Written by Alena Jain
In 2022, the influencer marketing industry expanded to a staggering $16.4 billion from $1.6 billion in 2016. This extensive growth can be largely attributed to the rise in short-video platforms during the global pandemic, fueling data collection leveraged for social media marketing. Brands are adopting various strategies to stay ahead of the curve. The question thus arises, what kind of strategies are brands looking at?
In an article published by ET Brand Equity, a statistic revealed that approximately 39% of brands in 2023 chose nano-influencers (1,000-10,000 followers) as their preferred partners, followed by 30% opting for micro-influencers (10,000-100,000 followers). This indicates that as consumers increasingly seek genuine experiences and trust, partnering with micro-influencers provides brands with a more authentic voice. Today, partnerships with micro-influencers are a leading marketing strategy in the world of communications.
The second strategy is AI-driven brand influencer matchmaking. With the help of artificial intelligence and machine learning, brands can also automate various aspects of influencer marketing campaigns, such as contract negotiations, content approvals, and performance tracking. Approximately 56% of brands prefer joining hands with the same influencer across different campaigns, further proving the necessity of reliable matchmaking.
According to a study by Oracle and Brent Leary in 2022, as many as 37% of consumers trust social media influencers over brands, with 28% of the study participants stating they discover new products and brands through influencers. Brands continue to adopt highly accurate predictive analysis processes to examine brand reputation and user engagement.
Machine learning algorithms can accurately predict the success of influencer-brand collaborations to identify the most promising matches. Along with periodic content performance evaluation and past campaign results, brand and influencer sentiment analysis ensures accurate matchmaking that fits with both the influencer as well as the brand’s values and audience.
Feedback loops for machine learning are an emerging trend when considering marketing from the lens of influencers. Brands highly benefit from such feedback loops, accurately delivering more effective and efficient recommendations for influencer collaborations. This enables established algorithms to continuously learn and adapt by collecting and analyzing real-time campaign data.
The realm of influencer marketing is transforming, driven by the dynamic interplay of consumer preferences and cutting-edge technologies. Will brands continue to compete with this changing landscape in the world of marketing?