Investment in the future: Making influencers affordable

Influencers with a large following are expensive and usually not interested in promoting unknown brands. This marketing channel is therefore largely inaccessible to small and medium-sized companies (SMEs). Business professors are now suggesting a bypass from the financial sector: Forward contracts, which allow companies to sign such influencers early on, when they are still relatively unknown.

For companies, influencer marketing has become a reliable way for effectively reaching their target audiences. They are dedicating more and more marketing spending to it, with the global market for paid influencer endorsements expected to reach US$21.1 billion in 2023.

Expensive and hard to get

Companies are most interested in individuals with the most followers on online social networks. These macro-influencers have follower counts in the six or seven digits and a huge sphere of influence. Their price is accordingly high: for example, Kylie Jenner, one of the best-paid influencers, charges around US$2 million for sharing a single endorsement.

Such sums are out of reach for SMEs. Endorsing unknown brands is also associated with reputational risks, so the macro-influencers are often not willing to work with SMEs.

A future-oriented approach

A tool from the financial sector could provide a solution. In a recent article in the Journal of Marketing Research, Andreas Lanz, Professor of Digital Marketing Analytics at the University of Basel, and his colleagues recommend putting binding agreements in place for endorsements to be shared in the future, analogous to the use of futures in finance.

More specifically, they suggest contacting macro-influencers while they are still relatively unknown, and signing them to an agreement to market the company a few years later. This has the advantage of ensuring a significantly lower endorsement price.

The challenge lies in signing the right individuals at the right time since only the tiniest fraction of new users on an online social network become macro-influencers. To solve this problem, the researchers developed a predictive model and tested its effectiveness using data from a worldwide music platform.

The model analyzes users' activity and follows the developing community on an individual level. This enables the researchers to identify future macro-influencers a mere six months after they begin using a platform. This is of particular interest for record labels but also for the platform itself, as these users will eventually be responsible for a significant portion of the platform's traffic.

Each wave of new registrations is associated with earnings potential resulting from the value of the future endorsements. Assuming a three-fold return on spending on influencers, the proposed approach can capture around 20% of this profit potential.