Total Calls Booked DEV
123
Target: 150
↓ 3.2% vs last period
Cost Per Call Booked DEV
$101
Target: ≤$80
↑ 12% vs last period
Customer Acquisition Cost DEV
$442
Target: ≤$400
↑ 8% vs last period
AI Summary: 261 leads this month converting at 16.9% to 44 sales ($198K revenue). Booking rate is slipping (47.1%, ↓3.2%) — the biggest bottleneck in the funnel. Show rate is strong at 84.6%. Overall ROI of 3.54x is healthy, but attribution gaps mean these numbers may understate organic and overstate paid performance.
Lead → Won Conversion
16.9%
→ Stable vs last period
Booking Rate
47.1%
↓ 3.2% vs last period
Show Rate
84.6%
↑ 5.1% vs last period
Overall ROI
3.54x
↑ 0.3x vs last period
AI Summary: This funnel shows 261 leads converting to 44 sales (16.9%). The biggest drop is Lead→Booked at 53% loss — 138 leads never book a call, representing an estimated $134K/mo in missed revenue. Attribution issues make it hard to know which sources are actually driving quality leads.
REPAIR
Lead→Booked drop-off: 53% (138 leads lost)
Est. revenue impact: $134K/mo. Root causes to investigate: Pre-call video not watched? Unclear CTA on booking page? Speed-to-lead too slow? This is the #1 leverage point in the funnel right now.
Marketing → Sales Funnel
Lead Source Performance (Click to drill down)
AI Summary: 5 active lead sources generating 261 leads/mo. Website leads convert highest at $759 revenue/lead with minimal spend ($2.1K). Meta Ads spend $8.5K for just 28 leads (ROAS 2.6x) — worst performing source. Consider shifting budget toward organic and partner channels where ROAS exceeds 19x.
Pipeline Velocity
AI Summary: Average Lead→Sale velocity ranges from 14 days (Partners) to 28 days (Events). Partners are fastest — strong trust-based referrals shorten the sales cycle significantly. Events leads take longest, likely due to lower intent and longer nurture sequences.
REPAIR
Events avg 28 days Lead→Sale (vs 21 day blended avg)
Events leads take 33% longer than average. If event spend is $4.2K/mo, that capital is locked for nearly a month before any return. Consider pre-qualifying event leads harder or adding an immediate post-event nurture sequence.
ROI Dashboard
AI Summary: Blended CAC of $1,247 with 7.2:1 LTV:CAC ratio — well above the 3:1 healthy threshold. Payback period of 2.1 months is excellent. However, Ads (Meta) pulls the average down with a $1,700 CPA. Stripping out Meta, blended CAC drops to ~$194 — highlighting the drag paid ads have on overall efficiency.
Customer Acquisition Cost (CAC)
$1,247
Total marketing spend ÷ new customers
LTV:CAC Ratio
7.2:1
Target: >3:1 — Healthy ✅
Payback Period
2.1 mo
Months to recoup CAC
ROI by Source
| Source | Spend | Revenue | ROAS | CPL | CPA | LTV:CAC |
|---|
Quality · Quantity · Value Framework
AI Summary: Lead quality is strong (72/100 avg score, B+ grade) with an 84.6% show rate indicating good pre-qualification. Quantity is growing (+9% MoM) with 5 active sources providing diversity. Average deal size of $4,500 with 68% profit margin gives solid unit economics. The main risk is over-reliance on website leads (34% of volume).
AI Summary: Trend data shows consistent month-over-month growth in leads and revenue across all channels. Key concern: booking rate has declined for 6 consecutive months (50.3% → 47.1%) while show rate improved. This suggests the problem is in the booking step, not the call quality.
Funnel Over Time
Lead Volume by Source
Conversion Rate Trend
Revenue by Source
ROAS Trend
Pipeline Velocity Over Time
1. Execution Drivers — What Needs to Happen
📝 Content Production
92% capacity · 3 content gaps · 4 items stuck in approval
Impact: -15% reach risk
▼
AI Summary: 14 posts scheduled this week across 4 platforms, but 3 days have zero content (Wed, Thu, Sat). Creator is at 92% capacity with 4 overdue approvals stuck in pipeline. Instagram and TikTok are underserved — only 2 posts each vs 5 for Facebook. Asset inventory shows 8 ready assets but 6 still needed for next week.
