BetConstruct AI: Personalisation Lifts Turnover 10-15%
Turnover gains of 10% to 15%. That's the figure BetConstruct AI attaches to mature personalisation deployments, in the specific areas where the tech gets applied. Mushegh Khachatryan, the company's Chief AI Officer, is careful to avoid a universal number: results shift with the operator, the market, and how mature the product already is. Recommendation systems across its deployments, though, have delivered uplift ranging from high single digits to significantly higher levels.
That spread matters. High single digits is the kind of return an operator can model. Mid-teens in targeted areas is where personalisation stops being a feature and starts being a P&L line. Khachatryan frames the effect as compounding rather than singular: better game discovery, retention, CRM conversion and bonus efficiency stack on top of each other. His description of AI: an intelligence layer sitting across many decisions, not one tool chasing one KPI.
The mechanism, he argues, is friction. Players who find relevant content fast spend less time hunting and more time playing. That shows up as deeper sessions, more game discovery, higher bet frequency and better retention. Though Khachatryan says the goal isn't to stretch playing time for its own sake. Relevance comes first; the commercial numbers follow.
Personalisation at BetConstruct goes well beyond slot recommendations. The system tracks what players like and ignore, when and how they play, how sensitive they are to price and bonuses, and how that behaviour shifts. It then feeds the lobby, promotions, messaging and, pointedly, decisions about when not to contact a player at all. Khachatryan's stated aim: replace one casino experience served to a million players with a million tailored ones.
The bigger operational shift is agents. Until recently, AI in iGaming sat in the background running recommendation, churn and fraud models. BetConstruct is now wiring those models, the data and the operator's tools together through a conversational layer. A typical chain: an operator asks what happened to casino revenue yesterday, the agent explains the change, the operator asks which segments drove the decline, then instructs it to build a campaign for the segment with the highest churn risk. The company calls this the Operational Copilot. The interface is chat; the complexity stays behind it.
Where agents work best is predictable. Repetitive, data-heavy tasks with measurable outcomes: CRM, analytics, segmentation, campaign prep, customer support, content discovery and operational monitoring. An agent can scan millions of data points and surface patterns a human would miss. The line is equally clear on the other side. Financial decisions, responsible gambling interventions, regulatory calls and unusual customer situations stay with people. Khachatryan's pitch is workload reduction, not headcount removal, freeing staff for judgement and accountability.
Risk detection is where the machine-learning approach diverges hardest from rules-based systems. A traditional setup blocks an account after a set number of events. BetConstruct's models read hundreds of behavioural signals at once, covering deposits, withdrawals, betting patterns, bonus usage, game selection and deviations from a player's normal activity, and flag anomalies for review before a rule is technically broken. The same machinery feeds negative-NGR prediction, hidden VIP detection and churn models. Fraud screening moves from post-mortem reporting to early warning.
On keeping pace with model releases, BetConstruct isn't betting on a winner. The architecture is model-agnostic by design. New models get tested against real iGaming use cases, and anything that performs better gets adopted quickly. Khachatryan is blunt about where the edge sits: the models will be available to everyone, so the advantage is the operator's data, domain knowledge and ability to turn a model into a product that moves revenue. For affiliates, that cuts both ways. Operators running tighter segmentation and churn prediction will bid harder for players who actually retain, and the value of traffic that churns on arrival keeps falling. The next number to watch: how quickly those 10-15% turnover gains show up in operators' reporting, and whether they hold once the novelty of conversational tools wears off.