Level C

What we do:

• We walk the whole flow on test accounts (desktop + mobile) - registration, deposit, play, withdrawal - with the onboarding corridor and two entry scenarios; we check key scenarios for stability, speed and interface correctness (QA); we review the lobby, search, vertical discoverability, account area, withdrawal and KYC gates; we measure timings on money steps
• We complete the qualitative picture with a behavioral layer on the warehouse: clicks, rage clicks, on-screen returns and drop-off. A session-recording service shows what happened on screen, but linking that to the player, deposit and churn only exists in the operator's data - without the warehouse it is just a video archive with no conclusions
• We go deep on the cashier: Acceptance Rate and method success, reasons for failed FTD by method, PSP and repeat attempts, transaction cost and comparative findings by method. With access to the payments back office we add extended analytics - AR deviations by method and PSP, spikes in declines, provider degradation
• We build the funnel on event history: Reg → FTD → Dep2+, the "deposit attempt to successful FTD" gap, deposit-chain distribution Dep1-Dep10+, distribution by time interval from registration to FTD and its effect on funnel depth, money flow - deposits, withdrawals, HOLD
• We run anti-fraud on the money path: we detect multi-accounting and bonus abuse in the warehouse, calculate the share of players with deposits but no single bet (a product defect or a fraud pattern), run external bot probes within agreed limits, and reconcile test-account identifiers against internal tagging - this checks whether the product catches what the warehouse models see. The focus is primarily on tier 1 GEO

Deliverable in the package:

• Page-by-page matrix of key URLs; player journey map across four stages, with evidence; onboarding-corridor map; comparison of two registration scenarios; friction log (desktop + mobile); QA check log
• CJM behavioral layer: clicks, rage clicks, on-screen returns, drop-off
• Cashier materials: methods, error copy, deposit variations; breakdown of the "deposit failed" flow; "deposit attempt to successful FTD" gap; failed FTD reasons (method / PSP / retry); Acceptance Rate and method success; transaction cost and comparative findings; extended cashier analytics
• Deposit-chain distribution Dep1-Dep10+; distribution by time interval from registration to FTD; money flow: deposits, withdrawals, HOLD; actual funnel Reg → FTD → Dep2+
• Game sample with opening-time measurements and lobby notes; breakdown of the search form, lobby structure and quick links; surface audit of game offerings; "vertical to retention / return" hypotheses; account-area breakdown; withdrawal and KYC materials or a record of the flow being missing / blocked; timings and reports on performance
• Share of players with deposits but no single bet; external bot probes; reconciliation of test-account identifiers against internal tagging; detection of multi-accounting and bonus abuse in the warehouse

What we do:

• On the core paths we run a full WCAG 2.2 AA audit and assess the UI from the player's position: clarity of terms, statuses, errors, limits, bonuses and path steps
• We check pressure signals (timers, scarcity, pop-ups), CTA hierarchy and screen overload
• We check what we saw on the surface against the actual CRM chain: in-app windows, frequency and channel - this shows whether on-screen pressure lines up with what the player actually receives in communications
• We editorially proofread copy and graphics; native-language fit is assessed only by native speakers, and offer, messaging and creative fit to the target GEO - by market residents
• We check currency, amount, date and phone formats against the target GEO and check tier-3 pages against an agreed list of sections: help, rules, promo landing pages, provider and category pages, legal texts

Deliverable in the package:

• Design findings and a WCAG 2.2 AA issue register
• Trigger, CTA and overload assessment with a list of optimizations; comparison of triggers, CTAs and overload with the CRM chain: in-app windows, frequency and channel
• Editorial register for copy and graphics
• Register of format and locale errors; recorded language and currency; assessment of fit to the target GEO
• Check of tier-3 pages against the agreed list of sections

What we do:

