Complete business review produced from isolated raw data analysis. Every metric verified from source Excel files, BigQuery exports, and transcripts. Period: January 5 – April 13, 2026.
Every metric below is derived from raw Excel files and BigQuery exports — not pulled from prior intelligence files. See Methodology & Sources for per-metric verification status and calculation definitions.
Every recommendation in this review ladders to these targets. If a change doesn't move one of these levers, we cut it.
This business review was rebuilt from scratch. The V1 review contained 8 critical errors that fundamentally misrepresented UGCMA's performance. This V2 was produced by re-analyzing every raw data source in isolation.
Close rate has fallen from 14.1% in February to 5.9% in April — a 58% decline in 60 days. At February rates, the business would generate an additional $504K per month.
ROAS on revenue fell from 3.69x (Feb) to 0.59x (Apr partial). Spend increased 70% from January to March while close rate fell 37%.
The prior review inverted revenue attribution (claimed 88% organic, actually ~76% paid), overstated close rate by 6.5x, misidentified the primary ad channel, and missed an entire email program.
| Metric | V1 (Wrong) | V2 (Corrected) | Why It Matters |
|---|---|---|---|
| Close Rate | 67.8% | 10.4% | V1 overstated by 6.5x |
| Revenue Split | 87.8% organic / 10% paid | ~76% paid / 24% organic | Meta is the primary driver, not organic |
| Average Ticket | $3,519 | $5,295 | V1 underestimated by 50% |
| Primary Channel | TikTok ($74.7K/mo) | Meta ($610K total) | Meta spends 8.2x more than TikTok |
| Paid CPA | $3,913 | $1,896 (Meta) | V1 used TikTok-only denominator |
| Paid ROAS | 0.9x (unprofitable) | 2.77x (Meta, revenue) | V1 claimed paid was unprofitable. It's not. |
| Email Marketing | "None exists" | 94 broadcasts + 10-email nurture | V1 completely missed an active channel |
| Total Paid Closes | 23 | 348 (322 Meta + 26 TikTok) | V1 undercounted by 15x |
$610K spend, Jan–Apr. Drives 322 closes at $1,896 CPA. The revenue engine.
Paid Engine$75K spend, Mar–Apr. 26 closes. $6.07 CPL on LEADS campaigns. PURCHASE campaigns wasted $21K.
Paid85% of organic students come from IG. Engagement nearly doubled: 3.63% → 7.20% in 7 months.
OrganicMohni Makers (298 episodes) + UGC 101 (47 episodes). Top-of-funnel awareness, not directly tracked.
Organic| Tier | Avg Price | Share of Deals | Cash Collection |
|---|---|---|---|
| Pro (1:1 coaching) | ~$6,400 | 62.6% | ~80% |
| Accelerator (community) | ~$3,000 | 14.1% | ~65% |
| Lower tier | ~$2,000 | 19.2% | 42.8% |
| UGC Guide | FREE | Lead magnet | N/A |
The $2,000 tier has a 42.8% cash collection rate — well below the blended CPA of $1,461. The business may be losing money on lower-tier enrollments on a cash basis.
Where is money leaking? The full funnel from ad impression to close, with actual rates versus KPI targets.
| Stage | Volume | Rate | KPI Target | Status |
|---|---|---|---|---|
| Ad Spend | $610,620 | — | $40K/mo | OVER 2.5x |
| Registrants | 74,127 | — | 6,154/mo | BEAT (2x) |
| CPL | $8.24 | — | $6.50 | MISS (+27%) |
| Booked Calls | 3,094 | 4.17% of reg | 3.85% | ON TARGET |
| Close Rate | 10.4% | of booked | 13.5% | MISS (-23%) |
| Avg Revenue/Close | $5,250 | — | $4,150 | BEAT (+26%) |
| CPA | $1,896 | — | $1,252 | MISS (+51%) |
| ROAS (Revenue) | 2.77x | — | 3.31x | MISS (-16%) |
| ROAS (Cash) | 2.11x | — | 2.08x | HIT |
The funnel is overscaled at the top (2.5x budget, 2x registrants) but leaking at the bottom (close rate 23% below target). Higher ticket ($5,250 vs $4,150 target) partially compensates, but not enough to offset the CPA overshoot. The close rate is the bottleneck — fix it, and the entire funnel flips to profitable.
| Month | Closes | Booked Calls | Close Rate | Trend |
|---|---|---|---|---|
| January | 61 | 592 | 10.3% | Baseline |
| February | 126 | 895 | 14.1% | Peak |
| March | 118 | 1,319 | 8.9% | Collapse begins |
| April (partial) | 17 | 288 | 5.9% | Crisis |
Close rate has collapsed 58% from February's peak. If March's 1,319 booked calls had converted at February's 14.1% rate, that month alone would have produced 186 closes (vs actual 118) — an additional 68 closes worth $357K in revenue.
