Influencer marketing is growing like wildfire. The market has more than doubled since 2020 and nearly eight out of 10 US marketers expect to use influencer marketing campaigns in 2023, according to Statista.
B2B companies are no stranger to influencer marketing, but for this article we’ll focus on B2C influencer marketing campaigns where consumer brands partner with popular social media influencers (mostly on Instagram and TikTok) to post engaging content about the brand’s products. Generation Z, in particular, relies on social media influencers when making purchase decisions.
One critical aspect of B2C influencer marketing campaigns is how much of a risk they are if they’re poorly executed. Choosing the wrong influencer (ahem, Kanye West) can cause serious reputational harm and not measuring whether a campaign actually succeeded is a waste of marketing budget.
While popular brands including Dunkin’, Sony PlayStation, Absolut, Pepsi and many others regularly run influencer marketing campaigns, challenges still persist for brands, including:
- Finding appropriate influencers for their target audience
- Confirming that the influencers they’re targeting are not scammers
- Measuring how well influencer marketing campaigns performed
Artificial intelligence (AI), with its ability to mine social media data at scale, make predictions and calculate performance metrics, has emerged as a way to address influencer marketing campaign challenges. This year, 63% of brands plan to use AI in executing their influencer campaigns, according to Influencer Marketing Hub’s 2023 benchmark report.
Let’s delve into the ways that AI technologies, including machine learning (ML) and natural language processing (NLP), can merge with human creativity to make influencer marketing campaigns more efficient.
Finding the Right Influencers Using AI
Selecting influencers based solely on broad categories (Fashion! Fitness!) and vanity metrics such as likes and shares is risky, as it can lead to partnering with ill-fitting or, worse, fraudulent influencers.
In the influencer discovery phase, it is better to be as specific as possible about an influencer’s audience and areas of expertise — not just “fitness” but “running” and more specifically “marathon training.”
This is where AI technologies help, as they enable brands to collect detailed audience demographic data such as age, gender, location, marital status and interests to gauge if the influencer’s audience is a good match for your brand.
AI algorithms can also scan the actual content of hundreds of thousands of influencers to identify which ones post about topics relevant to your brand and have consistently high engagement rates.
AI tools can then dive deeper into the influencer’s posts with detailed content tagging using the following techniques:
- Image recognition: Social media is a visual medium. There’s often little to no text included in posts to guide you. AI’s image-recognition technology can scan an image or video and pull out demographic and contextual data — i.e., a location, product or person within the visual — to understand why an image or video drove engagement.
- Lookalikes: AI tools can analyze influencer topics — through mentions, hashtags and image recognition labels – and weigh those topics based on frequency and engagement rates. AI tools can then find similar influencers who have performed well in the past.
“To identify the ‘correct’ influencer match for a brand, influencers need to be tagged and analyzed objectively,” said Neal Schaffer, a marketing consultant and author of the book “The Age of Influence.” “And that tagging needs to be based on the elements in their actual content rather than on subjective categories or keyword searches.”
AI-powered influencer marketing platforms that specialize in finding authentic influencers:
Spotting Fraudulent Influencers Using AI
No one is immune to fake followers and fabricated engagement rates. Hugely popular brands such as Ritz-Carlton, DSW and Neiman Marcus have been exposed to fraudulent influencers.
Influencer Marketing Hub research states that 67% of brands are concerned about influencer fraud. In addition, fraudulent influencers with 100,000 or more followers are costing brands a loss of $300 per post, according to a 2019 research report from marketing cybersecurity company, CHEQ.
Learning Opportunities
AI intelligence tools help stay ahead of influencer fraud by doing the following:
- Analyze an influencer’s profile for red flags such as sudden spikes in followers, followers with no bios or profile photos, or large numbers of followers from the same country or age group.
- Scan for engagement rate anomalies such as having thousands of followers but few likes, comments and shares on their content, a telltale sign of fake followers.
Unfortunately, fake influencers undermine genuine brand-influencer partnerships through manipulation and it behooves all marketing teams to weed out scammers to ensure their brand is only reaching real people.
AI-powered influencer marketing platforms that specialize in fraud prevention:
Measuring Influencer Marketing Campaigns
Once a campaign is underway, AI-based tools can be used to measure how well a campaign performs in the real world.
Because influencer marketing is still in its early stages, brands are still figuring out how much to spend on influencers and what performance metrics to measure. AI tools can make campaigns more measurable in the following ways:
- Prediction: AI tools can analyze other brand campaigns that an influencer has been associated with and predict how well a campaign with your brand will perform. This helps reduce the trial and error of finding the right influencer.
- Sentiment: AI can monitor comments for sentiment analysis using natural language processing (NLP). This determines how an audience is reacting emotionally to an influencer campaign, whether positive, negative or neutral.
- Performance: AI tools can gauge engagement data (likes, comments, shares); reach and impressions; and conversions (downloads, sign-ups, sales). AI tools can also identify patterns in influencer campaign data such as content types that get the most comments or post times that get the most views. It will then suggest ways to build on these patterns to improve campaigns.