AcademicJournal of Marketing, 2026

Sponsored posts got more engagement when influencers had either very low or very high ad loads, and did worst in the middle

Shortlisting creators

Reader Note:: The paper's central finding, supported in two field studies and two randomised experiments. "Endorsement rate" is the share of an influencer's posts that are sponsored. The size of the effect, as model-based marginal effects for a 5-point rise in endorsement rate: on Instagram comments fall 6.9% on the descending side of the curve and rise 4.3% on the ascending side, while likes fall 4.3% and rise 3.6%; on Douyin reposts fall 4.4% and rise 4.9%. Where the bottom of the curve sits is the practical part, and it differs by platform. On Instagram it is at 47.5% of posts for comments and 39.3% for likes, while the average influencer there sits at 16% — well on the safe side. On Douyin the bottom is at 51.9% and the average influencer sits at 61%, just past it. The Instagram posts are from October 2022, with twelve months of prior history used to compute each endorsement rate. The Douyin videos were posted since 2018, drawn evenly from five domains. Engagement means something different in each study: likes and comments on Instagram, reposts on Douyin, engagement likelihood or actual sharing in the experiments.
Evidence:: Observed behaviour
Vendor interest:: None
Sample:: Multimethod study: Instagram field study of 6,869 sponsored posts from 4,908 influencers; experiment with 168 Twitter users; experiment with 208 Instagram users; Douyin field study of 147,877 sponsored videos from 1,000 influencers, plus comment-text analysis on 2,015,022 comments from 24,199 sponsored videos by 250 influencers.

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Saying that one thing caused another, or predicting what you would get.
This data is old for how fast this topic moves. A lot may have changed since then.
This is an aggregated or average number that covers very different cases. Real results in a particular segment may differ.
The source says how much it looked at, but not how any of it was chosen.

Influencer selection is mainly based on engagement rate and audience demographics (94% and 88%), with only 25% using social listening on what consumers discuss

Share of marketers using each data input to select influencers, and what it measures
Data input	Usage (%)
Engagement rate (Influencer output)	94%
Audience demographics (age, gender, location) — Influencer audience	88%
Past campaign performance data (Influencer track record)	81%
Audience interest / affinity data (Influencer audience)	78%
Follower count and reach metrics (Influencer scale)	72%

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1.95x

higher conversion odds came with stronger creator audience-brand fit in influencer marketing campaigns

97% of marketers say influencer marketing targeting is inconsistent, immature, or essentially guessing; only 3% say it is very mature with high precision

How marketers rate the influencer marketing industry's targeting maturity (% of respondents)
Industry targeting maturity	Share (%)
Very mature, targets consumers with high precision	3%
Somewhat mature, pockets of sophistication	44%
Immature, most campaigns lack structured targeting	29%
Very immature, essentially guessing	15%
Unsure	9%

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89%

of marketers do not plan to work with virtual influencers AI avatars or digital clones of creators in 2026

In creator selection, brand fit mattered most and follower count mattered least

Most Important Factor Brands Use When Selecting New Creators to Work With
Selection factor	Brands (%)
Creator suitability or fit for brand	22%
Content performance	17%
Working with a diverse group of creators	14%
Brand affinity, or a history of posting about your client	12%
Follower audience demographics	10%

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People were about twice as likely to share the sponsored Instagram post when the influencer had low or high ad load instead of a middle one

Sponsored posts got more engagement when influencers had either very low or very high ad loads, and did worst in the middle

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