The business is cash-flow positive on paid acquisition today. Each paid close generates $2,111 in immediate net cash after ad cost recovery (Meta cash/close $4,007 − CPA $1,896). At 322 paid closes, that's $680K in immediate margin on ad spend.
Where is the ad spend going? Meta is the primary revenue engine — $610K spend across 13 weeks, 24 webinar instances.
| Month | Spend | Closes | Revenue | ROAS (Rev) | ROAS (Cash) | Close Rate |
|---|---|---|---|---|---|---|
| January | $113,723 | 61 | $311,585 | 2.74x | 1.98x | 10.3% |
| February | $190,497 | 126 | $703,935 | 3.69x | 2.79x | 14.1% |
| March | $226,725 | 118 | $628,055 | 2.77x | 2.06x | 8.9% |
| April (partial) | $79,675 | 17 | $47,275 | 0.59x | 0.85x | 5.9% |
ROAS collapsed from 3.69x (Feb) to 0.59x (Apr partial). Spend increased 70% from Jan→Mar while close rate fell 37%. The marginal ROAS on March's incremental $36K spend over February is negative. Weeks spending over $55K deliver CPLs 142% higher and ROAS 51% lower than weeks under $35K.
Spend $25,094 (below median). 35 closes on 181 booked calls = 19.3% close rate. CPA of $717 — lowest in the entire period. $186,200 revenue.
Peak PerformanceSpend $51,976 (above median). Only 11 closes on 236 booked calls = 4.7% close rate. CPL of $11.67 — audience fatigue. Likely cash-flow negative.
Bottom Performance
$21,228 spent on TikTok PURCHASE-objective campaigns produced ZERO conversions. Structurally incompatible with a multi-step webinar-to-call funnel. This is a 5-minute fix: reallocate to LEADS → ~3,500 additional registrants at $6.07 CPL.
| Spend Tier | Weeks | Avg CPL | Avg ROAS |
|---|---|---|---|
| Under $35K | 7 | $6.84 | 3.60x |
| $35K–$55K | 8 | $7.60 | 3.07x |
| Over $55K | 3 | $16.54 | 1.77x |
How does the machine work? Revenue trajectory, cash collection, content performance, and the CRM gap.
| Month | Revenue | Cash Collected | Closes | Ad Spend | ROAS |
|---|---|---|---|---|---|
| January | $536,230 | $385,906 | 105 | $139,601 | 3.84x |
| February | $770,950 | $573,078 | 149 | $180,756 | 4.26x |
| March | $790,200 | $621,950 | 146 | $253,393 | 3.12x |
| April (partial) | $115,900 | $95,483 | 18 | $64,260 | 1.80x |
Revenue grew 47% from Jan→Feb, then flattened at ~$780K–$790K in Feb–Mar despite a 40% increase in ad spend ($181K→$253K). Spend is scaling faster than revenue — the funnel is hitting diminishing returns. Each marginal dollar of ad spend in March produced less revenue than in February.
3.63% → 7.20% over 7 months. Saves and shares exploded in Feb–Mar 2026.
Growth3x views, 3.4x saves, and 2.7x shares — yet only 22% of March posts were carousels.
OpportunityV1 claimed email marketing was "non-existent." This is wrong.
GHL functions as a booking calendar only — not a CRM. Across 419 days of BigQuery data:
All revenue tracking is manual Excel reconciliation. The business cannot diagnose where deals stall, measure closer performance individually, or forecast reliably.
What needs fixing, and in what order? Prioritized by urgency and dollar impact.
58% decline from February peak. Monthly impact: ~$504K in unrealized revenue.
3.69x (Feb) → 0.59x (Apr partial). Recent weeks are likely cash-flow negative on paid acquisition.
PURCHASE objective = ZERO conversions. Structurally incompatible with webinar-to-call funnel.
GHL tracks bookings but $0 revenue, 0 opportunities. All data in manual Excel spreadsheets.
94 broadcasts with no opens/clicks data, no segmentation, no A/B testing.
Budget increased from $10K/webinar target to $25K+ actual. Higher spend = lower ROAS consistently.
$2,000 tier: 42.8% cash collection vs 80% for $6,400 tier. Each $2,000 deal yields only $855 — below blended CPA.
