An offshore casino is an online gambling platform that operates under a licence issued outside the player’s home jurisdiction. The term is widely used in the iGaming industry to describe operators based in territories such as Curaçao, Anjouan, Costa Rica or Panama that accept players from markets where they hold no local regulatory approval. Understanding the distinction between offshore and locally regulated casinos is essential for operators, affiliates, and analysts. The regulatory status of a gambling brand directly shapes its KYC (Know Your Customer) obligations, payment processing options, tax exposure, and the level of player protection it can credibly offer. An offshore casino is an internet-based gambling operation that is incorporated and licensed in one country but targets players in other countries where it does not hold a local licence. In academic and regulatory literature, the term “offshore gambling” refers to operators that lack a valid licence in the jurisdiction where the player is located yet still provide gambling services to that audience. The concept exists on a spectrum. At one end are operators licensed by established regulators — such as the Malta Gaming Authority (MGA) or the Isle of Man Gambling Supervision Commission — that serve international markets where no local licensing regime exists. At the other end are sites operating under minimal or dubious authorisation, or none at all, that deliberately circumvent the rules of regulated markets. The International Association of Gaming Regulators (IAGR) groups all such activity under the umbrella of the “unregulated gambling market”, a category that also includes wholly unlicensed operators. Offshore casino vs regulated (onshore) casino: A regulated casino holds a licence from the jurisdiction in which its players reside — for example, a UKGC-licensed operator serving British players or a New Jersey–licensed site serving players in that state. An offshore casino does not hold such a local licence. This distinction affects dispute resolution, consumer protection standards, advertising permissions, and whether revenue is subject to local betting tax. The operating model of an offshore casino follows a predictable sequence: The quality and enforceability of all these steps depend heavily on the licensing regime the operator has chosen. For the iGaming industry as a whole, the offshore segment is too large to ignore. A 2025 analysis by the American Gaming Association estimated that Americans alone wager approximately $673.6 billion annually with illegal and unregulated operators, generating roughly $53.9 billion in revenue — nearly a third of the total US gaming market. For operators and analysts, understanding the offshore landscape matters because it shapes competitive dynamics, player acquisition costs, and the true addressable market in any geography. Offshore operators typically face lower regulatory overhead and tax burdens, allowing them to offer larger bonuses and more diverse product catalogues, which in turn affects the value proposition of locally licensed competitors. For compliance and payments teams, the offshore/onshore distinction determines the entire risk profile of a business: KYC/AML depth, PSP availability, chargeback rates, and exposure to enforcement actions. FATF guidance classifies casinos as designated non-financial businesses and professions (DNFBPs) and requires them to implement AML controls comparable to those of financial institutions — a requirement that offshore operators under weaker regulators may not fully meet. For affiliates, promoting offshore brands carries reputational and legal risk. Regulators such as the UKGC have warned B2B licensees that supplying content or affiliate services to unlicensed sites jeopardises their own licences. Blurred legitimacy. The term “offshore” conflates well-regulated international operators (e.g., MGA-licensed) with unregulated or fraudulent sites. This ambiguity creates confusion for players and complicates competitive benchmarking for analysts. Limited player recourse. Players at offshore casinos typically cannot file complaints with their home-country regulator. If an operator refuses a payout or breaches terms, the player’s options are limited to the licensing authority of the offshore jurisdiction — which may have limited enforcement capacity or incentive to act. Chargeback and payment risk. Offshore gambling is classified as high-risk by card networks and PSPs. Operators frequently see higher decline rates, elevated processing fees, and rolling reserves. Third-party processors may obscure transaction descriptors, increasing the likelihood of player-initiated chargebacks and scheme-level monitoring. AML exposure. Operators under weak regulatory regimes may lack robust transaction-monitoring and source-of-funds frameworks. This creates vulnerability to money laundering — a risk that FATF and regional regulators have flagged repeatedly in sector assessments. Tax and legal uncertainty. Offshore operators do not remit betting tax to the jurisdictions where their players reside. For players, tax obligations on winnings vary by country and are not always clearly communicated. For the operator, expanding into a newly regulated market without obtaining a local licence can trigger enforcement proceedings and market exit under penalty. The offshore casino segment is a permanent feature of the global iGaming market. It ranges from well-regulated international operators providing legitimate services in underserved markets to unlicensed platforms that exploit regulatory gaps at the expense of player safety and public revenue. For operators, affiliates, and analysts, the key is to move beyond the binary “legal vs illegal” framing and evaluate each entity on the strength of its licence, the depth of its compliance controls, and the transparency of its payment and responsible-gaming infrastructure. Blask helps teams benchmark offshore and onshore operators…
