Definition
What is recommendation-based distribution?
Recommendation-based distribution is a mode of delivery where the platform chooses the content shown to a person based on their observed behaviour, rather than on the accounts they follow. TikTok is the best-known example. An account with no followers can therefore reach a wide audience, but the audience gained remains less stable than a subscription.
Recommendation or subscription
On a platform built on subscriptions, a post first reaches the people who follow the account: reach depends on the follower base built over time. On a platform built on recommendation, each post gets its chance based on its topic. The system shows it to people whose behaviour suggests it will interest them.
Both logics now coexist on most networks. LinkedIn also ranks its content with a recommendation system (360Brew), which assesses the meaning of a piece of content and not only the reactions it gets.
What it changes for a business
- The media entry cost falls. A new account can be distributed without a follower base.
- The real cost becomes production cadence. Recommendation gives each post its chance, but it needs material to learn what works. Two posts a week, sustained, are worth more than ten in one week.
- Words matter. TikTok transcribes and indexes its videos. The words your customers use should appear in the caption and be spoken aloud, because the platform also serves as a search engine for part of its users.
- The audience stays less stable. A video that travelled well does not guarantee the reach of the next one.
In advertising: broad targeting
Advertising follows the same logic. On TikTok, broad targeting lets the platform find the audience, while narrow segments can block delivery. An ad that looks like an ad is dismissed in a second: the format that works resembles organic content.
Meta has taken the same turn with Andromeda, its ad retrieval system, which rebuilds the matches between a person and an ad on its own. The lever moves from adjusting targeting to producing different messages.
Fictional example: a renovation company opens a TikTok account with no followers at all. Its first video explains, in vertical format and facing the camera, what to check before installing a heat pump. The platform shows it to people who watch renovation content, even though none of them follows the account.
The next video, on another topic, does not benefit from that success: it starts from zero with another audience. The company therefore judges the platform on the visits and enquiries received after several months of regular posting, not on a single video.
We first look at the age of your clientele and your production capacity, not at the platform's popularity. On TikTok, the person who appears on screen stays with you: no supplier can provide that face in your place.
We track visits and enquiries, not follower counts. On a recommendation platform, a follower is worth less than elsewhere, because it does not guarantee that the next post will be shown to them.
Not to be confused with
- Organic reach
- Organic reach is the number of people a post reaches without buying advertising space. Recommendation-based distribution is the mechanism that decides who those people are.
- Subscription-based distribution
- Subscription-based distribution first shows a post to the people who follow the account. Recommendation-based distribution shows it to people chosen by their behaviour, whether they follow the account or not.
Related concepts
Further reading
- TikTok for business: audience, content and advertising
- Organic social media: what role should it play in your marketing?
- 360Brew: understanding LinkedIn's recommendation algorithm
- Meta Andromeda: how to adapt your ad campaigns
- Social media advertising: choosing a platform and budget
Related services
Frequently asked questions
Do we need followers to be seen on TikTok?
No. Distribution works by recommendation based on topic rather than by subscription: a new account can reach a wide audience. In return, the audience gained there is less stable than elsewhere.
Why don't all our videos get the same reach?
Because each post is recommended based on its own topic and on how the first people who see it react. A video that travelled well does not guarantee the reach of the next one. Regularity therefore matters more than the success of a single video.
