How AI Analysis Revealed that 45% of Contacts Were About Withdrawals and Bonuses and Created a Roadmap to Reduce Repeat Contacts by 35% | Real Case Study
In-depth analysis identified that banking data errors and misaligned marketing promises are the biggest invisible bottlenecks. Solution: automatic front-end validation, promotions eligibility engine and guided self-service for massive deflection.
Main Contact Reasons
We analyzed hundreds of support interactions to map the most recurring patterns
Concentration by Problem Category
Analysis revealed massive concentration on financial and promotional topics:
| Category | Main Issues | Estimated Impact |
|---|---|---|
| Withdrawal | Refusal, minimum limit, "processing", banking data error, PIX instability | ~25% |
| Bonuses & Missions | Mission used/ended, wheel didn't appear, bonus not credited, SMS promotions | ~20% |
| Deposit | Amount not credited, "deleted" account, promised bonus didn't appear | ~15% |
| Game/Bet Failures | Bet debited without entering, incorrect result, game freezing | ~15% |
| Account & Security | Reactivation, password/email recovery, phone change, KYC | ~15% |
| Responsible Gaming | Problem gambling, self-exclusion, refunds due to frustration | ~10% |
Critical Insight
Friction Points with Highest Impact
Three critical sources of inefficiency identified by AI
Banking Data: Root Cause #1 of Refusals
CriticalPredictable errors: Nubank digits, BB vs Nu bank, savings vs checking, closed account. Appear as root cause of refusal and massive rework.
Marketing Promises vs Reality
CriticalSMS spins, "cashback" found on social media, birthday bonus "no longer exists". This becomes contact and generates frustration, even when support responds correctly.
Screenshot/Video Proof Dependency
High FrequencyMany support interactions become back-and-forth requesting attachments, with several cases ending due to inactivity before completion.
How Cases Were Resolved
Quick Resolution
When the problem is "how to": withdrawals resolved with clear steps (Wallet/Profile menu etc.) and banking data verification.
High efficiency - AutomatableSpecialist Team Resolution
Bet failure and deposit frequently follow "ticket opened" pattern and subsequent return with refund or correction.
Medium efficiency - Requires evidenceHigh Screenshot/Video Incidence
Many support interactions become back-and-forth requesting attachments. Cases end due to inactivity before completion.
Low efficiency - High abandonmentEffort Waste in KYC
Timeline with Expected Gains
Structured in three progressive impact waves
Short Term (0-14 days)
Quick Wins
Implementable Actions:
- Smart forms by reason: Withdrawal (bank, account type, PIX key, exact error) | Game (bet ID, game name, time, balance before/after)
- Automatic banking data validation on front-end: Digits per bank, closed account blocking, savings alert when not supported
- Ready responses for recurring topics: "Withdrawal processing up to 2h", "PIX/Central Bank unstable", "bonus within 4h", "mission ended"
- Risk protocol: Problem gambling (empathetic response, self-exclusion, priority channel) | Threat (de-escalation language, audit check)
Expected Gains:
- 10-20% fewer repeat contacts on withdrawal and bonus
- 5-10% AHT reduction through standardization
- Immediate reduction in escalations and reputational risk
Medium Term (15-60 days)
Deflection & Automation
Implementable Actions:
- Status center and proactive communications: Status page for PIX/Central Bank and "store/VIP club" maintenance | In-app banner
- Guided self-service: Step-by-step flow for withdrawal and banking data correction | Recover access (email, token, resend)
- Promotions eligibility engine: Agent sees on screen: active promotion, deadline, eligible, already redeemed, mission completed on date X
- Upload and evidence improvement: Mobile attachment with visual instruction, "complete screenshot" check, option to send textual data
Expected Gains:
- 20-35% deflection in bonuses/missions and "how to withdraw"
- 15-25% fewer repeat contacts on game failures
- Fewer "what's happening" contacts
- Fewer abandonments due to attachment difficulty
Long Term (61-180 days)
Structural Transformation
Implementable Actions:
- Observability and financial reconciliation: Automatic detection of "deposit not credited" and "bet debited without entry" with alert and preventive refund
- Root cause quality management: Weekly dashboard: top reasons, top banks, top games, abandonment rate, time to solution, refund rate
- Responsible Gaming governance layer: Structured flow for self-exclusion, limits, blocks and monitoring
Expected Gains:
- 30-45% reduction in avoidable contacts (withdrawal, bonus, status)
- Significant drop in complaints due to delay and "promotion not found"
- Fewer critical incidents and better compliance
- Focus on improvements with real ROI
5 High-Impact Actions by Topic
Withdrawal
Quick WinAttack "banking data" as root cause number 1, with validation and improved UX.
Bonuses/Missions
Quick WinBring "benefit timeline" to agent screen (completed on, redeemed on, eligible until).
Game/Bet Failures
Quick WinStandardize minimum collection (ID, time, game, balance) and contact provider with complete package.
Access and Recovery
Quick WinSpecific path for Hotmail and token, with fallback to Gmail and automatic reissue.
KYC
Quick WinRequest documents only when necessary. If it's "maintenance information", first inform, then request validation if sensitive action required.
Cumulative Operational Gain
Progressive impact of the three implementation waves
Projected Contact Reduction by Phase
| Phase | Timeline | Repeat Contact Reduction | Deflection |
|---|---|---|---|
| Short Term | 0-14 days | 10-20% | 5-10% AHT |
| Medium Term | 15-60 days | 20-35% | 20-35% bonus/withdrawals |
| Long Term | 61-180 days | 30-45% | 45% avoidable |
Executive Conclusion
Progressive implementation of automatic validation, visible eligibility engine and guided self-service would allow realistic reduction of 30-45% of avoidable contacts, with measurable gains in AHT, NPS and operational cost.
AI-Powered Interaction Analysis by CYF
Transforming support interactions into critical operational intelligence for iGaming
Our Solution
We use automatic ticket and chat analysis + specialized language models (LLMs) + hybrid AI pipelines to understand:
- Banking data error patterns (by bank, account type, digits)
- Misalignment between marketing promises and operational reality
- Abandonment points due to evidence difficulty (screenshot/video)
- Topics with high recurrence and low deflection (bonuses, missions, withdrawals)
- Reputational risk cases (problem gambling, threats, complaint sites)
This combination of support analysis + AI reveals insights like "45% about withdrawals and bonuses" and "banking data = root cause #1" — transforming friction into opportunity for automation and massive deflection.
