The word affinity has a wide range of meanings. Its core meaning is a relationship by marriage according to Merriam-Webster. But it can also mean a likeness based on a relationship or causal connection.
Which is to say the spectrum of the relationship can be strong. Or not so strong.
I have an affinity for bourbon. But I also have an affinity for Hanson’s 1997 album Middle of Nowhere. I can live without one of those, so the affinity is different.
And that is the underlying factor a good influence marketing strategist needs to keep in mind about using affinity as a filter for influencer prioritization. If you’re not familiar with affinity and how that applies, let’s take a quick look at it, what it means, and how you can use it.
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What is Influencer Affinity?
Like the definition says, an influencer’s affinity is the likeness or commonality he or she has with your brand or your content. You can also look at affinity as likeness or commonality between your audience and the influencer’s. The more alike any of these are, the better your chances of the influencer, or his or her content, resonating with your audience. Or you brand or content with theirs.
To explain it at a very high, visual level, I like to use the Instagram Mood Board test to see if a given creator’s content there aligns with that of the client or brand in question.
Let’s say I’m a men’s accessory brand (watch, jewelry, eyewear, electronics, or even bourbon). My target audience is discerning men, aspirational to high-net worth, but in their 20s and 30s. I’m looking for potential influencers on Instagram and find @jackson_krecioch.
When I look at a snapshot of his Instagram posts, I get a sense of his content. A mood board, if you will.
Now I find Caesar Chukwuma. (Esquire, even.) His mood board looks quite different. More elegant. More up-scale, if you will.
So if I’m Manssion, which of the two looks more like me?
Now, this is a high-level, directional indicator. It is not a deciding factor. But the point is there is a higher degree of visual affinity between Manssion and Chukwuma, and thus a more likely connection to his audience.
The same thinking can be applied to other factors about the creator and compared to your brand. We do it quite naturally with demographics. His or her audience needs to look like ours … or at least the one we are striving to reach. Where the industry is starting to gravitate is content affinity.
How to Determine Content Affinity
Content affinity refers to the level of commonality one content stream has with another. My content stream is going to have a high level of affinity with Gordon Glenister’s. He is also an influencer marketing podcaster and author who posts a lot about the industry and practice.
My content stream is going to have some level of affinity, but not a great deal, with Alicia White (@bourbonsipper on Instagram) because I post about bourbon there sometimes.
My content stream doesn’t have much level of affinity with people who post about beekeeping. Or marathon running. Or quilting.
Tagger, the presenting sponsor of my podcast, has a proprietary algorithm it has developed to determine and present affinity within its tool. Other platforms have similar features, but Tagger’s has always seemed more precise and advanced to me. (Incidentally, you can read Kelsey Formost’s recent explanation of Affinity on AdAge. She’s Tagger’s content director and amazing to work with.)
You can look at a brand’s profile and see other brands or even individuals whose content and audience show high affinity. You can also look at an influencer’s profile and find similar influencers, then brands whose content aligns as well.
Typically, affinity algorithms are driven by an analysis of the keywords and hashtags used by a given influencer or brand. Match those occurrences with similar results in other accounts. The higher the percentage of commonality, the higher the affinity.
But you can go deeper. You can look at the percentage of a given content topic in someone’s content, then cross-check the engagement level for that type of content. Now overlay that with similar information from other influencers or even brands. Now you have a list of potential influence partners who not only post about the same topics, but drive similar (assumingly high) levels of engagement around that topic.
To restate that a little more clearly: I can now find influencers whose audiences get excited about the kind of content my brand wants to create with them, versus just ones with a minimal level of engagement rates.
Now you can start to see where affinity can be a super filter.
What’s the Catch with Affinity?
The problem with any data-only filter is the lack of humanity in the decision. Choosing only creators whose affinity with your brand is above a certain level on some algorithm’s analysis only means you have a better chance your partnership will resonate.
According to Tagger, I rate high on the affinity score for USAA, the financial services company for military personnel and their families. They also rate pretty well on mine if I reverse the analysis. But I have very little in common with that brand other than my Grandfather served in World War II and I know someone who used to work there in marketing.
If USAA reached out to me, I would be hard pressed to find a path of relevance for my audience, or enthusiasm for a financial services product. (Though if they want to assume my credit card debt, that could change. Heh.)
For a better example, if Bose (the speaker/audio company) reached out to one of the top affinity matches for its brand, it would be seeking a partnership with Craig. Who takes pictures of seascapes and his dog. (But his instagram handle is @headphone.)
Affinity is a deeper, smarter way to analyze the data points that help you decide who is more right to work with. The likelihood of success with someone whose affinity measures stack up better than others is probably higher. But just because there is affinity overlap, success is not guaranteed. Affinity is one thing that matters in the execution. But content, transparency, honesty, appeal, incentivisation and many, many more factors contribute to the audience actually responding.
Machines and data analysis are magical while at the same time being magically flawed. Sure, affinity analysis can be a super filter to help you get to a point of decision-making. Just don’t let it make the decisions for you.
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The original version of this content first appeared on jasonfalls.com.