TikTok's Smart+ and the Rise of Automated Ad Campaign Tools

TikTok has continued expanding Smart+, its automated campaign tool, to more app advertisers this month, following the same trajectory Meta set with Advantage+ and Google set with Performance Max. The pitch is consistent across all three: hand the platform your budget, your creative assets and your target outcome, and let the algorithm handle targeting, placement and bid decisions that a human media buyer used to make manually. For game UA teams, automated ad campaign tools are no longer an experimental option sitting alongside manual campaign structures. They are becoming the default path a platform steers advertisers toward, whether or not a team feels ready for that shift.

What buyers actually give up

The trade is not hidden, but it is easy to underweight until a team has lived with it for a full quarter. Automated campaigns absorb decisions that used to generate learning: which audience segment responded to which creative, which placement carried the best cost per install, which bid strategy performed at which time of day. A manually structured campaign produces that data as a side effect of running it. An automated campaign optimises toward the stated outcome without necessarily surfacing why it made the choices it made, because exposing that reasoning is not what the tool is built to do.

For a UA team that relies on granular reporting to inform creative strategy, not just campaign performance, that is a real cost. A team that has been running automated campaigns for two quarters and then tries to explain why a particular creative concept underperformed may find it cannot answer the question, because the platform never broke performance down by creative variant in a way the team could access.

This is not unique to TikTok. Meta's Advantage+ and Google's Performance Max both make a version of the same trade, and each platform frames the reduced visibility as a fair exchange for better raw performance. The argument has some merit: an algorithm managing thousands of micro-decisions per campaign genuinely can outperform manual rules built on weekly review cycles. What the platforms are less forthcoming about is that the trade is not symmetric for every advertiser. A team with a mature creative testing programme loses more from reduced granularity than a team that was never doing rigorous creative-level analysis in the first place, because the first team is giving up a real capability and the second is giving up something it was not using.

Where automated ad campaign tools are worth the trade

The trade looks different depending on what a team is actually trying to learn. If the objective is pure performance at scale, cost per install or return on ad spend at volume, automated tools frequently outperform manual structures, because the algorithm can react to signal faster than a human buyer checking dashboards once or twice a day, and it can test combinations of targeting and placement a manual structure would never attempt. If the objective is creative learning, understanding which hook, which pacing, which mechanic resonates with which audience, automation actively works against the goal by collapsing that granularity into a single optimised outcome.

Most UA teams need both. That is the actual problem automated campaign tools create: not that they perform badly, but that they perform well at the wrong altitude for a team that still needs creative-level insight to feed its production pipeline.

A structure that keeps both

The practical answer several teams have converged on is running automated and manual campaigns in parallel rather than choosing one. A team might run 70 percent of always-on budget through Smart+ or an equivalent automated tool to capture its performance advantage at scale, while holding 30 percent in a manually structured campaign built specifically to preserve creative-level reporting. The manual slice does not need to carry the same budget weight to be useful. It needs enough volume to produce statistically usable creative performance data, which is normally a smaller spend than teams assume once they define the sample size they actually need.

What to check before ceding more budget to automation

Before shifting further budget into an automated tool, a UA team should confirm three things: what reporting granularity, if any, the platform still exposes once a campaign is fully automated; whether the platform's stated optimisation goal actually matches the team's real business objective, since automated tools optimise hard toward whatever goal is set and will chase the wrong one just as efficiently as the right one; and whether the team has a separate mechanism, such as the manual parallel campaign above, for generating the creative-level learning the automated campaign will not provide.

A fourth check is worth adding specifically for games: confirm which in-app events the automated tool is actually optimising toward, and whether that event genuinely correlates with long-term value for the title in question. An automated campaign told to optimise toward install volume or a shallow early event, such as tutorial completion, will do that efficiently and say nothing about whether the resulting players ever become payers. Games UA teams that have had the best experience with automated tools are generally the ones that took the time to feed the platform a deeper, value-correlated event before switching optimisation on, rather than accepting a default goal because it was the fastest way to launch the campaign.

Watching the direction of travel

Automated campaign tools are getting better and more widely pushed by every major platform at once, and the trajectory shows no sign of reversing. TikTok's continued expansion of Smart+ this quarter is one more data point in a pattern that started with Google's Performance Max and Meta's Advantage+, and there is little reason to expect a fourth major platform to resist the same shift. That makes automation worth adopting for most UA teams running meaningful spend. It does not make it worth adopting blind, and the teams likely to regret the shift in a year's time are the ones that let the platform's own dashboard define what counted as success.

Related archive reading

These articles provide related context and remain subject to their stated review status.

• TikTok for games: beyond Spark Ads
• Automation did not remove judgement, it moved it