--- title: "Synthetic iGaming Research Laboratory — Xslot" description: "Generate reproducible synthetic iGaming cohorts, inspect FARO features and operational metrics, and export transparent research reports." url: https://xslot.io/lab/ language: en --- Connecting to the generator… Experiment configuration100 accounts · 365 days · mixed lifecycles Configure + ## Your experiment Setup What do you want to test? Choose a starting point. Every setting remains editable. Run name AccountsUp to 100 complete player histories. History, days History starts · UTC Market context Ledger currency Local timezone GEO identifies public context. It does not prove that individual trajectories represent that market. Public evidence profileNational aggregates require an explicit operator share. Product mix ### Operator cost model Separate inputs Cost / tax modelPlayer GEO supplies public player context; it does not select operator expenses in offshore mode. Market/product gaming-tax profile Operator jurisdiction / licence context Declared variable gaming cost, %0% is the selected offshore assumption, not a verified legal exemption. An empty rate stays unconfigured. Tax base in the model Profiles specify the product, taxable base, effective dates and original source. Annual licence / regulatory budget per registrationAllocation = annual operator budget / assumed annual registrations. 0 means not allocated yet, not a free licence. Currency follows this run; no FX conversion is assumed. Calculate the allocation from an annual budget Annual budget · run currency Expected annual registrations What does the operator jurisdiction change? Lifecycle scenarioA reproducible mixture of casual, loyal, high-value, interrupted and departing histories. Registration spread, daysAccounts register at different points in the initial window. Behavioural assumptions Editable simulation assumptions, not measured player benchmarks. Typical deposit Active days / month Deposits / month Growth multiplier in escalation Typical session, minutes Casino RTP, % Sportsbook margin, % Payment failures, % Bonus grant probability, % Self-exclusion share, % Bonus terms & operating costs Explicit assumptions, not operator benchmarks or jurisdiction-specific rules. Costs are recorded separately from the player wallet. Restricted bonuses, % Wagering multiplier x25 / x35 are deposit-bonus term templates. x5 illustrates a loyalty or VIP cashback assumption. The simulator applies the multiplier to restricted grants; it does not make every grant deposit-matched. Bonus validity, days PSP variable fee, % PSP fixed fee · currency units PSP settlement delay, days Chargeback probability, % Chargeback delay, days Assumed provider fee, % Assumed affiliate share, % Visitors per signup AML review threshold · currency units Reproducibility & model options Random seedSame inputs and version reproduce the history. Add controlled coverage witnessesMixed products only. Exercises rare formula branches; deliberately constructed, not natural behaviour. Simulate a missing sourcePayments Sessions Sports Profile Limits Jev Optional source review OFF Disabled. Enable only after a measured quality gain. Numeric values and ledgers are calculated locally. Model capability status is loading. Waiting for generator connection. ### Saved runs Completed runs appear here. Runs are scoped to this browser session and retained for 24 hours. Download exports for durable copies. Synthetic research workspace ## Player lifecycle research Explore the saved example or generate a cohort with your own settings. Saved synthetic example Registered — Complete synthetic cohort First deposits · FTD — Depositors / registrations Deposits — Settled · complete history Cash GGR — After ledger corrections NGR · scenario — After gaming deductions CAC / registration — Configured acquisition cost LTV · scenario / registration — History + next 12 months D30 · observed — Active / mature on day 30 Synthetic scenario · checking the saved calculation Income Deductions Totals Future assumption EUR / registration Linear monetary scale. X: calculation order. In 3D, depth is decoration; height remains money. Missing values stay unavailable. ##### Payment expenses — Generate or load a run to see where revenue is converted into value. History before CAC — Future PV — Scenario LTV — CAC — After CAC — Exact calculation and all deductions Calculation layer | Amount / registration | Basis | A reconciled scenario demonstrates the mechanism, not measured market LTV or ROI. Corporation tax, fixed licence fees, levies and overhead require separate operator settings; they are not silently assumed to be zero. #### Value after acquisition cost Observed history + next 12 months Assumption — Per registered account Loading the saved scenario… #### Expected future activity Share of registrations · scenario At least one activity