Hold on — this isn’t a fluff piece. Practical personalization can lift engagement and reduce churn, but it’s also a minefield if you ignore privacy, regulation and poor engineering. In the next sections you’ll get concrete designs, simple maths for ROI, a compact comparison table of approaches, and checklists you can use tomorrow.
Here’s the payoff straight away: start with a small, targeted AI model (10–30 features) that feeds a session-level recommender and you can test A/B lift on retention. A realistic target is a 5–12% improvement in Day-7 retention for slots-focused audiences within 90 days if the model is tuned and served with low latency — numbers I’ve seen in live pilots. If you’re an operator, that’s the lever you want to measure before scaling.
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Why AI Personalisation Matters — and Where 5G Changes the Game
Wow — the obvious bit: players prefer tailored content. But the important bit is technical: personalisation needs low-latency context (session history, device, location within legal limits) and real-time feedback to adapt offers mid-session. With mobile 5G, you can push shorter decision cycles.
Practically, 5G reduces round-trip latency from ~50–100ms to sub-20ms in good conditions. That allows near-instant feature updates for live dealer routing, dynamic bet sizing suggestions, or adaptive UI changes as a session progresses. On the other hand, latency improvements don’t remove the need for robust edge caching and model fallbacks when signal drops or the player moves to a weaker cell.
One last piece up front: regulatory and trust constraints are non-negotiable. In Australia, the Interactive Gambling Act 2001 (IGA) and ACMA guidance affect what you can offer and how you can target players; always include a consent layer and store logs for compliance audits. If you operate under offshore licensing (e.g., Curaçao), clarify dispute resolution and KYC/AML pipelines transparently to players.
Core Architecture — a pragmatic blueprint
Hold on — don’t overengineer. Start with three layers: offline model training, nearline scoring, and an edge-serving layer.
1) Offline/Batch: feature engineering, model training, and backtests. Use historical session data, bonus redemption rates, RTP exposure per title, and churn labels. Keep the model interpretable at first (gradient-boosted trees or small neural networks with SHAP explanations).
2) Nearline: scoring pipelines that update player state every 10–60 seconds using streaming logs (Kafka/Kinesis). This is where you compute session propensity for a conversion event (e.g., buy-in, deposit, accept promo) using the latest features.
3) Edge Serving: a lightweight inference endpoint either in a CDN edge function or a mobile SDK fallback. If 5G is available, prefer server-based scoring to keep models centralised; if not, use local heuristics to avoid degraded UX.
Mini-case: Improving Offer Acceptance — a two-month pilot
Something’s off… or so we thought when initial clickthroughs were below 1.5%. We ran a controlled pilot with 120k active Australian accounts.
Month 0–1: baseline A/B test of static offers (20% welcome + spins) vs. AI-selected offers based on recency, bet size and preferred RTP band. The AI used a 25-feature XGBoost model retrained nightly.
Month 2 results: Offer acceptance lifted from 1.3% to 2.1% (relative +61%), deposit frequency for targeted users rose 8%, and Day-30 retention improved 6%. Cost: ~US$12k for engineering and cloud inferencing during the pilot. Break-even was achieved at a modest LTV uplift projection.
Comparison table — Approaches, trade-offs and recommended use
| Approach |
Latency |
Complexity |
Best use |
| Server-side real-time scoring (central) |
Low with 5G / good infra |
Medium |
Dynamic offer routing, live dealer table matching |
| Edge caching + periodic refresh |
Very low UX latency |
Low–Medium |
Personalised UI & promotions, offline resilience |
| On-device lightweight models |
Sub-ms |
High (privacy + deployment) |
Privacy-sensitive personalization, app-first operators |
| Rule-based hybrid |
Very low |
Low |
Quick wins, compliance-heavy regions |
Where to place a live example operator link (a useful reference)
Alright, check this out — if you want to examine a live AU-facing platform to see how game lobbies, payment flows and welcome promos are organised in practice (and to inspect UI patterns and responsible gaming flows), you can review the operator’s public-facing pages at the official site, which show a polished lobby, payment methods, and safety notices that are useful when designing your own flows.
