Cheap entry almost always looks like high volume. Fast volume, low user acquisition cost, the feeling that the model has been “captured”. Over a short distance the numbers may look convincing, and ROI in media buying may inspire confidence. The problem is that the real price of such decisions is almost never visible immediately. Most often the model does not break on the first day and not even in the first week — but after 2–3 weeks, when as volume grows it becomes clear how mass player churn appears and how the quality of the entire flow begins to decline.
When cheap entry creates a false sense of stability
Cheap traffic is most often built on a bet on volume or the chicken road approach. A typical example is in-app traffic, which is common in affiliate marketing, where genuinely high-quality flows are relatively rare. In some GEOs crash games are perceived as quick emotions or an attempt at short-term earnings, without a focus on a long user lifecycle. For the platform such a product looks relatively neutral. It is easier to scale — it is not classified as a direct slot, since Facebook may perceive it more as game development content. Because of this, within facebook ads arbitrage the acquisition cost can be significantly lower. Inside a media buying strategy this is often justified by the expectation that payback will appear over time — through redeposits or occasional high-rollers.
In practice, such traffic most often:
• does not form stable repeat behaviour
• has low unique redeposit (uniq RD)
• quickly washes out after the first wave
This does not mean that such an approach does not work at all. In some GEOs — for example India and several European countries — it can be an effective tool. But only if the full unit economics of the product and the analytics are clearly understood from the beginning.
Why a higher entry cost does not always mean higher risk
If we compare this with slot traffic, the difference becomes noticeable quite quickly. The entry cost here is higher — the platform clearly sees a direct slot, and the cost per user increases. But at the same time the entire economic model changes.
The difference becomes visible in key metrics:
• ARPPU
• Q RD
• LTV
These metrics reflect not simply the paying ability of a user but their engagement with the game mechanics and gambling behaviour. Such traffic responds better to bonus mechanics and loyalty programs, which means it returns to the product and forms a more predictable long-term distance. This is what ultimately determines ROI in media buying, not the entry price.
“A cheap user is not always bad. A bad user is a user who has no future in the product,” adds Stanimir, BDM at Masons Partners.
Where media buying management most often breaks
The key mistake is evaluating traffic purely by the entry price. Cheap CPM creates a feeling of efficiency but does not take into account the cost of the negative tail. Refunds, fraud and churn start affecting the economics later — after the scaling decision has already been made.
At that moment it becomes clear that managing media buying is not about optimizing entry cost, but about controlling user behaviour over time. Without this, even a visually successful campaign begins to dilute the model.
Effective media buying is about a controllable structure where risks, timeframes and scaling limits are understood.
The Masons Partners approach: model first, then scale
In our work in affiliate marketing we look at traffic not as a source of volume but as part of the product’s economics. This means choosing one GEO, testing several products under the same conditions, and analysing user behaviour over time before making growth decisions.
This approach makes it possible to understand in advance where traffic creates value and where it only creates a short-term illusion of results. In this case scaling becomes the result of a stable model rather than an attempt to “push” the numbers further.
Instead of a conclusion
Over the years we have seen dozens of situations where a “working” campaign began destroying the product economics after several weeks. At the start everything looked correct: volume, deposits, first reports. But later it became obvious that the model had been built on too short a distance.
That is why our focus is always shifted from entry price to user behaviour over time. For us scaling is not a way to chase numbers but the result of a stable model where the sources of value and the limits of growth are clearly understood. Cheap traffic can be part of a strategy. But only when it is consciously integrated into the product economics, and not when it replaces it.