In iGaming, the starting event is usually a registration or a first-time deposit (FTD). A rising active-player total can hide the fact that each new FTD cohort returns less often than the last. This guide covers the cohort retention formula, how to read a retention table, and how operators use cohorts to judge acquisition, CRM, product, and revenue. Cohort analysis is a method that compares groups of users who started at the same point, measured at the same stage of their lifecycle. It answers the question: what happened to the players who joined together? Four terms define every cohort report: All players who made their first deposit in January form the January FTD cohort. Each player’s clock starts on their own FTD date, so Day 30 for a player who deposited on January 3 is February 2. A standard active-player report mixes players on their first day with players in their second year. Cohort analysis lines them up by lifecycle stage instead of by calendar month. Most cohort reports follow five steps. Step 1: Define the cohort. Choose the starting event. Registration measures onboarding. First-time deposit measures the behavior of paying players. Step 2: Choose the metric. Pick one KPI: retention, repeat deposit rate, NGR per player, or deposit frequency. Step 3: Define the observation windows. Use fixed checkpoints, such as Day 1, Day 7, Day 30, Day 60, and Day 90, and keep them identical for every cohort. Step 4: Calculate and compare results. Measure the metric for each cohort at each checkpoint, so January’s Day 30 sits next to February’s Day 30. Step 5: Identify patterns and investigate changes. Look for cohorts that break the trend and dates that coincide with product releases, bonus changes, or new traffic sources. The cohort event changes the answer. Registration cohorts include people who never deposit, so they retain worse than FTD cohorts built from the same database. Pick the event that matches the business question first. There are two main types of cohorts. The difference is what puts a player in the group: a date or an action. Acquisition cohorts group players by when they first reached a lifecycle stage: Acquisition cohorts show how onboarding and marketing performance change over time. Behavioral cohorts group players by what they did: Behavioral cohorts help test whether early actions are linked to later outcomes. They show association, not cause: players who deposit twice in a week may simply have been more engaged from the start. The two types combine. “Players who made their FTD in March and verified within 24 hours” is an acquisition cohort split by behavior. Cohort analysis is a method, not a single metric, so it has no single formula. The calculation depends on the KPI you track. The most common one is cohort retention: Cohort Retention Rate (%) = (Active Players from the Original Cohort at Period N ÷ Total Players in the Original Cohort) × 100 The numerator counts only players from the original group who meet the activity rule at period N. The denominator is the full original cohort, and it never shrinks as players leave. Example: a cohort contains 1,000 players who made their first deposit in January. On Day 30, 350 of them meet the activity criteria. Day 30 Retention = (350 ÷ 1,000) × 100 = 35% “Day 30 retention” can mean three different things: The third method always produces the highest number. Analytics tools name these options differently: Mixpanel separates “On” from “On or After” retention criteria, and Google Analytics offers standard, rolling, and cumulative cohort calculations. Never mix methods in one table. The examples in this article use exact-day retention. A cohort table, also called a cohort matrix, puts every cohort on one grid: Example: an online casino tracks monthly FTD cohorts from January to April. Activity means at least one real-money wager on the exact checkpoint day. Data was extracted after the last April player reached Day 90. Read across a row to see how one cohort decays. January keeps 68% of players on Day 1 and 21% on Day 90. Read down a column to compare cohorts at the same age. Day 30 retention rises from 35% in January to 44% in April. Convert percentages back to players. The March cohort has 900 players. A Day 30 retention of 41% means about 369 of them placed a real-money wager on their own 30th day after FTD. April’s stronger numbers show better observed continuation. They don’t prove the product improved. A different acquisition mix, seasonality, market conditions, or player profile can produce the same pattern. Every cell needs a mature cohort. If data had been pulled in mid-June, the April cohort’s Day 60 and Day 90 cells would be incomplete and should stay blank. Mixpanel, for example, flags incomplete buckets with an asterisk and leaves them out of the average row. The three terms often appear in one report, but they are different things. Cohort analysis can produce both metrics: the January cohort above shows 35% retention on Day 30, so 65% were inactive that day. But cohort analysis itself isn’t a retention or churn formula. Retention is the default, but almost any player-level metric can be tracked by cohort. Two rules keep these numbers honest. Fix the denominator. Cohort ARPU divides revenue by every original member, including those who never came back. Revenue per retained player divides by survivors only. Illustrative: a cohort of 1,000 FTDs generates $80,000 in NGR by Day 90, and 210 players are active on Day 90. That’s $80 per original player or about $381 per retained player. Both are valid, but they answer different questions and can’t sit in the same column. Separate observed from modeled. Observed LTV is revenue the cohort has already generated. Modeled LTV is a forecast of what it may generate. Label which one you show, and never present a projection as realized revenue. The first-time deposit is the most useful cohort anchor for most iGaming operators. A registration costs the player nothing. An FTD…