Posts Per Week (Last 12 Weeks)
Creator Capacity % (Last 12 Weeks)
Approval Time (Days) — Last 12 Weeks
REPAIR
Creator at 92% capacity + 3 content gaps this week
Wednesday, Thursday, and Saturday have zero scheduled posts. 4 items stuck in approval pipeline >48h. If gaps aren't filled by Tuesday, expect 15-20% organic reach decline.
Posts This Week
14
Target: 21 (3/day)
Content Gaps
3 days
Wed, Thu, Sat
Assets Ready
8 / 14
6 still needed
Approval Stuck
4 items
>48h in pipeline
Creator Capacity
92%
Max safe: 80%
| Platform ↕ | Scheduled ↕ | Target ↕ | Gap ↕ | Owner |
|---|---|---|---|---|
| 2 | 5 | -3 | Sarah M. | |
| 5 | 5 | 0 | Sarah M. | |
| 3 | 4 | -1 | James R. | |
| 🎵 TikTok | 2 | 4 | -2 | Sarah M. |
| 2 | 3 | -1 | Marketing |
💰 Paid Media Management
5 campaigns · 2 ROAS <2.5x · 3 stale creative (18+ days)
Impact: $2.1K/mo burn on underperformers
▼
AI Summary: 5 active campaigns spending $8,500/mo. 2 campaigns have ROAS below 2.5x threshold — "USCA Cold Traffic" (1.8x) and "Retargeting - Generic" (2.1x). Ad creative on 3 campaigns hasn't been refreshed in 18+ days (target: 14 days). 2 split tests queued but not running due to budget constraints.
Ad Spend Trend ($K/week)
Cost Per Click (CPC) Trend
Creative Fatigue Score (Last 12 Weeks)
REPAIR
3 campaigns with stale creative (18+ days) + 2 with ROAS <2.5x
USCA Cold Traffic ROAS at 1.8x is burning $2.1K/mo for minimal return. Creative fatigue likely contributing — last refresh was 22 days ago. Pause underperformers and refresh creative before restarting.
| Campaign ↕ | Spend/mo ↕ | ROAS ↕ | CTR ↕ | Creative Age ↕ | Status | Owner |
|---|---|---|---|---|---|---|
| AUNZ Lookalike | $2,800 | 4.2x | 2.3% | 8 days | Active | Ads Mgr |
| AUNZ Retargeting | $1,200 | 5.1x | 3.8% | 12 days | Active | Ads Mgr |
| USCA Cold Traffic | $2,100 | 1.8x | 0.9% | 22 days | Underperforming | Ads Mgr |
| Retargeting - Generic | $1,400 | 2.1x | 1.1% | 18 days | Review | Ads Mgr |
| Webinar Promo | $1,000 | 3.4x | 2.7% | 5 days | Active | Ads Mgr |
Split Tests Queued
2
Waiting for budget
Budget Utilization
94%
$8.5K of $9K allocated
💬 Engagement & Nurture
12 SLA breaches · 3.4h avg response · 6.2% bounce rate
Impact: 23% lower booking rate
▼
AI Summary: 12 comments/DMs waiting >2h for response (SLA breach). Average lead response time is 3.4 hours — well above the 2h target. Pre-call video watch rate is only 41% (34 of 83 booked leads didn't watch). Email sequence open rate at 28.4% is healthy, but bounce rate at 6.2% needs attention.
Response Time Trend (Hours)
Conversion Rate by Response Speed
REPAIR
Avg response time 3.4h (target: <2h) + email bounce rate 6.2%
12 engagement items breaching SLA right now. Slow response correlates with 23% lower booking rate. Bounce rate above 5% threshold risks deliverability score degradation — clean list immediately.
