Function
Similar Channels expands from what already works: start with proven seed channels and discover lookalike placements based on overlap and relevance signals. This is the fastest way to scale reach while preserving audience fit and reducing random prospecting.
5-15
recommended seed channels per run
x3
typical candidate expansion from seed set
Overlap
built-in relevance signal for filtering
2-step
discover first, validate second
Use this when you already have channels that convert and need to scale safely. It is ideal for teams that want new inventory without moving too far from proven audience behavior.
Add a seed list of high-quality channels, run Similar Channels processing, and review overlap with processed-channel context. Keep broader output first, then narrow by relevance and size before validation.
Run discovery from 5 to 15 seeds, shortlist lookalikes with acceptable overlap and scale, then send that shortlist to Channel Analysis. This keeps expansion fast while preserving quality control.
An online school seeds 10 channels that delivered paid trials. The run returns 120 lookalikes, and after filtering plus analysis the team keeps 35 channels with stronger audience affinity than generic category search.
A local service enters a new city and starts from regional seed channels. Similar Channels surfaces adjacent communities with matching behavior, helping the team avoid random buying in unrelated channels.
Avoid using weak seeds. If the initial channels were low-quality, lookalike output will inherit that bias and create noisy expansion candidates.
You get a filtered expansion list rooted in proven audience behavior, ready for final validation and placement planning.