Why is it that automation has failed to take off so far? At the most basic level, algorithms require a significant amount of data input to effectively predict and automate tasks, and previous attempts at automation have only scraped the surface of capturing all possible data.
To meet this goal, we must begin with predictive analytics, such as algorithms that can predict story views and identify key influencers based on predetermined criteria. By first focusing on predictive analytics, agencies can minimize the guesswork involved with a brand and creator collaboration and provide an educated estimate of the ROI of a campaign as well as fuel attribution.
With strong predictive analytics models in place, the influencer marketing industry must continue developing algorithms and conducting regular post-mortems to improve AI capabilities. That feedback loop is critical for ensuring that automated processes continue to become smarter and more effective over time.
To effectively address this challenge and build AI that effectively captures the nuances of human creativity, we must develop collaborative AI. This will require a best-in-class team working alongside algorithms to correct mistakes and ensure that processes can scale quickly. When scaling AI, models can easily become oversaturated and fail to distinguish platforms, patterns and content opportunities.
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