How To Evaluate Influencer Marketing Platforms That Use AI Technology

Sep 16, 2019 | Social Media Marketing | 0 comments


What actually constitutes artificial intelligence in influencer marketing? As providers rapidly innovate their solutions, marketers should become familiar with the intricacies (and buzzwords) of influencer marketing tech.

First, let’s talk vocabulary. We see the terms “artificial intelligence,” “machine learning” and “algorithms” used throughout marketing materials, but what do they actually mean?

Artificial intelligence (AI) is when a machine can do things that typically require human intervention (decision-making, speech recognition, visual perception, etc.). Machine learning (ML) describes a subfield of applied statistics that, when paired with standard IT algorithms, lays the technical groundwork for AI (for example, image recognition, natural language processing, artificial neural networks).

In influencer marketing technology, a single process like influencer identification may combine simple statistics (such as engagement metrics), machine learning applications (to identify whether the profile is a person or a company page based on image recognition, for example) and some AI algorithms (to decide whether or not that profile is influential based on posting style, performance history and the sentiments expressed in followers’ comments).

That would be an ideal process anyway. But buyers should be aware of how these terms are often used interchangeably (in many industries, not just influencer marketing). As CEO of an influencer marketing company myself, I’ve come across many potential clients and brand marketers who aren’t aware of the differences between these terms or how to determine if their prospective provider offers the solutions they’re seeking.

So, with these distinctions in mind, let’s break down one of the more “trendy” features offered by influencer marketing platforms today: AI-assisted influencer recommendations.

Influencers aside, what is an AI-assisted recommendation? Consider Amazon’s product recommendations: “If you liked X and Y, you’ll love Z.” This type of cross-recommending becomes more powerful as it learns what the customer searches for and, more importantly, settles upon.


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