Does It Pay to Increase Public Messages in Nano-Influencer Campaigns? Investigating the Optimal Public Message Share for Sales and Return-On-Investment
- Online influencer marketing has matured, and many managers now seek better performance with nano-influencers in seeding campaigns. Compared to macro-influencers, nano-influencers create more private (direct online or offline) brand messages that supposedly deliver more “persuasive punch”. But such private messages lack the usual online tracking per message, forcing managers to ask nano-influencers for self-reports, or to estimate sales at a campaign level if they want to assess their own decisions. Therefore, little is known about how to set up effective nano-influencer campaigns: does it pay to increase the public message share in nano-influencer activity, in order to benefit from private and public influencer activity? Or does it defeat its purpose, diminishing nano-influencers’ persuasiveness and total campaign results? This study analyses a unique campaign-level data set of various seeded marketing campaigns comprising the activity of 700,000 nano-influencers. We model the impact of nano-influencers’ activity on incrementalOnline influencer marketing has matured, and many managers now seek better performance with nano-influencers in seeding campaigns. Compared to macro-influencers, nano-influencers create more private (direct online or offline) brand messages that supposedly deliver more “persuasive punch”. But such private messages lack the usual online tracking per message, forcing managers to ask nano-influencers for self-reports, or to estimate sales at a campaign level if they want to assess their own decisions. Therefore, little is known about how to set up effective nano-influencer campaigns: does it pay to increase the public message share in nano-influencer activity, in order to benefit from private and public influencer activity? Or does it defeat its purpose, diminishing nano-influencers’ persuasiveness and total campaign results? This study analyses a unique campaign-level data set of various seeded marketing campaigns comprising the activity of 700,000 nano-influencers. We model the impact of nano-influencers’ activity on incremental
sales and return-on-investment (ROI) at the campaign
level. To answer our questions, we include effects of public
message share and optimal public message share and their
interaction with activity; instrumenting both for identification,
and controlling for campaign, marketing, and brand context.
We find conditional optimal public message shares for both
sales and ROI. The average campaign benefits most when
increasing influencer activity (through design or incentives)
at public message shares between 4% and 5%. This shows
the importance of striking the right balance between nano� influencers’ online and offline activity, and suggests a similar
balance for combining nano- and macro-influencers in
future campaign formats…
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