Interviews, insight & analysis on digital media & marketing

How AI is refining influencer discovery

By Jenny Tsai, CEO of WeArisma

With the rise of social media and influencer culture, there has been a shift in the way consumers make buying decisions. People used to turn to search engines such as Google to search for products they were interested in purchasing and make a decision based on reviews, articles and brand websites. But now, consumers are increasingly discovering brands they purchase on social channels, especially younger generations. Today, the average UK consumer makes 10 purchases on TikTok each year, rising to 19 for Gen Z.

These changing habits have catapulted the importance of influencers as a key marketing channel to boost brand visibility and discovery, and to build vital credibility and trust among target audiences. However, to gain optimum results and return on investment, it is important to be strategic in influencer selection.

Considering there are more than 64 million influencer accounts on Instagram alone, the days of marketers manually searching for and compiling influencer databases are long gone. Now, advanced analytics and AI sit at the forefront of influencer strategy, ready to put forward influencers that are most likely to make a tangible impact on a business.

Finding the perfect match

The scale and diversity of the online influencer community can make finding the right partner challenging. With millions of different influencers with various skills and different levels of following, brands need to ensure an influencer’s values align with those of the brand and that the partnership doesn’t introduce risk to a brand’s reputation or safety.

A-list celebrities bring value to brands due to their personal brand recognition and wide-reaching audience. Let’s take a look at WeArisma‘s proprietary data analysis from Milan Fashion Week where a single Vogue reel of Prada’s red carpet, featuring A-listers such as Kylie Jenner, Scarlet Johansson and Hunter Schafer, generated an impressive media value of $736.7k for Prada.

Celebrity partnerships, however, may not be the obvious choice for many brands. Micro-influencers’ audiences may be smaller, but they are often much more engaged and invested in a particular niche and the communities they are building. For example, Paul Costello took the niche-influencer route at London Fashion Week and Eve-Lily’s Instagram reel earned a strong 8.6% engagement rate, proving the impact of the smaller, more engaged audiences of niche influencers.

Getting the most out of influencer investment

As the data shows, both approaches hold value, but knowing which type of influencer best suits a brand requires analytics and insight. Laborious manual data-crunching, however, is a huge drain on a marketing team’s time and resources, leaving less space to devise creative activations and nurture relationships with influencers.

Instead, marketers are now using specialised influencer analytics platforms to refine the search for influencers based on sophisticated criteria, ranging from previous product and brand collaborations to KPIs such as engagement rates, allowing them to make strategic, data-driven decisions on influencer partnerships, leading to longer and more fulfilling relationships.

Using data analytics to assess relevant factors can also find previously unknown high-performing influencers who otherwise may have been overlooked. It also gives back time and headspace to marketers for the creation of stronger, more successful relationships for both brand and influencer, and more broadly, to create exciting campaigns.

Rich metrics map road to success

Measuring the impact of an influencer campaign is critical in assessing how effective the partnership is in elevating the brand. Traditionally, these have included metrics such as engagement rate but AI and analytics enable marketers to gain much richer insight into the performance of a partnership, and this should be leveraged as much as possible to draw learnings for future collaborations.

Delving beyond surface-level statistics allows marketers to assess whether the partnership is effective in driving commercial impact, brand loyalty and affinity. By using AI and advanced analytics to analyse granular details of the brand partnership, marketers can gather tangible data to inform and optimise future activations and adapt to ever-changing consumer behaviours.


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