About iGamingAnalytics
Built for iGaming from day one
iGamingAnalytics is a specialist data engineering and analytics team that builds custom BI, data warehouse and AI solutions for iGaming operators and platform providers. We do not sell subscriptions to a platform. We build what you actually need and hand it over fully owned by you.
The Team
The people you'll work with
No account managers between you and the specialists. Every engagement runs directly with the engineers and domain experts doing the work.
12+ engineers on the team
Senior specialists across data engineering, BI, ML and iGaming domain — not all listed here.
Our Background
Where this comes from
The team behind iGamingAnalytics has spent years inside iGaming data operations — building reporting stacks for operators, designing data warehouses for platform providers, and working through the real problems that emerge when a business tries to scale its analytics alongside its product. We know what a Tuesday morning looks like when the CEO wants an answer about NGR, the CRM team is waiting on a new segment and the trading desk needs a live exposure view. We have been the people building those systems under that pressure. That background is what shapes how we work.
The Problem
What we kept seeing
Most iGaming companies reach a point where their data setup stops working. Either reporting is too slow and too shallow — high-level KPIs with no depth, no monitoring and no ability to answer hard questions quickly. Or the stack becomes a fragmentation problem — five tools that almost talk to each other, three conflicting definitions of NGR and an analyst team buried in ad-hoc requests. The SaaS analytics market offers a third option: subscribe to a platform, get started quickly, and accept the tradeoffs. Fixed features. Shared infrastructure. Perpetual licensing costs. Your player data on someone else's servers. For many operators those tradeoffs do not hold up.
What We Do
What we build instead
We design and deliver custom data infrastructure for iGaming — data warehouses built on your own servers or cloud accounts, BI layers governed and owned by your team, AI and predictive models trained on your data and running inside your environment. Every build is scoped to your operation, delivered iteratively with your input and handed over in full at the end. No subscription attached. No access rights held back. The code, the pipelines, the dashboards and the models are yours — permanently.
The Difference
Why ownership matters in iGaming
iGaming data is among the most valuable and sensitive material an operator holds — player identity, transaction history, full behavioural records and betting history. The question of where that data lives and who has access to it is not abstract. An owned, on-premises data infrastructure keeps everything inside your environment, governed by your policies, accessible only to your team — with no third-party servers and no data sharing agreements to manage.
On-premises as standard
Deployment on your own hardware or private cloud is a first-class option, not a premium add-on. If your operation cannot or will not share data with third-party SaaS servers, we are the right fit.
Senior team, direct contact
No project managers between you and the engineers. No ticket queues and handoffs. You work directly with the specialists building your system, which is why our projects move faster than the industry expects.
iGaming expertise, not generic BI
We understand NGR, RTP, margin drift, cohort retention, bonus economics, bet acceptance and affiliate quality because we have built systems around them — not because we read about them.
Who We Work With
Who we work with
iGamingAnalytics works with iGaming operators across verticals — online casino, sportsbook, poker, lottery and affiliates — as well as platform providers who need to offer analytics to their operator clients. We work with businesses at different stages: those building a data layer from scratch, those replacing or extending what they already have, and those with specific capability gaps — a missing predictive model, a reporting layer that does not scale, a data pipeline that keeps breaking.
Engagement
How we engage
Every engagement starts with a scoping conversation — not a sales deck. We want to understand your data sources, your team structure, your verticals and what you are actually trying to solve. From there we scope the work, agree on delivery cycles and set a realistic timeline. We work in short iterations with regular checkpoints, so you are never three months away from seeing progress. Engagements can be project-based, retainer-based, or a combination — we structure it around what your operation actually needs.