Two years ago, we wrote about How Machine Learning Transforms The Way You Discover Influencers, and how CreatorIQ’s approach to intelligent creator discovery and recommendations helps turn a largely manual search process powered by complex filters and keywords into a scaled process built on inbound reviewing, screening, and vetting.
While all of this still rings true today, a lot has changed in two years. The rise of mainstream AI and machine learning tools like ChatGPT, and the increased prevalence of AI offerings from SaaS technologies can save you time and effort in your everyday work life.
However, generalized GPT integrations or outputs will only go so far. It’s our belief that machine learning models only work well if applied specifically to solve real challenges experienced in your workflow. When it comes to creator discovery, the upside potential is massive if the downside pain is well understood.
Let’s take a look at the most common challenges faced when embarking on new creator discovery, and the technology we’re employing to solve them.
Better.
Creator Discovery needs improved quality and usability of creator data, and increased results relevance within large databases.
Having access to all of the creators in the world isn’t helpful if the data isn’t comprehensive, up-to-date, and actionable. In this case, actionable is the key. One of the ways to make this data actionable is to use it to surface more relevant results when inputting search terms and receiving an output of creators.
The current reality of many Discovery databases is that search results present significant challenges and limitations. As a result, finding the right creators can be time-consuming and less effective. Many databases miss out on high-potential creators who may not match keywords even if their interests and style are relevant. Overall, these search limitations hinder the efficient, effective discovery of creators who align with your brand.
So what makes searching for creators in CreatorIQ’s Discovery database different today than it was two years ago? The use of AI and LLMs (large language models) to power semantic searches using natural language processing.
Faster.
Natural language processing enables consumer-grade quick searching.
To date, most creator databases have relied on keyword matching through Elastic Search. This is a useful approach, but as mentioned above, this method has its limitations when it comes to accuracy and effectiveness.
Semantic search is a data searching technique that uses the intent and contextual meaning behind a search query to deliver more relevant results. Powered by natural language processing (NLP), semantic search processes, analyzes, and returns results based on better criteria than traditional search technology.
All this is made possible by large language models (LLMs). LLMs are recent advances in AI technology that focus on human languages to train and learn. They can understand and often generate human-like text—think ChatGPT.
Semantic search allows us to go beyond simple keyword matching and enables searching based on the meaning and context of the content. By leveraging semantic search, we can provide more accurate, relevant search results that match specific interests, demographic, or content requirements.
This style of search not only helps you discover a broader range of relevant potential partners and explore new opportunities— it helps you do so quickly. Semantic searching enables quick searching, so you can type in what you’re looking for exactly as you would type it in everyday speech. No more toggling filters, crafting boolean searches, or wasting time on trial and error terms in your search bar. See it in action below.
Smarter.
Contextual semantic models power smarter creator recommendations.
Let’s build upon what semantic modeling allows us to do. Not only does it power your outbound discovery with new quick searching, but the same foundation can be used to deliver you creator recommendations with improved relevance. This means you’ll be able to find more creators who fit your criteria, all while AI-based recommendations surface similar creators to those you already know and love.
The recommendation engine takes a 360-degree approach to creator evaluation, analyzing content, topics, mentions, keywords, and demographics like age, gender, and location for both creators and their audiences. Relevance is weighted, so the first thing you’ll see are the creators most similar to the creators you input.
Using similar creators as a standard step in your discovery and evaluation practice can help you reduce the time spent on searches. As a result, you can redistribute your energy towards screening and evaluation, helping you get to the outreach and recruitment phase faster. Additionally, you can leverage recommendations if you’ve found a handful of ideal creators from a recent search you really like, or even input creators from your network or a recent campaign who have proven to be successful partners.
When you use existing partners as your inputs, you’ll not only fast-track your creator discovery, but assemble a set of recommended creators who are highly likely to become strong partners for your brand.
Safer.
Built-in visual cues help screen creators and quickly identify issues.
Identifying more relevant creators faster is great—but it’s only half of the required work before these creators can be added to lists for recruitment and activation. On the other side of creator discovery comes creator vetting. That’s where you’ll need to spot check that each creator’s data, content, and health aligns with your brand guidelines and doesn’t pose a risk.
While creator vetting will always require a human eye, creator databases can help speed up your screening process by flagging content or information worth your attention. In CreatorIQ, capabilities like Active Audience Score, the Social Timeline, and audience data breakouts help paint a complete picture of a creator’s profile, which you can use to screen creators before moving them along in your planning process. However, meeting brand suitability guidelines may not always be enough, which is why customizable brand safety and compliance capabilities can play a more active role in your discovery screening process.
When it comes to safety and fit, every brand’s needs are different. You may be a family brand with specific brand safety needs, or you may be a brand with outlined suitability guidelines to ensure that your partners don’t land you in hot waters on the internet discourse.
Customized brand safety is where the value of keywords still comes into play. Similar to in-flight campaign brand safety monitoring, you can set up your own keywords based on your vetting rules, then run brand safety checks whenever you want. These checks automatically flag creator content in Discovery and CRM for easy visual identification and quick review, and are applied to content dating back three years.
In an age of immense technological advancement, access to troves of creator data points, and the actionable processing and translation of that data, is absolutely paramount to making work easier. CreatorIQ is dedicated to bringing our customers the best and most innovative solutions to creator discovery, and all of creator marketing, by leveraging AI and machine-learning models for thoughtful application within our platform.
For tips on how to use Al for creator discovery