How to use AI to Scale your Influencer Marketing Campaigns

December 20, 2018

By Ricky Ray Butler

alashi/Getty Images

Over the last several years, influencer marketing has established itself not only as a viable form of marketing for brands, but as a growing marketplace. In 2017, the influencer marketing industry was worth two billion dollars, and it's predicted to be worth 10 billion dollars by 2020. Another study found that 94 percent of marketers found influencer marketing to be effective.

Despite this growth, what marketers fail to grasp is the speed at which influencer marketing is evolving. This accelerated evolution can be explained in part by new emerging platforms, the glut of content flooding the space and, subsequently, consumers' rising consumption habits — over four billion YouTube videos are viewed a day, while Twitch boasts over 15 million daily active users. Where does this leave marketers trying to integrate themselves into this abundance of content and, further, those trying to determine the true impact that influencer marketing can have on their brand?

The answer: Artificial Intelligence (AI). AI has been on the tongues of marketers for years, but many are unsure how or what to make of the technology when it comes to influencer campaigns. Marketers may assume AI is a tool used by primarily businesses to help diminish menial tasks, but that is a narrow view of AI's capabilities. In reality, applying AI to the influencer marketing space goes well beyond alleviating menial work. As audiences consume more content across different platforms, more data is generated, but not data in the traditional sense marketers are referring to — followers, views or engagement — rather, unstructured data, or the extensive information embedded into the content itself.

According to Datamation, 80 percent of all enterprise data is unstructured and is growing at a rate of 55 percent to 65 percent per year. A robust set of analysis that can capture and understand structured and unstructured data is essential for marketers. This type of advanced AI — deep learning neural networks — is the only way for marketers to truly analyze all content that's available. This is no small feat for one company to take on by themselves, and scale cannot be achieved without the right resources.

AI has the power to analyze millions to billions of terabytes of data, creating sophisticated and comprehensive deep learning models trained to predict influencer campaign performance. For example, when examining a spoon a human may only catch a certain number of nuances — color, size, shape (length, weight), or material — while AI could understand the object from thousands of various perspectives, including analyzing a person's face while holding a spoon and the emotional implications that might have on viewers.

In the case of influencer marketing, some creators have a "gut" feeling on what will perform well. However, in using deep learning neural networks, marketers can more quickly identify the nuances and patterns of "successful" campaigns to predict the success of other campaigns. Maybe certain colors evoke emotional responses, some more positive than others. Through the intelligence provided by AI and custom deep learning neural networks marketers can scale these queries across thousands of campaigns to increase the effectiveness and ROI of their own influencer marketing campaigns.

The reality for marketers is that not all AI is created equal. When looking for platforms that address scale — both the volume of content being created, and the amount of content brands wants to sponsor in their campaigns — marketers need to look beyond solutions that are designed only to provide influencer matching or to address brand safety. Instead, it is time to focus on neural networks that leverage the untapped potential of unstructured data to generate real insights and, ultimately, predict campaign success.

 

Ricky Ray Butler is CEO at Branded Entertainment Network.


The views and opinions expressed in Marketing Maestros are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.


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