Digital marketing has reached a stage where professionals have to process a huge amount of data daily. This is impossible without artificial intelligence. Every tool on the market uses machine learning, algorithms, and other technological achievements to some extent. HypeAuditor is no exception.
Keeping our mission in mind to make influencer marketing as transparent and efficient as possible, the HypeAuditor platform started implementing AI years ago. In this article, you get familiar with our platform’s features that are enhanced by the latest developments and we share with you what technologies we use in them.
Calculating Audience Quality Score (AQS) with AI
Although AQS is an integral part of the Instagram and TikTok creator reports, we discuss it individually because it is one of the most valuable metrics brands use to check an influencer’s quality.
The AQS is HypeAuditor’s proprietary metric. It uses AI to analyze the audience and engagement authenticity of an Instagram and TikTok influencer. The metric ranges influencers’ account quality from 1 to 100, 1 being poor and 100 excellent.
For Instagram creators, AQS is calculated by checking their engagement rate, the percentage of real people among their followers, their followers and following growth, and the authenticity of comments. TikTok influencers’ AQS is counted by considering their follower growth in the past 30 days and engagement and comment rates.
We rate influencers with an AQS of 90 or higher as ‘Excellent’. Those with a score between 80 and 89 are marked as ‘Very good’, and those between 60 and 79 are labeled ‘Good’.
Creators are rated as ‘Average’ if they score between 40 and 59, and ‘Could be improved’ if their score is between 25 and 39. Lastly, those with a score lower than 25 are labeled as ‘Low’.
AI-Assisted Influencer Report Creation
We use numerous AI technologies to analyze influencers and create a detailed report on them.
Audience and engagement authenticity
We measure and check engagement and audience authenticity using numerous methods. In terms of audience, we check images and avatars with computer vision AI. We examine bios and the nature of comments the content received. Our machine-learning models are trained to analyze over 53 patterns on accounts. We detect 95% of fraudulent activity successfully and flag bot accounts as suspicious.
Influencer fraud is still a controversial aspect of influencer marketing and it holds back many companies from engaging with it. For reassurance, HypeAuditor is an industry leader in fraud detection. We apply machine learning algorithms to compare the account’s historical engagement and follower growth data to its current activity. Dramatic shifts in the followers’ numbers or audience interaction can signal the use of automated services.
Our algorithms analyze the relevancy and quality of comments as well. Short generic comments and the overuse of emojis are major red flags, as well as comments that don’t align with the post’s topic.
Audience demographics
Machine learning and computer vision analyze users’ bios, names, and posts (including images.) They help us define the users’ gender and location, and estimate their age. Language detection models identify their language. When we define an interest category for a user, we take into account their interactions with other accounts.
Sentiment analysis
Our AI-driven sentiment analysis feature studies users’ comments and groups them into three categories: positive, negative, and neutral. We mark comments positive if they display happiness, joy, and agreement with the discussed topic. We classify comments as negative if they show sadness, anger, or disagreement with the post. Finally, we categorize comments as neutral if we can’t link any emotional meaning to them.
When examining comments, first a language detection model identifies the language. Then we analyze the users’ feedback with Natural Language Processing (NLP). NLP determines the probability of whether the comment is positive or negative. Besides, we use an emoji sentiment detection model to improve the detection of comment qualities.
Estimated prices
We provide price estimates for influencers based on their location, follower size, and engagement. We use machine learning trained on market values to calculate a price range for different content types, such as posts or stories for Instagram. The real price - of course - can vary depending on the length and complexity of the collaboration.
Advertising performance
We use machine learning to compare the engagement performance of posts without brand mentions to those with mentions. Advertising performance allows businesses to understand how the influencer’s promotion would perform in a collaboration.
Brand safety
A brand safety analysis entails the careful examination of an influencer’s posts. It detects any harmful, risky, or dubious content that may pose a threat to a brand if they work together.
Text: A brand safety analysis uses NLP to understand the meaning and context of a post. NLP breaks down the whole text into words and analyzes the relationship between them. During this procedure, it recognizes names, objects, locations, and organizations. This is crucial to identify texts that target specific people, or groups in a negative manner.
Emotions: The content’s sentiment analysis enables us to discover negative sensations - in some cases hostile emotions - such as hate speech, violence, or bullying.
Imagery: We use computer vision AI to assess photos, images, and videos for explicit content or graphic violence. With this technology, we are able to recognize weapons, blood, nudity, etc. Advanced computer vision comes in handy to identify manipulated or deepfake images.
AI-Driven Influencer Discovery
Filters: Our AI-based discovery tool enables businesses to refine their searches based on multiple factors. Due to NLP, we can determine the influencer’s and their audience’s language and category. A computer vision analysis of their avatars and images enables us to define their interests and gender. AI technology helps us establish creators’ countries along with their followers.
Niche search: We use the above systems for our recently introduced niche-based search. In contrast to keyword searches, which are word-sensitive, niche searches evaluate both text and imagery. Thanks to Natural Language Processing we successfully identify creators who use synonyms. Plus, with NLP we manage to find niche influencers in different countries. We examine images for relevance to the niche.
Influencer Lookalike Tool
HypeAuditor’s Lookalike tool uses AI and machine learning to evaluate an influencer’s content, category, engagement, and audience demographics and behaviors. Based on the accumulated information it creates a template. This template then serves as a basis for finding comparable influencers. An influencer lookalike tool can help brands scale their campaigns. They can identify similar creators in other countries or smaller influencers in their region.
AI-Enhanced Media Plans
Launching a campaign without knowing its effectiveness is a huge risk even for a seasoned team. To receive the necessary financial resources from stakeholders, the marketing team must collect a lot of data and make calculations. The Media Plans feature takes the workload off the team’s shoulders and performs these tasks. Hence, it allows businesses and marketers to gauge how efficient and profitable their influencer marketing campaign will be. We use predictive AI to calculate critical KPIs such as EMV, ROI, CPE, and CPM along with ER and reach of course.
Check back regularly because we constantly introduce new tools and features and improve our current ones.
