Imagine two digital products.
The first sells a monthly subscription for BYN 25. If 4,000 people subscribe, the product generates around BYN 100,000 in revenue before expenses.
Users place BYN 280,000 in bets through the second product. This does not mean the product has earned BYN 280,000. Most of that money will return to users as winnings.
The same large number in a report can therefore represent a completely different business reality.
Three financial layers in iGaming
We can make the economics easier to understand by separating three levels.
- Turnover is the total amount wagered.
- GGR is the amount left after player winnings are paid.
- Revenue after variable costs shows how much the product has contributed before the company's fixed expenses.
A common mistake for newcomers is to focus only on the first number.
Turnover is useful for understanding activity, but on its own it says very little about profit.
A simplified example
Suppose users place BYN 280,000 in bets during one month.
They receive BYN 263,200 in winnings.
Gross Gaming Revenue would be:
BYN 280,000 − BYN 263,200 = BYN 16,800 GGR
But the BYN 16,800 is still not profit.
For the same period, imagine the product spends:
- BYN 2,100 on bonuses;
- BYN 2,500 on content and technology provider fees;
- BYN 3,000 on taxes and mandatory charges;
- BYN 3,400 on payments, partners and acquisition;
- BYN 600 on chargebacks and fraud losses.
Around BYN 5,200 remains after these costs.
This number is much closer to the product's real contribution. The company may still need to cover its team, infrastructure, development, support and other fixed expenses.
This example is intentionally simplified. Companies define NGR, or revenue after key deductions, and product contribution differently. It is important to check the internal methodology before comparing reports.
Why the financial result keeps changing
In a subscription service, each additional paying customer usually adds a known amount of revenue.
In iGaming, the team can observe the user's activity, but the final financial result of that activity is not fixed in advance.
It can be affected by:
- a large win;
- an unexpected sporting result;
- a change in the mix of bets;
- the activity of a few high-value users;
- the choice of more volatile games;
- the actual return of gaming content;
- bonuses and acquisition channels.
Two months with the same turnover can therefore produce very different GGR.
One month may deliver a stable positive result. In another, several large wins may reduce revenue even though user and betting activity has barely changed.
How RTP works
Casino products often use the term RTP. It describes the share of wagers that a game is theoretically expected to return to players over a large number of rounds.
Suppose a game has a theoretical RTP of 95.2%.
This does not mean that every user will receive exactly 95.2% of their wagers back. One person may win more, another less and a third nothing at all.
The theoretical value becomes more visible only across a large volume of rounds. Over a short period, the actual result can differ significantly.
This is why a week or even a month may not provide enough information for a confident financial conclusion.
Sportsbook economics works differently. Odds, built-in margin, the distribution of bets and actual sporting results all affect the outcome. The underlying issue is similar: final revenue cannot be determined from betting volume alone.
When a good product looks financially weak
Imagine that a team improves the application's navigation and speed.
After the release:
- active users increase by 12%;
- the number of bets increases by 18%;
- users return to the product more frequently.
According to the product metrics, the change looks useful.
During the same period, however, several large wins occur and GGR falls by 9%.
Does this mean the update was a failure? Not necessarily.
The product became easier to use and increased activity. The financial result fell because of the way gaming events unfolded during that particular period.
The reverse can also happen. The team makes no meaningful product changes and user behaviour remains stable, but GGR temporarily rises because the statistical result is favourable to the operator.
Looking only at GGR may lead a team to praise an ineffective change or reject a genuinely useful feature.
Two layers of analytics
Product teams therefore need to evaluate results in two layers.
The first layer shows how user behaviour changed:
- registration and conversion;
- active users;
- frequency of use;
- number of bets;
- retention;
- product stability and speed.
The second layer shows the financial result:
- turnover;
- GGR and NGR;
- actual margin;
- bonus costs;
- provider and payment fees;
- acquisition costs;
- chargebacks and fraud losses;
- contribution to profit.
The first layer helps the team understand whether the experience became more useful. The second shows how that activity translated into money.
Only together do they provide the full picture.
Why a short test may not be enough
Imagine tossing a coin ten times. Even with an ordinary coin, the result could easily be 7:3.
If we toss it several thousand times, the result will probably move closer to the expected balance.
A similar principle applies in iGaming. The shorter the period and the smaller the volume, the more one win or sporting result can affect the overall picture.
Teams therefore use:
- control groups;
- a sufficient volume of users and bets;
- longer observation periods;
- separate analysis of mass-market and high-value users;
- individual analysis of games, sports, markets and traffic sources.
This helps distinguish a genuine product effect from ordinary statistical fluctuation.
Questions teams should ask
Instead of asking only whether turnover increased, a team can ask:
- Are there more active and retained users?
- Did their behaviour change after the release?
- Is there enough data for a confident conclusion?
- Is the result driven by a small number of large wins?
- How did bonus, partner and payment costs change?
- Did fraud losses increase?
- Are responsible gaming requirements being met?
- How much remains after variable costs?
These questions help separate an impressive number in a report from genuinely healthy growth.
The key idea
iGaming economics moves through several stages:
user activity → statistical outcome → variable costs → contribution to profit
A product can increase turnover without increasing profit. It can also show weak GGR over a short period even when an update has genuinely improved the user experience.
The goal of analytics is not to find one perfect metric. It is to separate product quality from the randomness of gaming outcomes and understand how the entire system performs over time.
We are developing SPINCON as a space where complex iGaming topics can be discussed in clear language.