in a future 30-day window; permitted accounts only. Counts are expected values, not exact future players. Additional months to recover CAC — LTV / CAC — Remaining value before CAC — #### Your assumptions These controls recalculate this view. They do not regenerate player events. Future horizon, months Monthly continuation, %Activity in adjacent complete 30-day windows. Monthly margin multiplier Annual discount, % CAC / registered account Planned annual licence costs / registrationFuture fixed cost, spread over 12 months. It does not decline with player activity. Observed foundation — Recurring contribution before annual licence allocation / active player-month, across complete 30-day windows. How LTV and retention are calculated #### Observed history GGR = cash bets minus cash winnings, after corrections. NGR = GGR minus realised bonuses, configured gaming tax, provider/platform and affiliate costs. Acquisition cost is separate. ARPU below uses accounts that were active at least once. #### Remaining scenario Estimated LTV = observed contribution before CAC per registration + discounted future contribution. Future activity starts from the latest complete 30-day window and declines by the chosen monthly continuation rate. CAC is deducted once. A negative result remains negative. #### Two retention definitions D30 is exact-day observed activity among accounts that completed that day. Monthly continuation is activity in two adjacent complete 30-day windows divided by eligible active predecessor windows. These rates answer different questions. #### Scope of the estimate Synthetic histories and configured costs support scenario exploration. The model assumes the pooled mature cohort represents the future, constant margin and no explicit reactivation. The three sensitivity cases are assumptions, not confidence intervals. Daily totals and technical overview Generated accounts — No run yet Events & transactions — A traceable event ledger FARO feature coverage — / 180 Checked from the live catalog Integrity checks — Measured after generation ### Cohort over time Daily totals reconstructed from the generated history. Chart metric ### The shape of your experiment Generate a cohort to explore activity, cash flows and changes over time. Synthetic observations; no forecast has been run. Generated history ### Operator view Beyond FARO Payments, bonus economics, product mix and account restrictions will be calculated from events. ### How to read this run Coverage confirms the feature contract. Eligibility tells you whether a particular account has enough history. Undefined values carry a reason rather than becoming zero. ## Turn this experiment into a pilot Define one task for your platform, the data it needs and how you will accept the result. Take a brief into your next product or engineering meeting. Prepare a pilot for my stackLocal brief · no sign-up The task to validate Market for the pilot Player platform / PAM CRM / engagement platform Payments / PSP Available data What should this pilot answer? Acceptance criteria to include These are proposed checks. Their selection does not mean that they have passed. Attach the current laboratory configurationNo generated run selected. The brief can still be prepared. Inputs stay in this tab. Downloading creates a local file; it does not send a request or contact anyone. Your pilot worksheet ### #### Data to map #### What this can establish #### Validation still required Synthetic histories test logic and integration. Behavioural realism, model accuracy and commercial impact require a separate operator validation. Why these checks matter GB guidance applies to its licensed market. Provider documentation describes capabilities, not independent proof of results. Synthetic event samples and field mappings are in the integration pack under Compare decisions. ## Structured data ```json [ { "@context": "https://schema.org", "@graph": [ { "@type": "Person", "@id": "https://xslot.io/#person", "name": "Stanislav Sablin", "url": "https://xslot.io/", "jobTitle": "Product Lead / COO", "sameAs": [ "https://www.linkedin.com/in/stanislav-sablin/", "https://t.me/stas_69" ], "knowsAbout": [ "iGaming", "Product management", "Payments", "Retention", "CRM" ], "email": "mailto:fivetowers66@gmail.com" }, { "@type": "WebSite", "@id": "https://xslot.io/#website", "url": "https://xslot.io/", "name": "XSlot", "inLanguage": [ "en", "ru" ], "publisher": { "@id": "https://xslot.io/#person" } }, { "@type": "WebPage", "url": "https://xslot.io/lab/", "name": "Synthetic iGaming Research Laboratory — Xslot", "description": "Generate reproducible synthetic iGaming cohorts, inspect FARO features and operational metrics, and export transparent research reports.", "inLanguage": "en", "mainEntity": { "@id": "https://xslot.io/#person" }, "isPartOf": { "@id": "https://xslot.io/#website" } } ] } ] ```