The “honeymoon effect” definitely exists. When launching a new campaign or introducing a new title, ad networks sometimes distribute traffic more generously at the beginning. This can look like lower CPI, better visibility, or stronger early performance overall. But it’s inconsistent, and it doesn’t behave the same way across networks.

In most networks, you’ll encounter some version of a honeymoon period (though, Google is debatable). Sometimes it’s predictable. Sometimes it’s pure luck. For example, in Unity, the probability of hitting a honeymoon phase with minimal learning in a solid publisher segment is relatively low, but it does happen.

In practice, our rule is simple: if we don’t see acceptable payback metrics within the first 30 days, the product likely doesn’t have a future. The honeymoon might temporarily push month-one ROAS to 105%, only for it to fall to 92% later. Or the effect might be much shorter. Either way, it doesn’t fundamentally change the long-term picture.

So, should you adjust forecasts for an “average” honeymoon? Technically, yes, you can factor in a potential metric decline. But we approach it differently. We already account for natural product burnout over time, and the honeymoon effect fits into that broader pattern. Every product experiences some level of metric normalization. If a title has been live for several years, you can also forecast seasonality. That’s predictable. The honeymoon effect, however, is often random. In some cases, it lasts three months. In others, just a couple of weeks.

It’s important to understand that a honeymoon isn’t a generous gift from the network. It’s a strategic investment on their side. The network is willing to temporarily “lean in” on a title to help it find its core audience and optimize faster. How long this phase lasts is impossible to predict with certainty. We do have internal indicators that help us recognize it, but they’re not perfectly reliable.

That’s why we don’t build honeymoon effects into final profit forecasts. We simply acknowledge that they exist and treat them as part of broader product lifecycle dynamics. And most importantly: if a product doesn’t reach target metrics within the first 30 days, it probably won’t survive, with or without a honeymoon boost.

There’s a related phenomenon sometimes called the “golden cohort.” This happens when, right after release, the most engaged and highly targeted users enter the game first. Early metrics look strong, but they may decline over time as less-targeted audiences begin to arrive.

We don’t treat this as a separate effect. It’s more common in projects that build hype long before launch. In hardcore, midcore, and some casual genres, the effect can be more noticeable. The same applies to heavily hyped mechanics or breakout trends — early adopters are naturally more aligned with the concept. If a studio actively works with its community before launch, even community management efforts can help attract that first highly targeted wave.

But for us, all early-stage metric spikes fall into the same broader category.

It’s simply a phase when the network delivers highly targeted impressions at a lower cost, helping your learning phase mature faster. The real danger isn’t the honeymoon itself.

The real danger is making earlier mistakes — which we’ll cover shortly — for example, launching with a weak product that looks strong because of inflated metrics. If the product is fundamentally solid, the right move is simple: acquire users, leverage the momentum, and wait for metrics to normalize.