Influencer pricing: size over substance?

There is huge variation in the amount influencers get paid for sponsored content. Even influencers with the same number of followers can get paid wildly different amounts.

Various factors are taken into consideration, but without doubt the key variable is account size. Influencers with more followers have the potential to reach wider audiences; their posts have the potential to drive more engagement, more conversions and ultimately, more sales. They therefore justify a higher price.

Attributing value all the way along this chain from potential reach to sales is tricky to impossible. Follower counts are an easily accessible but misleading indicator for potential future performance.

In this post we discuss why using follower count is not the best way to determine how much to pay an influencer and propose a more data-driven alternative.

Misleading followers

Follower counts are used to guide judgements on how much to pay influencers. The implicit assumption is that more followers means more audience engagement, which means more sales. But there is huge variation in the engagement performance between influencers who have the same account size.

mtcars (1).png

This chart shows that average engagement performance for influencers at each size category can vary hugely, and that variation increases as the account size category gets larger. The green box shows the span of average performance for influencers in each size category. The bulk of influencers perform  within this range. The horizontal black line shows the (median) average performance.

This also varies by vertical, as some influencers perform better in some verticals than others.

Engagement rates

Some planners can and do get hold of average engagement rates for influencers. Engagement rate is (usually) calculated as the sum of engagements divided by the follower count.

But these numbers are often problematic for the following reasons:

  1. They tend to be averages across both their past branded and non-branded posts. If their non-branded engagement is higher than branded, this will be an inflated figure.
  2. They tend to be averages over a small handful of posts.
  3. They may not be up-to-date. If their account size has since grown it’s likely their engagement rate will have fallen.
  4. It is not sensitive to the vertical. Influencer performance varies across verticals and sub-verticals.
  5. An engagement rate is a crude (mean) average. These can be inflated by a few very popular posts and may not give a realistic idea of what they’re likely to achieve on a new campaign.


With enough of the right data we can build robust statistical models. These can tell us what the likely engagement performance is for particular influencers on particular platforms, posting particular types of content in particular verticals.

At CampaignDeus we collect vast volumes of data on social influencer branded and non-branded posts. We know, for example, whether a post is in the Fashion & Style vertical, or in the Travel vertical. We know which products are being talked about and whether the influencer can be seen in the photo or not.


We use this data to build models that allow us to disentangle the factors impacting post performance. We can work out the difference between the performance of branded and non-branded posts, controlling for other factors such as the time of day the post was published. We can get a handle on the relationship between follower counts and average engagement by different influencers across different verticals.

There is a lot of noise in social data and no model can control for everything. Some posts go viral for reasons hidden from the data. But we can use models to form projections and get an idea of which outcomes are best case, likely case and worst case scenarios. These projections can then be used to guide influencer negotiation and campaign planning.

Using data to negotiate pay

If influencer X with 100k followers is likely (with say a 75% chance), based on their historic data, to generate 10k+ engagements posting branded content in the Gaming vertical, and influencer Y with 200k followers is only likely to generate 5k+; clearly it makes sense to pay influencer X more than influencer Y. A system that gives users access to this information will greatly improve the efficiency of influencer selection and payment, and campaign planning more generally.

The direction of travel in all corners of marketing is towards more data-driven, fact-based decision making. That does not mean abandoning intuition, experience and common sense. Influencer marketing is still maturing, and as with all young markets, price has yet to fully align with value. The information available to buyers (brands and agencies) and sellers (influencers and their agents) is still inadequate. Scenario planning tools, backed by good data will address this problem over the coming months and years.


CampaignDeus works with Brands & Agencies to help them optimise their influencer campaign planning. Our platform provides clients with unbiased data, enabling them to choose the right influencers, guide pricing decisions and work with them effectively. Get in touch if you'd like to learn more.

CampaignDeus is the leading independent provider of influencer marketing campaign data for Instagram and YouTube. Our platform identifies and classifies brand sponsored influencer campaign performance metrics, tracking hundreds of thousands of posts.

We use this data to provide Brands & Agencies with industry insights across verticals, benchmark campaigns against vertical & competitor averages, and equip clients with in-depth reporting and recommendations on how to make campaigns more effective. Get in touch for more details.

Duncan Stoddard is the co-founder and Chief Data Officer of CampaignDeus. Previously a data scientist with DS Analytics, AlphaSights and media agency Mindshare. He specialises in statistical modelling, machine learning and data visualisation.