Influence marketing’s problems can be solved with a machine-learning solution
People subjectively picking influencers who will best represent their brand is risky, argues Social Soup's Sharyn Smith. Instead, the world of influence marketing should turn to machine learning.
Social media influencers have become a common tool in a marketeer’s toolbox to reach current and potential customers, building awareness, third-party credibility and purchases.
But the rapid growth of this channel (in little over 10 years) has brought with it significant debate on its effectiveness and ethics. And those criticisms warrant the introduction of machine learning.
Criticisms levelled at the industry and individual influencers have included: a lack of transparency of the effectiveness of influencers, unfilled promises made by those representing influencers, and a lack of knowledge among in-house marketing teams on how to use influencers for their brand effectively.
The industry has certainly matured over this time, with steps being taken to address these challenges. But identification and selection of an influencer from the hundreds of thousands that exist remains predominantly based on human assessment.
This poses a significant a risk to brand marketeers considering engaging with an influencer community.
It presents a risk because the current type of human-only assessment leaves a marketeer vulnerable to subjective selection and partnering with an influencer who may not be the best fit for the brand. This result is a wastage of marketing dollars through poor ROI. Or, at worst, it opens the brand up to risks due to the conduct of that influencer.
A solution to this ongoing issue is on the way though, with the advent of new technologies including machine learning and artificial intelligence (AI).
With the ability to review and assess millions of pieces of data – in this case, social media content – quickly and without bias, brands are now able to use technology, such as machine learning, to dramatically improve their effectiveness and efficiency.
The result will see the selection of an influencer more closely matched to the brand across a wider range of measurable criteria. An influencer that has a stronger alignment will result in the content being more authentic and, ultimately, more effective.
In the future, I predict machine learning will evolve beyond assessment, currently based on programmed criteria, to a position where it is using AI not just to assess, but to recommend, further criteria to review in order to improve the influencer’s appropriateness. The algorithms will learn over time which criteria performs better for your brand as we create positive feedback loops.
After a decade of growth in the influence space, we are about to see a new era where technology takes the lead, removing for marketeers the risks associated with the channel, and demonstrating to the rest of the marketing world that influence marketing is here to stay.
Sharyn Smith is the founder and CEO of Social Soup
topics
ai
artificial intelligence
influence marketing
influencer marketing
Machine Learning
Sharyn Smith
Social Soup
Fantastic read! Was this article written using machine learning?
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