Unearthing the bots: the importance of fraud detection in influencer marketing

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Unearthing the bots: the importance of fraud detection in influencer marketing
Influencer marketing is an imperfect science. But it’s an increasingly important strategic arm of marketing and business.
Photo by Mateus Campos Felipe on Unsplash

Detecting fraud is a serious business.

So is influencer marketing, though it may not appear that way on the surface. Detecting fraud within the influencer ecosystem is a whole other ball game. In fact, influencer fraud costs businesses an estimated $1.3 billion per year, according to Cheq. This accounts for some 15% of total influencer advertising spend lost to fraudulent activity. 

Stemming from a range of practices, influencer fraud is predominantly the purchasing of fake followers, often bot accounts, which then like and engage with posts, appearing to be an authentic interaction. This makes an account look more popular than it truly is, and businesses can be deceptively drawn to commercial arrangements with someone that actually has few or no authentic followers.

There are some easy warning signs to spot a fake influencer — a classic tell is if the account has half a million followers, but only gets a few dozen likes or comments per post. This level of interaction, or engagement as it’s commonly known, is often the key metric brands need to measure in their influencer campaigns. Without engagement, sponsored posts are being shouted into the void, which may too be filled with fake or bot followers anyway.

Data shows that only around 55% of all Instagram accounts are held by real people. And this has serious implications when it comes to the market capitalisation of the social media platforms themselves, not to mention the impact on brand collaborations and scamming trusting consumers.

But increasingly advanced anti-fraud technology is going far to expose the rife amount of fraud in the industry. Progressions in artificial intelligence (AI) and machine learning are going far to expose the widespread amount of fraud in this growing industry. 

Ultimately, businesses engaging in any kind of influencer marketing who are not protecting themselves against fraud are flushing good money down the drain.

Natural Language Processing will uncover the fakes

A sub-field of AI, Natural Language Processing (NLP) gives machines the ability to read and comprehend human languages. Drawing from the disciplines of computational linguistics, it bridges the gap between the way people communicate and how computers understand them. In influencer marketing, these complex sets of algorithms run semantic evaluation, analysing the authenticity of comments on an influencer’s account and posts.

Through cross-checking accounts for suspicious patterns and behavior, NLP can successfully detect bots and other low-quality accounts. It also has the ability to detect if engagements on an individual or mass scale are authentic or not. This brings enormous value back to the commercial aspect of the influencer and brand relationship, and as a result, to the influencer marketing industry itself. 

Unexplored realms ripe for the right technologists

As a technologist or anyone interested in the AI space, influencer marketing represents a neglected field, and one teeming with possibilities. As bots and scams grow in sophistication, so too will the technology required to stamp out fraud and keep the industry honest. From travel influencing to hospitality and fashion, there’s a whole legislative and regulatory landscape starting to form around this industry, and the technology that underpins it will be key to its success.

Influencer marketing is an imperfect science. But it’s an increasingly important strategic arm of marketing and business, and a lot of livelihoods ride on its authentic success. With the correct technology, businesses and consumers can be protected from the lucrative bots that threaten the integrity of the entire industry.

Alexander Frolov, CEO and Co-Founder of HypeAuditor.

Copyright © BIT (Business IT). All rights reserved.

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