Yes, Real Data Science and ML is Coming to Influencer Marketing
In the future, many points of friction that influencer marketers experience today will be solved by advanced data science solutions
This means not only addressing ongoing problems like follower fraud and audience deduplication, but leveraging technology to streamline the core parts of existing influencer programs. For example, optimizing creator identification; content selection to help identify and predict performance; audience targeting to help scale the reach, and driving real ROI and business outcomes.
Once the underlying infrastructure is optimized, the industry will be able to deliver a holistic, performance-focused solution that can scale and become an integrated, impactful part of the overall marketing stack. Additionally, this performance-focused lens will allow influencer marketing to become a launchpad to drive the efficacy of other marketing initiatives through better insight into audience and content performance. But the only way to do this is through data science and predictive modeling.
Leveling Up Influencer Marketing
I know, for an industry that relied on screenshots for reporting just a couple years ago, the idea of applying data science and predictive modeling might seem surprising, but influencer marketing has matured rapidly. Here is a look at how data can be applied to streamline and optimize influencer marketing workflow:
Creator Identification: Over the last few years, the identification process has slowly evolved from a volume-focus to a performance-focus, where advertisers are more concerned with finding influencers that are the best fit for the brand. Now, leveraging millions of data points across performance, brand affinity, and industry alignment, brands can build a next-generation recommendation engine trained to identify the best creators for each individual campaign based on goals. And utilizing historical performance data, advertisers can then identify additional talent with similar characteristics to other high-performing creators, but in different locations for spinning up local, community-based campaigns.
Content Prediction: It’s no secret that content must be specifically crafted for individual platforms, because what resonates on TikTok is very different from what resonates on Facebook. A good way to remove some of the guesswork is by utilizing visual insights to build data science models focused on the identification and recommendation of high-performing content. For example, leveraging virtual recognition engines like Google Vision to analyze tens of millions of pieces of content, which can then inform specific visual and performance attributes detected within the content and provide recommendations around what has the highest likelihood to perform well. This approach can also be used to drive on-going campaign performance by refining content briefs and guidelines to align with existing high-performing content. For example, make more content like 'this'.
Audience Targeting, Scaling Reach: What this boils down to is brands having the ability to take a subset of high-performing influencers within a designated campaign, and then identify additional lookalike influencers based on those performing well. These can be used to create a “seed segment” that drives lookalike audience targeting, leveraging influencer data like demographics, and a variety of other factors. The immediate result to note is an exponential improvement on ROAS (Return on Ad Spend) due to improved targeting within ad platforms.
Influencer marketing has already proven to be an essential part of the marketing mix, with nearly half of all consumers depending on influencer recommendations when it comes to purchasing products. And when data science is put at the core of the entire influencer marketing strategy, it results in significant improvements in conversion data - whether that be sales, website traffic, page follows, or just overall awareness. With a renewed focus on optimization, this data-driven approach is the only way for the industry to level up.