Anonymised engagements illustrating how we've helped operators build, enter, and optimise across three continents.
The group's existing planning process was built on annual budgets extrapolated forward with optimistic growth assumptions. The CEO wanted to double revenue to £360M within five years, but there was no model connecting that headline number to the operational levers that would actually deliver it — headcount, marketing spend by jurisdiction, product mix, regulatory timelines, or capital requirements.
The board was making investment decisions based on ambition rather than architecture. Market entry sequencing was driven by gut feel. The iGaming launch had no standalone P&L. And investor conversations were stalling because the financial narrative lacked the granularity institutional capital demands.
We constructed a bottom-up long range plan spanning five years, built from the unit economics up rather than the headline down. Each existing market got its own P&L with realistic growth curves based on market maturity, regulatory headwinds, and competitive intensity. The three target expansion markets — Brazil, a second Southern European jurisdiction, and an African mobile-first market — each received a standalone entry model with phased investment, licensing cost projections, and time-to-breakeven scenarios.
The iGaming vertical was modelled separately with its own player acquisition funnel, game supplier margin structure, and cross-sell assumptions from the existing sportsbook database. We defined 18 KPIs across the group — from blended GGR per active player and marketing efficiency ratios to jurisdiction-level contribution margins and cash conversion cycles — and built a quarterly tracking dashboard that linked actuals to the plan in real time.
Every assumption was stress-tested. We ran Monte Carlo simulations on the three most sensitive variables (sportsbook margin volatility, regulatory approval timelines, and FX exposure) and presented the board with a probability-weighted outcome range rather than a single deterministic forecast.
The long range plan was adopted by the board and became the basis for all capital allocation decisions. Market entry was resequenced — Brazil moved from Year 1 to Year 2 (pending regulatory clarity), while the African market was accelerated to Year 1 based on superior unit economics revealed by the model.
Within six months, the plan was used to secure a £45M credit facility, with the lending syndicate citing the quality of the financial model as a key factor in their underwriting decision. The quarterly KPI dashboard is now the centrepiece of every board meeting, with actuals tracking within 4% of plan through the first three quarters.
The sweepstakes casino model is deceptively complex. On the surface it looks like free-to-play gaming — players receive Gold Coins for entertainment and Sweeps Coins for prize redemption, with a "no purchase necessary" legal foundation borrowed from decades-old contest law. But underneath, the financial dynamics are unlike anything in traditional iGaming: conversion rates hover around 12% (compared to 50%+ on licensed gambling apps), payout ratios run between 68-72% of gross, and the entire revenue model depends on a small minority of high-frequency purchasers.
The studio also faced a rapidly shifting regulatory landscape. California had just banned sweepstakes casinos effective January 2026 — removing roughly 17% of the addressable market overnight. Connecticut and Montana had followed. The question wasn't just "can we build this?" but "what does the five-year model look like when your largest state can disappear from the TAM with six months' notice?"
We designed the complete business model from scratch: a dual-currency P&L that separated Gold Coin purchase revenue from Sweeps Coin redemption liabilities, modelled at the state level to account for regulatory risk. Each of the 45 accessible states was weighted by population, smartphone penetration, and proximity to regulated online casino alternatives (states without legal online casinos showed 2.3x higher sweepstakes adoption).
The unit economics model was built around the reality of low conversion / high ARPPU: we modelled the player funnel from free registration through first Gold Coin purchase to repeat purchasing behaviour, with cohort-level decay curves calibrated against industry benchmarks. The revenue model included scenario analysis for progressive state-level bans, showing the board exactly how much GGR was at risk under three regulatory trajectories.
We also designed the promotional economics — welcome bonus sizing, daily login reward structures, and referral incentive programmes — with a clear link to LTV payback periods rather than the "spray and pray" approach most sweepstakes operators were taking.
The platform launched with over 800 games from tier-one providers, a mobile-first progressive web app, and the pricing and promotional architecture we designed. The financial model was used to raise a $6.2M Series A from investors who had previously only backed regulated gaming companies — the dual-currency P&L and state-level risk modelling gave them confidence in a segment they'd been hesitant to enter.
Within the first four months, paying conversion hit 14.2% (above the 12% industry average), and ARPPU tracked 22% above the base case — largely attributable to the tiered coin package structure and VIP programme economics we designed into the model.
The team understood order books, liquidity pools, and matching engines. What they didn't understand was how iGaming economics actually work — the relationship between GGR, NGR, and take rate in a peer-to-peer context; how to model liquidity depth for sports markets that expire (unlike financial assets); and what a sustainable fee structure looks like when you're competing with both traditional bookmakers offering zero-commission promotions and existing exchanges.
They also needed a financial model that would satisfy both UKGC licensing requirements and institutional investors who were used to evaluating SaaS metrics, not betting KPIs.
We designed the complete financial architecture: a dynamic take-rate model that varied by market liquidity depth (lower rates on high-liquidity football markets to attract volume, higher rates on niche markets where the exchange provided genuine price discovery); a maker-taker fee structure inspired by financial exchanges but adapted for the betting context; and a market-making reserve fund model that ensured minimum liquidity on launch markets without exposing the platform to directional risk.
The P&L model translated exchange mechanics into investor-friendly metrics: matched volume as GMV, take rate as net revenue margin, active trader count as MAU, and liquidity provider incentives as customer acquisition cost. We built a 5-year projection with three scenarios tied to regulatory outcomes — full UK approval, restricted licence, and pivot to international markets.
The platform secured an $8M Series A within 4 months of our engagement completing, with the lead investor specifically citing the financial model's sophistication and the clear translation between exchange mechanics and traditional iGaming KPIs. The tiered fee structure was adopted as designed.
In its first year of operation, the exchange matched £340M in total volume across football, tennis, and US sports markets — with the effective blended take rate settling at 2.4%, above the 2.1% base case in our model.
The operator ran a traditional CRM-driven retention programme: when a player's activity dropped, they'd trigger a bonus. By that point, the player had usually already opened an account with a competitor. The median time between a VIP player's last session and the retention offer was 19 days — far too late for players being actively targeted by rivals.
Their existing BI stack could tell them what happened yesterday. What they needed was a system that could tell them what would happen in two weeks.
We developed a gradient-boosted classification model trained on 18 months of historical player data across all three markets — approximately 2.4 million player records. The feature set included 47 behavioural signals: session frequency decay curves, deposit velocity changes, game-type migration patterns, bonus-to-deposit ratios, time-of-day drift, and cross-brand activity indicators.
The model was calibrated to flag players with a >65% probability of churning within 14 days, segmented into three risk tiers. Each tier triggered a different intervention pathway — from personalised game recommendations for moderate risk, to direct VIP manager outreach with tailored retention offers for critical risk players.
Within the first quarter of deployment, VIP segment churn decreased by 41%. The average intervention now happened 14 days before projected last session — versus 19 days after under the old system. That 33-day swing translated to $2.8M in retained annual GGR, against a total engagement cost of under $200K.
The model was subsequently extended to mid-tier player segments and integrated into the operator's real-time CRM pipeline, with predictions refreshing every 6 hours.