Awaiting Response
12
>2h SLA breach
Avg Response Time
3.4h
Target: <2h
Pre-Call Video Watch
41%
34 of 83 didn't watch
Email Open Rate
28.4%
Industry avg: 22%
Bounce Rate
6.2%
Threshold: <5%
| Source ↕ | Avg Response ↕ | SLA Breaches ↕ | Responder | Status |
|---|---|---|---|---|
| Instagram DMs | 4.1h | 5 | Sarah M. | Breaching |
| Facebook Comments | 2.8h | 3 | Sarah M. | At Risk |
| Website Chat | 1.2h | 0 | Sales Team | On Track |
| Email Enquiries | 5.6h | 4 | Marketing | Breaching |
🤝 Sales Handoff
8 leads pending >2h · 72% follow-up · 44% attribution
Impact: -10% booking probability per hour delay
▼
AI Summary: 8 leads pending first contact (>2h). Follow-up compliance at 72% — below the 80% target. Booking confirmation rate is 89% (good). Attribution accuracy only 44% — more than half of leads have unknown or incorrect source tags, making ROI calculations unreliable.
Sales Handoff Rate Trend (%)
SQL Attribution Accuracy (%)
REPAIR
Follow-up compliance 72% (target: 80%) + attribution only 44%
8 leads waiting >2h for first contact. Every hour of delay reduces booking probability by ~10%. Attribution at 44% means more than half your ROI data is guesswork — you can't optimize what you can't measure.
Pending First Contact
8
>2h waiting
Follow-up Compliance
72%
Target: ≥80%
Booking Confirmation
89%
Target: ≥85% ✅
Attribution Accuracy
44%
Target: ≥80%
| Source ↕ | Leads ↕ | Contacted <2h ↕ | Quality Score ↕ | Attributed ↕ |
|---|---|---|---|---|
| Meta Ads | 28 | 64% | 68/100 | 32% |
| Website | 89 | 78% | 74/100 | 51% |
| Social Organic | 78 | 82% | 71/100 | 48% |
| Partners | 41 | 91% | 82/100 | 87% |
| Events | 25 | 76% | 65/100 | 24% |
2. Process & System Drivers
🔗 Integration Health
2 of 5 failing · GHL↔Meta last sync 26h · Email deliverability 87%
Impact: 55% attribution gap from webhook failure
▼
AI Summary: 2 of 5 integrations have issues. GHL↔Meta webhook is failing intermittently (last successful sync 26h ago — breaching 24h threshold). Email deliverability score dropped to 87% from 94% last month. GA4 and Xero integrations are healthy.
Integration Uptime % (Last 12 Weeks)
Data Sync Delays (Hours)
REPAIR
GHL↔Meta webhook failing — last sync 26h ago
Lead form submissions from Meta aren't syncing to GHL pipeline. This directly causes the 55% attribution gap. Every hour this stays broken = leads lost or misattributed.
| Integration | Status | Last Sync | Health | Owner |
|---|---|---|---|---|
| GHL ↔ Meta Webhook | Failing | 26h ago | ❌ Intermittent failures | Ops |
| GHL ↔ Xero Sync | Active | 2h ago | ✅ Healthy | Ops |
| GA4 Tracking Pixel | Active | Real-time | ✅ Healthy | Marketing |
| Email Deliverability | Degraded | 4h ago | ⚠️ Score: 87% (was 94%) | Marketing |
| SMS Gateway | Active | 1h ago | ✅ Healthy | Ops |
🚧 Workflow Bottlenecks
Lead→Booked 2.1x target · 23 stuck >7 days · Content approval 3.1 days
Impact: $103K/mo in stuck pipeline value
▼
AI Summary: Lead→Booked stage takes 4.2 days avg (target: 2 days) — 2.1x over target and the biggest bottleneck. Marketing→Sales handoff averages 6.8 hours. 23 leads stuck >7 days in same stage. Content approval taking 3.1 days vs 1 day target.