What moves the needle most? The four biggest levers ranked by monthly revenue impact.
| Lever | Current | Target | Monthly Impact | Difficulty |
|---|---|---|---|---|
| Close rate recovery | 5.9% (Apr) | 14.1% (Feb peak) | +$504K/mo | High |
| CPL optimization | $8.24 | $6.50 | +27% more leads | Medium |
| TikTok PURCHASE → LEADS | $21K wasted | Reallocate | +~3,500 registrants | Low |
| Payment collection | 42.8% | 65%+ | Revenue already booked | Medium |
Close rate recovery is worth more than all other levers combined. A 1 percentage point improvement at current volume is worth ~$60K/month. This must be the #1 priority — every other optimization is noise until the close rate stabilizes.
These gaps prevent full diagnosis and must be addressed to manage the business at scale.
Every claim in this review is grounded in raw data. This section documents exactly where each number comes from, how it was calculated, what we can verify, and what we cannot.
| Source | Type | Date Range | Metrics Provided | Access |
|---|---|---|---|---|
| Ad tracking - SYDNEY.xlsx | Excel | Jan 12 – Apr 9, 2026 | Meta spend, registrants, booked calls, closes, revenue, cash (weekly + monthly) | Parsed |
| Customers - SYDNEY.xlsx | Excel | Jan – Apr 2026 | Deal-level pricing tiers, cash collection rates, product mix | Parsed |
| Webinar Reviews - Sydney.xlsx | Excel | Jan – Apr 2026 | Webinar-level close rates, attendance, organic vs paid attribution | Parsed |
| Tiktok Ads_performance_UGCMastery_001.xlsx | Excel | Mar – Apr 2026 | TikTok campaign spend, registrants, objective split (LEADS vs PURCHASE) | Parsed |
| Tiktok Webinar Reviews - Syd.xlsx | Excel | Mar – Apr 2026 | TikTok-attributed booked calls, closes, revenue | Parsed |
| Tiktok Customers - Syd.xlsx | Excel | Mar – Apr 2026 | TikTok-attributed customer records | Parsed |
| ShortForm Content Data Review - SYDNEY.xlsx | Excel | Sep 2025 – Mar 2026 | Instagram engagement rates, carousel vs reel performance | Cited only |
| BQ: ugc_mastery_academy_h_level.daily_metrics | BigQuery | 419 days (through Apr 2026) | GHL/CRM booking counts, pipeline status | Queried |
| BQ: ugc_mastery_academy_facebook_ads.campaign_history | BigQuery | Jan – Apr 2026 | Meta campaign names, statuses, objective types | Queried |
| performance-data-2025-10-to-2026-04.csv | CSV | Oct 2025 – Apr 2026 | Charlie AI / lead activation messaging activity (not revenue) | Parsed |
| opportunity-stats-report-1/2.pdf | Jan – Apr 2026 | GHL pipeline stage counts, opportunity statuses | Cited only | |
| 80-student survey (self-reported) | Survey | ~Mar 2026 | Instagram acquisition attribution ("85% found via IG") | Survey |
"Parsed" = data was extracted into structured JSON and all derived metrics can be independently verified. "Cited only" = data was referenced from the original Excel file but not available as machine-readable data for independent verification. "Survey" = self-reported data with inherent sampling limitations.
The source data contains an internal inconsistency: paid_closes (348) + organic_closes (96) = 444, but total_closes = 418. After review, the breakdown that sums correctly is:
| Source | Closes | Verification |
|---|---|---|
| Meta Ads (weekly sum) | 322 | 13 weekly rows sum to 322 |
| TikTok Ads | 26 | From TikTok customer files |
| Organic / Other | 70 | Derived: 418 − 322 − 26 = 70 |
| Total | 418 | Matches hero stat |
The source JSON labels 96 records as "organic closes" but this appears to include 26 TikTok-attributed closes. True organic is 70 closes. The hero KPI card has been corrected to show "322 Meta + 26 TikTok + 70 organic = 418."