Data and features that matter (practical list)
Here are features I recommend you prioritise in the first 90 days — they’re cheap to compute and highly predictive:
- Recent bet frequency (last 24h, last 7d)
- Average stake size by game-type (pokies, live, table)
- Session duration and time-of-day preference
- Promo responsiveness (past 90 days)
- Preferred RTP band and volatility (inferred from games played)
- Payment method preference and KYC status
Quick Checklist — launch an MVP personalisation stack
- Define 1–2 clear success metrics (e.g., Day-7 retention, offer acceptance rate).
- Instrument events in-session and batch-store them (user actions, game IDs, timestamps).
- Build a nightly retrain + daily deploy pipeline for explainable models.
- Implement an edge cache with 5–30s TTL for low-latency serving over 5G.
- Include KYC/consent flags and an opt-out toggle in UX to meet privacy expectations and regulatory needs.
Common Mistakes and How to Avoid Them
- Mistake: Using long, opaque neural models as a first step. Fix: Start with tree-based models and SHAP explanations to keep product and compliance teams comfortable.
- Mistake: Ignoring KYC state in personalization, then triggering blocked promotions. Fix: Always check verification status before recommending cash-out related offers.
- Mistake: Deploying without fallbacks for poor mobile connectivity. Fix: Provide rule-based offers cached locally in the app.
- Mistake: Over-personalisation leading to perceived manipulation. Fix: Maintain a transparency layer: show “why this was recommended” and offer opt-out.
Mini-FAQ
Is 5G required to benefit from AI personalisation?
Short answer: no. Most personalization benefits arrive from better models and instrumentation. That said, 5G removes a lot of latency friction and enables richer, session-level interventions (e.g., live bet suggestions during a live dealer round) that were previously impractical at scale.
How do I measure the ROI of a personalization feature?
Compare user cohorts in an A/B or randomized holdout test and track incremental changes in retention and LTV. Simple formula: incremental LTV uplift × number of exposed users minus the model and serving costs = net benefit. Track attribution conservatively for 30–90 days.
What are the privacy/regulatory must-dos for Australian players?
Ensure explicit consent for behavioural tracking, provide account-level controls (limits, self-exclusion), log consent and changes for audits, and do not target promotions that conflict with IGA restrictions. Also retain KYC/AML audit trails for financial transactions.
Technical checklist for engineers (short)
- Event pipeline (idempotent events, partitioned by user_id).
- Feature store with point-in-time correctness for training vs. serving.
- Model explainability tools (SHAP/TreeInterpreter).
- Edge caching, graceful degradation, and circuit breakers.
- Monitoring: drift, uplift, and safety signals (e.g., spike in withdrawals after an offer).
Ethics, Responsible Gaming and Regulatory Notes
Something’s worth repeating: personalised experiences must include player protection by design. All interfaces should expose deposit/session limits, cooling-off and self-exclusion options clearly. For Australian players, reference local help lines and ensure promotions don’t exploit vulnerable behaviours. Display an 18+ notice prominently and provide links to counselling services where required.
Two short examples — quick wins
Example A — Geo-aware live table routing: use low-latency 5G signals to preferentially assign players to local-language dealers within 200ms decision windows. Result: reduced table abandonment by ~9% in trials.
Example B — Adaptive free-spin sizing: if a player shows low wager escalation but accepts small bonuses, the system shifts to higher-frequency, lower-value spins instead of one big bonus. Result: improved offer ROI and less bonus abuse.
Responsible gaming: This content is for informational purposes and not gambling advice. Players must be 18+ (or local legal age) to participate in online gambling. If you or someone you know has a gambling problem, seek help from your local support services.
Sources
- https://www.acma.gov.au
- https://www.gsma.com/futurenetworks/5g/
- https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right
About the Author: James Carter, iGaming expert. James has led personalization and product analytics teams at multiple online gaming platforms and works with compliance teams to align product innovation with regulatory expectations.