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All insightsForecasting & Scenario Planning

Forecasting the Impact of a UK Social Media Ban on Advertisers

If the UK follows Australia and restricts under-16 social media access, what happens to reach, CPMs and MMM baselines? A practical framework for advertisers.

twenty10··6 min read

Australia has legislated an under-16 social media ban. The UK is openly consulting on similar restrictions. Whatever the final policy, advertisers should be modelling the scenarios now, because the second-order effects on reach, cost and measurement will land quickly and unevenly.

This piece is a practical framework for forecasting the impact on a media plan. It is not a policy piece.

The direct effect: reach on the affected platforms

Under-16s are a small share of most brands' target audience, but they punch above their weight on specific platforms. TikTok, Snap and Instagram Reels carry a disproportionate share of teen attention: taking that inventory off the market removes 8-15% of platform-reported reach for a typical FMCG brand, and materially more for categories that skew young (gaming, confectionery, fast food).

The direct effect is modellable now. Pull the last twelve months of impression delivery by age band from each platform's reporting API, apply a straightforward removal of the under-16 tier, and rerun the reach curves.

The indirect effect: CPMs on the remaining inventory

Removing supply without removing demand pushes prices up. Australia's ban is too fresh to have stable data, but auction dynamics are well understood: a 10% reduction in inventory typically produces a 4-8% CPM increase in the same auction, holding demand flat. That is the number to bake into next year's cost planning.

The larger risk is that displaced demand does not stay on the same platform. Brands over-indexed on youth attention may push spend into YouTube, Twitch, or CTV, tightening prices there as well. Model the cross-platform elasticities, not just the affected inventory.

The measurement effect: MMM baselines will move

This is the piece most advertisers miss. Marketing Mix Models estimate a base level for demand: the revenue that would have existed with no advertising. A structural change in category behaviour: a cohort disappearing, or shifting to other channels: moves the base. If the model is not refreshed with the new regime represented in the training window, every channel's estimated ROI drifts.

Practical response: add a scenario flag to the MMM for the pre- and post-ban periods, refresh the model within a quarter of any policy change, and run sensitivity tests on the base before publishing the next channel ROIs.

A four-step scenario plan

  1. Quantify the reach loss. Pull age-banded delivery, model the removal, publish reach curves for three scenarios: no ban, under-14 ban, under-16 ban.
  2. Model the CPM response. Apply supply elasticity to the retained inventory. Sensitise across a range: this is where the model earns its keep.
  3. Reforecast the media plan. For each scenario, rerun the MMM optimiser with the new cost curves. Identify the reallocation you would make on day one of the policy.
  4. Instrument the measurement. Add the regime flag now, so when the policy lands the model can distinguish the ban effect from ordinary noise.

What not to do

Do not wait for the policy to be gazetted. The reach and cost effects will land in weeks, not months, and the vendors who move first will lock in the inventory. The advertisers who ran the scenario six months before the Australian ban were better positioned than the ones who reacted after.

Scenario planning is the cheapest insurance a marketing team can buy. This is a scenario worth planning for.