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How Cluster Analysis Takes Influencer Marketing to the Next Level

Cluster analysis is basically examining data, finding commonalities, then grouping data points together logically based on those commonalities.

As influencer marketing continues to deliver results for brands, marketers naturally look for ways to learn from and scale their success. With so many creators speaking to so many niches though, finding the right influencers and connecting with news audiences to scale campaigns present a challenge.

Cluster analysis can help.

Why Cluster Analysis Matters to Marketers

It helps to understand the basics of how cluster analysis works but let’s be honest: as marketers, we’re more interested in how cluster analysis helps. So let’s start there.

Improved Targeting

By figuring out how audiences are similar and different, cluster analysis can help marketers identify specific audience segments that are most likely to engage with our brands. With this information, we can create audience personas that help to hone in on the right platforms, tactics, and messages to speak to these personas. 

Similarly, grouping content creator influencers into clusters, we can determine which clusters are most relevant to our brands and audience personas, further focusing our campaigns.

Smart Targeting

BENlabs AI-powered Smart Targeting uses cluster analysis to go way beyond demographics and psychographics. Smart Targeting unlocks audience “neighborhoods” and understands online behavior to surface receptive audiences, sometimes in unexpected places.

Increase Engagement

Collaborating with influencers who connect with our target audiences, our marketing messages have a much better chance of landing. The result is increased engagement leading to direct sales, brand lift, or whatever success metric makes most sense for our brand and campaign. Identifying influencers who speak to our target audiences makes it easier to create content that resonates with those audiences.

Cost Savings

Cluster analysis can help marketers to optimize our campaigns. Focusing on influencers and audiences with the best chance for success just makes sense. Even the best content falls flat if the audience doesn’t care. Working with the right content creators to speak to the right audiences shortens the path to conversion and improves overall return on investment (ROI).


When we see success in influencer marketing, the marketer’s natural instinct is to scale. Cluster analysis can help here too. Clustering content creators and audiences provides a pool of lookalikes and a clear path to scale campaign success. This is especially helpful in niche marketing as scaling with micro and nano influencers necessarily means qualifying more content creators to bring into the mix.

Smart Creator Matching

BENlabs AI-powered Smart Creator Matching uses cluster analysis to surface and stack rank content creators who align with brand values and who have your target audience’s attention.

What is Cluster Analysis?

Cluster analysis is a statistical technique that groups data into clusters based on similarities. In influencer marketing, cluster analysis can identify groups of influencers with similar characteristics, such as demographics, audience engagement, and content themes. Grouping influencers into clusters, marketers can gain insights into their target audience’s preferences and identify the influencers that are most likely to have that audience’s attention.

Types of Cluster Analysis

Broadly speaking, cluster analysis falls into two primary types: hierarchical clustering and k-means clustering. It’s useful to define each in broad strokes but any mathematicians, data scientists, and data analysts in the house be warned: you will not love this oversimplification.

Hierarchical Clustering

Hierarchical clustering looks at each data point as a single entity. It then combines the two closest data points into a cluster. It continues this process, adding to clusters where data points are meaningfully similar or creating new clusters where appropriate. The process continues until all of the data points belong to a single cluster. 

Hierarchical clustering is best suited to smaller data sets.

K-Means Clustering

K-means clustering is a method that groups data points into k clusters based on their similarities. The algorithm randomly selects k data points as a centroid, which we can think of as a sort of data nucleus. It then assigns each data point to the most like centroid. Centroids are continuously updated until all the clusters are optimized and all the data belongs to a cluster.

K-means clustering is useful for larger datasets.

All That to Say…

Cluster analysis is a powerful statistical technique that can be put to work helping marketers to optimize their marketing campaigns. By identifying influencers and audiences with similar characteristics, marketers can improve their campaigns; targeting, engagement, cost, ROI, and scale. 

And anything that helps us up our data game as marketers is a good thing.

Influencer Marketing Made Simple

The BENlabs team uses proprietary AI tools to help make influencer marketing smart, scalable, and successful. From finding the perfect content creators, automating A/B testing of content, copy and CTAs, and surfacing clear, predictive analytic insights early, when they can make the most difference, BENlabs is here to help.

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