• We inventory the visible bonus offer (terms transparency, wager, risk to the operator), review the loyalty program and gamification, compare welcome / FTD offer personalization against Dep2 and Dep5, and record the 14-day communications feed on test accounts
• We calculate bonus load and effectiveness: accumulated issuance, the share of bonuses in GGR per player, gamification reach by participant share
• On event history we review the same thing by name: wager progress, usage, abuse patterns, and above all the attribution: exactly which bonus and which mechanic drove the deposit and the return. We measure gamification return the same way, along with its effect on LTV and churn
• We calculate the effect of the welcome package on the chain FTD → Dep2 → Dep3 → Dep4 → Dep5 and match the terms of the visible offers against the warehouse facts
• We compare the bonus load in the affiliate platform (bonuses issued) against the actual cost of bonuses in the warehouse (bonuses used), calculate the size of the gap and its effect on NGR and RevShare; the reconciliation-bridge summary is rolled up in C4
• We build the "RFMVPS strategic segment → offer and message" matrix. The VIP segment is separate, in module C6
• We formulate priorities for a personalized lobby and product: primarily by vertical and game type that the player actually plays - the link between vertical and retention is calculated in C5

Deliverable in the package:

• Map of general bonuses with an assessment; breakdown of the loyalty program and offer personalization; assessment after onboarding (welcome / FTD vs Dep2 / Dep5); gamification assessment; 14-day communications feed
• Bonus load and effectiveness on aggregates; accumulated bonus issuance and the share of bonuses in GGR per player; gamification reach on aggregates; lifecycle chains and reactivation policies
• Bonus Performance with a "bonus to deposit and return" attribution; gamification return and its effect on LTV and churn; effect of the welcome package on the chain FTD → Dep5; matching the terms of visible offers against the warehouse facts
• Comparison of bonus load in the affiliate platform and the actual cost of bonuses in the warehouse: the size of the gap and its effect on NGR / RevShare
• "RFMVPS strategic segment → offer and message" matrix; priorities for a personalized lobby and product by vertical and game type

What we do:

• We cut the funnel by entry type and calculate FTD and NGR by source, affiliate and country
• We calculate concentration: the top-10 affiliates' share and the leader's share
• We review the quality of the cohort for each affiliate with a flow of 50+ FTD a month: retention, churn, share of one-time depositors, bonus load, how many "whales" and "sharks" are in the flow
• We build a demographic profile of the base: age, gender, geo and ARPU by segment
• We trace the path from the click: click-to-registration and click-to-deposit conversion by affiliate and source type. We reconcile paid and organic traffic for consistency
• We break down three CACs - full, affiliate and blended with organic in the denominator - on the affiliate layer and on the actuals layer, and build a reconciliation bridge between the layers: FTD, bonuses issued vs used, the admin fee already inside the affiliate platform's GGR, NGR
• We measure three payback periods - by NGR, by gross profit and by operating profit - on every CAC and on both layers
• We cut payback by affiliate, source type and target country, alongside the funnel
• We break it down further by deal terms and price corridors (CPA / RevShare / Hybrid) within the affiliate × source type × target country combination: the same affiliate's prices can differ by geo. We take affiliates with 50+ FTD a month
• We collect traffic tagging - source type and deal terms on the path to registration and FTD - and formulate decisions: a cumulative scale / cut / hold / renegotiate map by affiliate, source type and deal terms (from 100 FTD)
• We look for hidden fraud: cohort anomalies, including time-interval metrics, relative to the average of the relevant market - benchmark tier 1 GEO

Deliverable in the package:

• Funnel cut: affiliate entry vs organic; FTD / NGR cut by sources and affiliates, paid vs organic share; concentration of top affiliates (top 10 and leader's share); cohort distribution by affiliate; distribution by age, gender and geo with ARPU
• Click funnel by affiliate and source type
• Three affiliate-layer CACs and three actuals-layer CACs with a reconciliation bridge; three payback periods on both layers
• Funnel and payback broken down by affiliate, source type and target country (from 50 FTD / month); payback by deal terms and price corridors
• Cumulative scale / cut / hold / renegotiate map (from 100 FTD); hidden fraud - cohort anomalies relative to the relevant market

What we do:

• We calculate retention by cohort D1 / D3 / D7 / D14 / D30 / D90 (where fields are available), the retention curve and early churn with the share of one-day FTD, D7→D30 and D30→D90 decay, the share of one-time depositors against repeat depositors and the distribution of closed accounts; we reconcile communication density against dips in the curve
• We calculate churn with a comparison baseline and the implied daily outflow; we build churn prediction by ACTIVE / RISK segments - by recency, accounting for behavioral patterns
• We determine Churned by the last change to the real balance - a deposit, withdrawal or bet, not just account login; through CRM we link them to touches and offers: which messages did or did not bring activity back
• We calculate the full RFMVPS - six axes, including deposit-chain speed and behavioral security - and build the segment distribution; this is how we surface "sharks" wrongly tagged as "whales" and formulate reactivation policies from COLD and LOST
• We calculate volume distribution by provider, category and vertical on the warehouse actuals, link a specific player's vertical to their retention and return, and measure tournament participation: effect on the next deposit and retention

