In player-level cohort data from one Philippine operator, 94.3% of players produced 37.1% of revenue. The 5% above them produced almost two thirds. The entry tier had not paid back its acquisition cost after eleven months. Here is the full tier breakdown.
Player-level cohort data from a newer operator, still building out retention and VIP infrastructure.
94.3% of players. 37.1% of revenue. 26.9% M1 retention. Payback beyond month eleven.
4.6% of players. 26.1% of revenue. 63.8% M1 retention. Payback in month one.
1.1% of players. 28.0% of revenue. 72.8% M1 retention. Payback in month zero.
0.1% of players. 8.2% of revenue. 69.9% M1 retention. Payback in month zero.
Under 0.1% of players. 0.6% of revenue. 91.7% M1 retention. Payback in month zero.
Retention tracks payback almost exactly. The tier with the worst retention has the worst payback, and every tier above 70% retention pays back immediately. Tier 3 alone outproduces Tier 1 at about one percent of its size in player count.
Cohorts shrink every month as players churn. Every tier declines as its cohort thins, and the distance between tiers holds.
By month eleven a remaining Tier 4 player still generates roughly 600 times what a remaining Tier 1 player generates at the same point in their lifecycle. That ratio barely moves from month one. Tier 5 has very few players, so its month to month figures move around more than the trend suggests.
The figures above come from an operator still building its VIP program. We looked at a mature, VIP-optimized operator in the same market with years of retention data behind it. The pattern is sharper there, not softer.
Its smallest player tiers, under one in ten depositing players, produced more than 80% of total deposit value.
Blended payback across the mature platform ran around 1.5 months, against roughly 14 months at the newer operator.
More of each new cohort landed in a tier worth acquiring. A mature VIP program gets better at finding and keeping the players who matter most. That is the whole difference.
A blended number hides exactly the problem this page describes. Tier retention here ranged from 26.9% to over 90%, and the blended figure tells you nothing about either end.
Spending evenly across all tiers feels fair. A dollar spent on a player trending toward Tier 3 or 4 is worth far more than the same dollar spent broadly across Tier 1.
The operators with the fastest payback identify high-potential players inside the first few weeks after acquisition, while the intervention still changes the outcome.
Faster support, better terms, and real relationship management for anyone showing early signs of upper-tier behavior. The infrastructure has to exist before the player arrives.
In player-level cohort data from one Philippine operator, the entry tier held 94.3% of players and produced 37.1% of revenue. The tiers above it, around 5% of players combined, produced close to two thirds. Tier 3 alone outproduced the entry tier at about one percent of its size.
At month eleven after acquisition, a remaining Tier 4 player generated roughly 600 times what a remaining Tier 1 player generated at the same point in their lifecycle. That ratio barely moved between month one and month eleven.
In this dataset the entry tier had not recovered its acquisition cost within eleven months. Tier 2 paid back in month one. Tiers 3, 4, and 5 paid back in the same month the player deposited.
No. It widens as an operator matures. A mature VIP-optimized operator in the same market showed under one in ten depositing players producing more than 80% of deposit value, and blended payback around 1.5 months against 14 months at a newer operator.
By tier. A blended retention figure hides the problem entirely. In this dataset tier retention ranged from 26.9% at entry level to over 70% in the upper tiers, and retention tracked payback almost exactly.
We build segmentation on behavioral signals rather than deposit tier alone, and report retention against a holdout so the number is defensible. A discovery call is 45 minutes and there is no deck.