose their value once you have more than 100 influencers to assess. An expert, for example, would need to memorize all the influencers in a category to effectively compare and choose the right ones. A.I., therefore, makes it easy to winnow down a list of thousands to a handful. By mapping the labels generated via image recognition to post performance, Open Influence can actually let brands take it a step further and show relegate performance metrics per keyword!
For example, if an advertiser is looking for pet influencers it can not only identify influencers with pets and specifically influencers with pugs, but can rank influencers by those who have the highest engagement rates when they talk about their pugs. Clearly, this kind of data is gold for advertisers as it allows them to know which influencer partnerships will generate the highest ROI. A.I. also allows marketers to build look-a-like models, finding influencers similar to those that have performed well in the past. It can help flag influencers that are engaged in fraudulent behaviour such as buying followers or likes.
In conclusion, A.I. has made it possible to transform an industry that was extremely manual and subjective that contained only a handful of celebrities- into an industry that is objective and scalable to hundreds of thousands of influencers across various platforms and content types.