Deliverable in the package:

• Retention by cohort D1 / D3 / D7 / D14 / D30 / D90; retention curve and early churn, including the share of one-day FTD; D7→D30 decay; share of one-time depositors against repeat depositors; distribution of closed accounts; reconciliation of communication density with dips in the curve
• Churn with a comparison baseline and the implied daily outflow; churn prediction - ACTIVE / RISK segments
• Churned by the last change to the real balance; link between Churned and touches and offers
• Segment distribution on the full RFMVPS: six axes, including deposit-chain speed and behavioral security
• Volume distribution by provider, category and vertical; link between a specific player's vertical and their retention and return; tournament participation and effect on the next deposit and retention

What we do:

• We calculate VIP by grade and contribution to NGR; we reconcile the internal VIP list against actual revenue per player, against the full RFMVPS and against gross profit per depositor - who the status describes in fact, and who only by name; we track the dynamics of new, returned, inactive and deactivated VIPs
• We analyze VIP behaviour: session and betting behavior, behavior-based churn risk, personal offers and retention experience
• We calculate the speed of becoming VIP: the distribution of days and deposits from registration to status, by entry cohort
• We single out "sharks" - players with a negative contribution by withdrawal-to-deposit ratio - and calculate the churn rate inside the VIP segment separately from overall portfolio outflow
• We calculate the share of the personal bonus budget going to the VIP segment, and its return; through CRM we build the "VIP grade → offer and message" matrix
• We track the reactivation of dormant and lost VIPs: the share of returners, the return's contribution to NGR and the effectiveness of reactivation campaigns by grade

Deliverable in the package:

• VIP by grade and contribution to NGR; reconciliation of the internal VIP list against actual revenue per player; VIP dynamics over the period
• VIP behaviour: session and betting behavior, churn risk, personal offers
• Speed of becoming VIP by entry cohort; breakdown of "sharks"; churn rate in the VIP segment
• Share of the personal bonus budget in the VIP segment and its return; offer and message assignment for the VIP segment by grade
• Reactivation of dormant and lost VIPs: share of returners, the return's contribution to NGR, effectiveness of campaigns by grade

What we do:

• We build the waterfall down to GGR: Bets - Wins → GGR under white label; In - Out → GGR under Turn Key; then - Bonus → NGR; - Royalty → Gross Profit; - ∑ CAC (PP) → Income
• We calculate ratios: the Royalty and ∑ CAC shares, gross and operating margin (Gross Profit / Income), gross profit per depositor, HOLD, the withdrawal-to-deposit ratio
• We calculate the cost of bonuses; Royalty (providers / white label) and PSP; ∑ CAC (PP) and marketing; the cost distribution by provider fee groups and how effectively the negative-GGR buffer is managed. With the full warehouse we build the ladder down to Income and the money flow
• We calculate product activity aggregates on the warehouse actuals - bet volume and count, average per player, bet-to-deposit ratio, RTP and Margin
• We calculate LTV, ARPPU and the share of bonuses in GGR, LTV on the 7 / 30 / 60 / 90 horizons and cohort revenue per FTD by entry cohort
• We build unit economics in two forms: on cumulative revenue without cost allocation, and with cost allocation per player and per product

Deliverable in the package:

• Operator P&L: waterfall GGR → NGR → Gross Profit → Income (→ Profit with full inputs on direct marketing and events)
• Cost of bonuses, Royalty (PSP / providers / WL) and marketing; cost distribution by provider fee groups and an assessment of how effectively the negative-GGR buffer is managed
• Product activity aggregates: bets, average per player, bet / dep, RTP and Margin
• LTV, ARPPU and the share of bonuses in GGR; LTV on the 7 / 30 / 60 / 90 horizons; cohort revenue per FTD by entry cohort
• Per-player unit economics on cumulative revenue without cost allocation; unit economics with cost allocation per player and per product

What we do:

• We inventory the product offer to the player: jackpots, lottery, referral, VIP program, tournaments, app, mobile, gamification, knowledge base, catalog composition and providers, presence of sportsbook / prediction markets - with no functional audit
• We look at support from two sides: channels and discoverability on the public surface, then on the data - ticket load, SLA, CSAT / NPS, staffing and script QA, the improvement backlog and above all: conversion to deposit and churn after contact broken down by topic. The ticketing system knows response time, but what a contact actually cost in deposits and retention is only visible on the warehouse. This needs the CS back office and the warehouse
• We calculate the distribution of outbound calls by segment and their effect on deposit conversion - with access to Call Service
• We take SEO at two depths: top-level findings (indexability, robots / sitemap, speed of key URLs), and, with access to SEO tools, rankings and organic visibility with a visibility map and next steps
• We collect reputation from gambling portals and forums: complaint topics and tone relative to 3 agreed competitors - with no survey of your own base and no paid media monitoring
• We build an eleven-axis SWOT against 3 competitors, every claim backed by a screenshot, link and observation date

Deliverable in the package:

• Map of player-facing offers and features
• Top-level audit of the support service; ticket load, SLA, CSAT / NPS, staffing and script QA; improvement backlog; conversion to deposit and churn after contact by topic
• Distribution of outbound calls by segment and their effect on deposit conversion
• Top-level SEO findings; rankings and organic visibility
• Public brand trust with growth points, including a distribution by complaint topic; comparison of reputation tone with 3 competitors
• Product SWOT against 3 competitors and comparison axes: localization, USP, UX / UI, registration form, cashier and payment methods, deposit and withdrawal limits, providers visible at onboarding, the player offering, bonuses and the loyalty program, support service, brand trust; "product vs competitors" comparison table with a limits table; opportunities and threats

What we do:

• We agree the landscape snapshot of the systems: which event tables and fields are actually available - the package composition adapts to that
• We roll up an executive summary and separate what the data confirms from what needs access that turned out not to be in scope
• We formulate quick wins: revisiting offer terms, cutting and renegotiating unprofitable sources, correcting VIP policies, blocking toxic patterns
• We consolidate the findings of all C1-C8 modules into a single report and build a change roadmap prioritized by contribution to NGR and by risk, ranking tasks by operational payoff and implementation complexity
• We give a qualitative assessment of how proposed new mechanics and tools would affect conversion, LTV and trust

Deliverable in the package:

• Systems landscape snapshot
• Executive summary; priorities confirmed by the numbers
• A list of priority tasks (quick wins); proposals for long-term strategies
• A hypothesis-testing plan; a "Level A observation to Level B / C KPI family" map; a list of diagnostic pairs
• Proposals for integrating new mechanics and tools with a qualitative forecast of their effect on conversion, LTV and trust; recommendations for raising brand trust

A live RFMVPS attribute in the CRM and a regular recalculation of the six axes on top of the full scoring in the C5 module's report: a standing loop instead of a one-off snapshot

Auto-issuance and orchestration of personal offers on top of the C3 module's "segment → offer" matrix: a standing loop instead of a one-off assignment in the report

A live buffer, thresholds, alerts and auto-actions on top of the C7 module's buffer-management effectiveness: a standing loop instead of a one-off plan

Personal lobby and product delivery at runtime on top of the C3 module's segment priorities: a standing loop instead of a report recommendation

An alert when the Acceptance Rate drops by method, spikes in declines, provider degradation, on top of the C1 module's extended cashier analytics: continuous monitoring instead of a one-off cut. Requires additional access to the payments back office (PaymentIQ / DevCode, FinteqHub and similar)

Continuous collection of clicks, rage clicks, on-screen returns and scroll on top of the C1 module's one-off behavioral-layer map: linking behavior to the player, deposit and churn instead of a video archive with no conclusions. Requires additional access to page tagging and session services

An alert on SLA breaches and queue spikes plus automated routing and template responses, on top of the C8 module's support analytics: a standing loop instead of a one-off cut. Requires additional access to the Customer Support back office

Discovery: interviews, a map of manual steps in marketing, support and product, a shortlist of use cases and a list of the data needed. Workflow audit: bottlenecks, manual operations and delays, a rollout roadmap naming the points of impact on margin. The rollout itself is a separate proposal after the audit. Requires additional access to workflow regulations

Automatic collection of trends and new reviews on top of the Level A breakdown of open sources: continuous monitoring instead of a one-off cut. Requires additional access to review-portal accounts