REPAIR
Lead→Booked at 2.1x target time + 23 stuck leads
Lead→Booked averaging 4.2 days (target: 2). This single bottleneck is the biggest revenue leak in the funnel. 23 leads have been stuck in the same stage for >7 days — each one represents ~$4.5K potential revenue going cold.
| Stage ↕ | Avg Days ↕ | Target ↕ | Ratio ↕ | Stuck Leads ↕ | Status |
|---|---|---|---|---|---|
| Lead → Booked | 4.2 days | 2.0 days | 2.1x | 12 | Bottleneck |
| Booked → Showed | 1.8 days | 2.0 days | 0.9x | 3 | On Track |
| Showed → Proposal | 3.5 days | 2.0 days | 1.8x | 5 | Slow |
| Proposal → Won | 2.1 days | 3.0 days | 0.7x | 3 | On Track |
| Marketing → Sales Handoff | 6.8 hrs | 4.0 hrs | 1.7x | — | Slow |
| Content Approval | 3.1 days | 1.0 day | 3.1x | — | Bottleneck |
🗃️ Data Quality Issues
62% quality score · 147 attribution gaps (56%) · 34 duplicates
Impact: All ROI data unreliable
▼
AI Summary: Data quality score is 62% — well below the 85% target. 147 leads missing source attribution (56%), 34 duplicate contacts found, 18 missing email addresses, and 12 with invalid phone formats. This is the root cause of unreliable ROI reporting.
REPAIR
Data quality score 62% (target: 85%) — 147 attribution gaps
56% of leads have no source attribution. This makes every marketing ROI calculation unreliable. You literally cannot know which channels are working without fixing this. The Attribution Fixer Agent would resolve ~80% of these automatically.
Data Quality Score
62%
Target: ≥85%
Missing Attribution
147
56% of all leads
Duplicates
34
Need merge
Missing Email
18
7% of contacts
Invalid Phone
12
Wrong format
3. Agent & Human Orchestration
📈 Agent Performance (Active)
2 active · 1 at 94% success · 1 at 78% (needs attention)
Impact: 12 human interventions this week
▼
AI Summary: 2 agents currently active. Email Sequence Agent performing well at 94% success rate. Social Listener Agent at 78% — below 85% threshold, requiring frequent human intervention (12 times this week). Cost savings still positive: $2.1K/mo combined.
REPAIR
Social Listener Agent success rate 78% (target: ≥85%)
12 human interventions this week. Agent struggles with sarcasm detection and multi-thread conversations. Needs retraining or scope narrowing to improve accuracy.
| Agent | Tasks (Pending/Done) | Success Rate | Human Interventions | Cost Saved/mo | Status |
|---|---|---|---|---|---|
| Email Sequence Agent | 3 / 142 | 94% | 2 this week | $1,400 | Healthy |
| Social Listener Agent | 8 / 87 | 78% | 12 this week | $700 | Needs Attention |
🔥 Team Workload Heatmap
2 of 6 >90% capacity · Sarah 92% · Dave 94%
Impact: Task drops imminent · 10 overdue items
▼
AI Summary: 2 of 6 team members are over 90% capacity (red zone). Content Creator at 92% and Sales Rep 1 at 94% are both at risk of burnout or dropped tasks. Marketing Lead and Ops have bandwidth available. Rebalancing 3-4 tasks could bring everyone under 85%.
Agent Recommendation Adoption Rate (%)
Team Workload Distribution (Variance)
REPAIR
2 team members >90% capacity — task drops imminent
Sarah M. (Content Creator, 92%) has 4 overdue items. Dave K. (Sales Rep 1, 94%) is carrying 31 active leads. If either drops tasks, downstream funnel stages will feel the impact within 48h.
Person
Role
Capacity
Tasks
Overdue
Blocked By
Sarah M.
Content Creator
92%
23
4
Content Approval (James)
Dave K.
Sales Rep 1
94%
31
6
Lead Handoff (Marketing)
Lisa T.
Sales Rep 2
78%
22
1
—
Mike P.
Marketing Lead
65%
15
0
—
Rachel W.
Ops Manager
58%
12
0
—
James R.
CEO
85%
18
2
—
🚫 Who's Blocked & Why
4 people blocked · 5 tasks stuck · Longest: 4 days (Sarah)
Impact: 3 content gaps cascade from 4-day approval wait
▼
AI Summary: 4 people currently blocked across 5 tasks. Sarah (Content Creator) blocked for 4 days waiting on content approvals from James. Dave (Sales) blocked on 2 leads waiting for marketing handoff data. Longest block: Sarah's TikTok series at 4 days.
