The science of using influencers made simpler by data

Influencers aren’t going away. But data could make working with them easier.

The science of using influencers made simpler by data

People have been too quick to sound the death knell of influencer marketing, arguing that public trust in personalities will decline. Gartner, for example, has predicted that “by 2023, CMO budget allocation to influencer marketing will decrease by a third as consumers continue to lose trust in brands and entities they don’t personally know.”

Failures of influencer marketing have garnered plenty of attention, like the disastrous 2017 Fyre Festival, an event that had been touted by social influencers such as Kendall Jenner, but never actually happened.

Yet influencers continue to prosper. BigCommerce recently estimated that 65% of influencer marketing budgets will increase in 2020. That’s the direction this space is headed.

At the high end, personalities can earn hundreds of thousands of dollars for a social update. Influencer marketing may have a lower profile in the b-to-b space, but it’s still a significant business segment, perceived as conveying a brand’s voice with more authenticity than advertising. Of course, b-to-b influencers are more likely to be peers and practitioners than celebrities.

Influencers have a role to play in PR campaigns, just as much as in marketing campaigns. The right influencer can often reach a target audience more effectively than a journalist, especially if their messages are targeted through social channels to followers who are highly likely to be interested in, and responsive to, the message.

It’s almost too obvious to say that finding the right influencers is both difficult and essential. What is less obvious is that the science of identifying and driving maximum impact from influencers has made big strides over the last couple of years. Like most other business initiatives, the influencer space is now data-driven, or, at least, it should be.

Here are some examples of important questions that data can help to answer:

  • An influencer may have a large audience, but is it long-standing and loyal?

  • Is an influencer relevant to the topic of the message?

  • Is an influencer relevant to the intended audience for the message?

  • Is an influencer in concordance, not only with a brand’s message, but with a brand’s values?

Considerations like the above can actually be scored, giving potential influencers an actual numerical value. There are practical considerations, too: impact is going to be partly a function of available budget. A network of “micro-influencers,” influencers with a small but highly relevant following, is likely to be a less expensive proposition than a big-name national influencer. But depending on objectives, it might also be more effective.

The good news is that there are platforms that help with the heavy lifting when it comes to influencer data. GRIN, Klear, and Traackr are just examples of platforms that filter and find influencers and provide support in managing and paying them, too.

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