| Report Section | Status | Detail |
|---|---|---|
| Hero KPIs (revenue, cash, closes, deal value, spend) | Math verified | All 6 hero stats independently derivable from JSON. All arithmetic confirmed. |
| Meta ROAS (revenue & cash) | Math verified | Both ratios confirmed: $1,690,850/$610,620=2.77x, $1,290,190/$610,620=2.11x |
| Blended ROAS (2.71x) | Math verified | Uses paid revenue only: ($1,690,850+$165,630)/$684,972 = 2.71x |
| Meta CPA, CPL, Close Rate, Booked Rate | Math verified | All 4 ratios independently confirmed from spend/regs/booked/closes totals. |
| Weekly Meta performance (13 rows) | Math verified | All column sums match stated totals. 1-unit booked rounding noted. |
| Monthly Meta performance (4 rows) | Math verified | All column sums match stated totals. Monthly ROAS recalculated and confirmed. |
| Monthly close rate trend | Math verified | Jan 10.3%, Feb 14.1%, Mar 8.9%, Apr 5.9% — all closes/booked confirmed. |
| Close rate decline (58%) | Math verified | (14.1−5.9)/14.1 = 58.2%. Confirmed. |
| TikTok ROAS, CPA, CPL | Math verified | $165,630/$74,352=2.23x, $74,352/26=$2,860, $53,124/8,748=$6.07 |
| TikTok PURCHASE waste ($21K) | Math verified | $21,228 spend, 0 registrants, 0 closes, $0 revenue. Confirmed from objective split. |
| Product mix (tier pricing, shares) | Cited from Excel | Pricing tiers from Customers - SYDNEY.xlsx. Not independently recalculated. |
| Cash collection by tier (42.8%) | Cited from Excel | Derived from Customers Excel. Requires original file for independent verification. |
| Instagram engagement (3.63% → 7.20%) | Cited from Excel | From ShortForm Content Data Review. Not machine-readable for verification. |
| Carousel vs reel multipliers (3x/3.4x/2.7x) | Cited from Excel | From ShortForm Content Data Review. Not independently verified. |
| 85% of organic students from IG | 80-student survey | Self-reported by ~80 students. Not cross-referenced with platform data. |
| Email: 94 broadcasts, 10-email nurture | Cited from markdown | Sourced from email-marketing-syd.md and lead-magnet-email-sequence.md files. |
| BU2 monthly revenue ($182K) | Cited from source | From webinar review data. Single-month figure (Feb 2026). |
| Podcast episode counts (298, 47) | Not verified | Cited from brand context. Not independently counted from platform data. |
| Net cash per close ($2,111) | Verified | Meta cash/close ($1,290,190 ÷ 322 = $4,007) − Meta CPA ($610,620 ÷ 322 = $1,896) = $2,111. Total margin: $2,111 × 322 = $680K. |
| ~$504K/mo unrealized revenue | Scenario calc | Projection using close rate gap × implied booked calls × avg deal. Formula documented above. |
| $60K/month per close-rate ppt | Scenario calc | Sensitivity estimate. Exact formula: monthly booked × 0.01 × $5,295. Depends on assumed booked volume. |
Google Ads is not active. No search or display campaigns to analyze.
Only click-through attribution available. View-through and cross-channel models not possible.
Ad performance cannot be broken down by age, gender, geo, or interest targeting.
Every finding in this review maps to a specific data source and corrects a specific V1 error. This is the ground truth.
| Area | Finding | Impact | Priority |
|---|---|---|---|
| Close Rate | 14.1% → 5.9% (58% collapse) | ~$504K/mo unrealized revenue | P0 |
| ROAS Trend | 3.69x → 0.59x (Apr partial) | Approaching cash-flow negative | P0 |
| TikTok PURCHASE | $21K spend, 0 conversions | +3,500 registrants if reallocated | P1 |
| CRM Revenue | $0 tracked in GHL | Cannot forecast or diagnose | P1 |
| Email Analytics | 94 broadcasts, 0 tracking | Unoptimizable channel | P2 |
| Spend Controls | $55K+ weeks = 1.77x ROAS | $35K cap = 3.60x ROAS | P2 |
| Cash Collection | $2K tier at 42.8% | Below CPA on cash basis | P2 |
What success looks like at Day 90: Close rate recovered to 12%+. Spend capped at $35K/webinar with 3.0x+ ROAS floor enforced. TikTok waste eliminated. Stripe connected to GHL for real-time revenue tracking. Email program running with segmentation and A/B tests. Attribution model connecting ad spend → webinar → close → cash.
Beyond the phase-specific deliverables, here's the baseline you can count on throughout:
Written status report on all active workstreams, wins, and blockers.
Live strategy and performance review with dashboards and next moves.
Full channel-by-channel reporting with KPI tracking against targets.
Direct line to your MH-1 operators in a shared Slack channel.
The V1 misattribution occurred because Meta Ads API was not connected at the time of the initial audit. Without Meta data, the system attributed revenue to organic by default — inverting the entire revenue picture. This V2 was produced by analyzing every raw Excel file, BigQuery table, and transcript in isolation, with no prior intelligence files referenced.