REPAIR
4 people blocked — longest block: 4 days (Sarah, content approval)
Sarah has been waiting 4 days for content approval from James. This cascades into 3 content gaps this week. Approving the queued items today would immediately unblock 4 tasks.
| Person ↕ | Task | Blocked By | Days Blocked ↕ | Unblock Action |
|---|---|---|---|---|
| Sarah M. | TikTok Series (4 posts) | James R. (Approval) | 4 | |
| Sarah M. | Instagram Reels | James R. (Approval) | 3 | |
| Dave K. | Follow up - Meta leads (3) | Marketing (Handoff data) | 2 | |
| Dave K. | Strategy call prep (2 leads) | System (GHL sync) | 1 | |
| Mike P. | Campaign budget approval | James R. (Budget sign-off) | 2 |
4. Attribution & Data Quality — Actionable Fixes
🔧 Attribution & Data Quality — Actionable Fixes
147 gaps · 89 auto-fixable · 58 need manual review
Impact: 44% → 78% accuracy if fixed
▼
AI Summary: 147 leads have missing or incorrect source attribution. Of these, 89 can be auto-fixed using referrer and UTM data (high confidence). 38 need manual review. Top 20 shown below — sorted by stage (closest to revenue first). Fixing these would increase attribution accuracy from 44% to ~78%.
REPAIR
147 leads with attribution issues — only 44% accuracy
Every marketing decision is compromised by unreliable source data. 89 leads can be auto-fixed now. The remaining 58 need manual review or the Attribution Fixer Agent. Est. revenue impact of misattribution: $47K/mo in misallocated budget.
| Lead Name ↕ | Created ↕ | Current Source | Likely Source | Stage ↕ | Action |
|---|---|---|---|---|---|
| Tom Richardson | 12 Mar | Unknown | Meta Ads (UTM match) | Strategy Booked | |
| Jenny Walsh | 14 Mar | Unknown | Website (referrer) | Strategy Booked | |
| Mark Stevens | 15 Mar | Blank | Instagram (form ref) | Strategy Showed | |
| Sarah Chen | 10 Mar | Unknown | Meta Ads (UTM match) | Proposal Sent | |
| David Williams | 18 Mar | Blank | Manual Review Needed | New Lead | |
| Lucy Patel | 19 Mar | Unknown | Facebook (referrer) | New Lead | |
| Brian O'Neill | 11 Mar | Wrong (Manual) | Partner - TradeHub | Strategy Booked | |
| Amanda Torres | 20 Mar | Blank | Website (landing page) | New Lead | |
| Chris Murray | 13 Mar | Unknown | Manual Review Needed | Strategy Showed | |
| Karen Liu | 16 Mar | Unknown | Meta Ads (UTM match) | New Lead | |
| Peter Nguyen | 17 Mar | Blank | LinkedIn (referrer) | Strategy Booked | |
| Fiona Campbell | 21 Mar | Unknown | Website (blog post) | New Lead | |
| James Hart | 9 Mar | Wrong (Website) | Event - Webinar Mar | Proposal Sent | |
| Sophie Adams | 22 Mar | Blank | Instagram (story link) | New Lead | |
| Ryan Cooper | 23 Mar | Unknown | Manual Review Needed | New Lead | |
| Mia Zhang | 8 Mar | Unknown | Meta Ads (pixel match) | Strategy Showed | |
| Oliver Burke | 24 Mar | Blank | Facebook (ad click) | New Lead | |
| Chloe Martin | 25 Mar | Unknown | Website (SEO) | New Lead | |
| Nathan Price | 26 Mar | Blank | Manual Review Needed | New Lead | |
| Emma Collins | 27 Mar | Unknown | TikTok (bio link) | New Lead |
5. Drop-Off Analysis — Revenue Impact
📉 Drop-Off Analysis — Revenue Impact
$267K total leak · $80K realistic recovery · Lead→Booked biggest (53%)
Impact: $40K/mo recoverable from Lead→Booked
▼
AI Summary: Total revenue leaked across all drop-off stages: est. $267K/mo if all were recoverable. Realistic recovery (30% rate): $80K/mo opportunity. Lead→Booked is the biggest leak at 53% drop-off ($134K). Booked→Showed at 15% is actually good but the no-show recovery campaign could still capture $14K/mo.
REPAIR
Lead→Booked drop-off: 53% — est. $40K/mo recoverable revenue
138 leads/mo never book a call. Even recovering 30% would add $40K/mo. Top hypotheses: slow response time (3.4h avg), pre-call video not watched (59% skip it), unclear booking CTA. These are testable — run experiments this week.
| Stage ↕ | Dropped ↕ | Drop Rate ↕ | Revenue Impact ↕ | Root Cause Hypothesis | Action |
|---|---|---|---|---|---|
| Lead → Booked | 138 | 53% | $40.2K/mo | Slow response (3.4h), video not watched (59%), unclear CTA | |
| Booked → Showed | 19 | 15% | $14.1K/mo | No reminder sequence, no pre-call value delivery | |
| Showed → Proposal | 22 | 21% | $9.9K/mo | Call quality variance, unclear next steps after call | |
| Proposal → Won | 38 | 46% | $17.1K/mo | Price objection, competitor comparison, decision delay | |
| Won → Onboarded | 5 | 11% | $2.3K/mo | Onboarding friction, payment setup delays |
6. Campaign Health — All Campaigns
7. Risk Table — All Active Leads
⚠️ Risk Table — All Active Leads
31 active · 8 high-risk (≥70) · 5 no contact 7+ days
Impact: $36K revenue at risk · $15K preventable
▼
AI Summary: 31 active leads in pipeline. 8 are high-risk (score ≥70) representing $36K potential revenue at risk. Top risk: Marcus Webb (score 95) — no contact in 14 days, was at Proposal stage. 5 leads haven't been contacted in 7+ days. Immediate action on top 8 could prevent ~$15K in lost revenue.
REPAIR
8 high-risk leads (score ≥70) — $36K revenue at risk
5 leads haven't been contacted in 7+ days. 3 are at Proposal stage — these are the most expensive to lose since significant time has been invested. Priority: contact Marcus Webb and Jennifer Tran today.
| Risk ↕ | Lead Name ↕ | Source ↕ | Stage ↕ | Days No Contact ↕ | Owner | Action Needed | Risk Factors |
|---|---|---|---|---|---|---|---|
| 95 | Marcus Webb | Meta Ads | Proposal Sent | 14 | Dave K. | Call immediately | No reply to proposal, email unopened |
| 88 | Jennifer Tran | Website | Strategy Showed | 11 | Lisa T. | Follow-up call | Showed but no proposal sent, going cold |
| 85 | Andrew Kim | Unknown | Proposal Sent | 10 | Dave K. | Send reminder | Price objection mentioned, competitor shopping |
| 82 | Rachel Foster | Partner | Strategy Booked | 9 | Dave K. | Confirm booking | Rescheduled twice, may be losing interest |
| 78 | Sam Douglas | Social | New Lead | 8 | Lisa T. | First contact | Never contacted, 8 days old |
| 75 | Diana Price | Website | Strategy Showed | 7 | Dave K. | Send proposal | Good call, but proposal delayed 7 days |
| 72 | Kevin O'Brien | Event | New Lead | 7 | Lisa T. | First contact | Event lead, interest fading |
| 70 | Natalie Cheng | Meta Ads | Strategy Booked | 6 | Dave K. | Confirm + send video | Booked but hasn't watched pre-call video |
| 62 | Tony Russo | Social | Strategy Booked | 4 | Lisa T. | Send reminder | Call tomorrow, no video watched |
| 55 | Michelle Lee | Website | Proposal Sent | 3 | Dave K. | Follow up | Proposal sent 3 days ago, no response |
| 48 | Luke Harrison | Partner | Strategy Showed | 3 | Lisa T. | Send proposal | Good call, needs proposal |
| 42 | Grace Thompson | Website | New Lead | 2 | Dave K. | Book call | High engagement, responded to email |
| 35 | Ben Walker | Social | Strategy Booked | 1 | Lisa T. | Prep for call | Call Thursday, video watched |
| 28 | Olivia Park | Meta Ads | New Lead | 1 | Dave K. | First contact | Fresh lead, responsive |
| 22 | Daniel Moore | Partner | Strategy Booked | 0 | Dave K. | Call today | Confirmed, video watched, ready |