Lead Genius is the revenue operations layer for hospitality technology companies — automating inbound classification, enrichment, scoring, outbound CRM campaigns, and prospect discovery in a single connected workflow.
Problem Statement
Marketing and sales teams at hospitality SaaS companies spend 60–70% of their ops time on manual lead triage, copy-paste between tools, and one-size-fits-all outreach. Leads go cold because enrichment is slow, sequences aren't contextual, and there's no feedback loop between what converts and what gets sent.
Success is measured across three horizons: operational efficiency, lead quality, and revenue impact.
Goal
Metric
Baseline
Target (6mo)
Reduce manual triage time
Hours/week on lead classification
~12 hrs
≤ 2 hrs
Improve lead-to-opportunity rate
% of hot leads that become opportunities
~8%
≥ 15%
Increase sequence open rates
Avg open rate across all sequences
~22%
≥ 35%
Faster lead response time
Time from capture to first email sent
~4 hrs
≤ 15 min
Prospect pipeline volume
Net new qualified prospects/month
~80
≥ 300
Scoring model accuracy
% of High-score leads that reach opp stage
n/a
≥ 40%
Rep alert action rate
% of escalation alerts acted on within 4hrs
n/a
≥ 70%
Non-Goals
Lead Genius is not a CRM. It reads from and writes to HubSpot but does not replace it. It is not a writing tool — sequences use templatised blocks with dynamic fields, not fully AI-generated copy per send. It does not make autonomous decisions about closing deals — all final sales actions require human involvement.
User Personas
👤 Marketing Ops Manager — Primary User
Goal: Set up and maintain workflows, monitor pipeline health, review AI suggestions. Pain: Spends hours in HubSpot manually tagging leads and assigning sequences. Needs: Clear visibility into what's automated, easy override controls, reliable reporting.
👤 SDR / Sales Development Rep — Secondary User
Goal: Receive warm handoffs at the right moment, understand why a lead is scored high. Pain: Gets alerts too late, or for leads with no context — wastes time on low-fit prospects. Needs: Lead context cards, clear score explanations, quick-reply prompts.
👤 Growth Lead / Head of Marketing — Stakeholder
Goal: Understand pipeline attribution, ROI of sequences, quality of prospect lists. Pain: Can't connect outreach activity to revenue — data lives in three different tools. Needs: Revenue attribution, sequence health scores, monthly trend views.
👤 Sales Manager — Stakeholder
Goal: Trust that automated outreach isn't damaging brand reputation or spamming key accounts. Pain: No visibility into what's being sent on behalf of the team. Needs: Sequence approval workflow, suppression lists, compliance confirmation.
Inbound Pipeline Requirements
Lead Capture
ID
Requirement
Priority
IB-01
System receives lead data from HubSpot via webhook within 60 seconds of form submission
P1
IB-02
Deduplication check runs on email (exact) and domain (fuzzy) before classification
P1
IB-03
Re-entry logic: returning leads flagged as "re-engaged" and routed to win-back flow
P1
IB-04
Lead classified as Hot, Warm, or Cold within 2 minutes of capture
P1
IB-05
Classification logic: Hot = business email + domain resolves; Warm = Gmail + form context present; Cold = Gmail + no context
P1
IB-06
Source page recorded and stored for all leads regardless of classification
P1
IB-07
Failed webhook deliveries retry up to 3 times with exponential backoff
P2
Enrichment
ID
Requirement
Priority
EN-01
Hot leads trigger enrichment pipeline: Clearbit domain lookup → Apollo company profile → contact-level data pull
P1
EN-02
Enrichment completes within 5 minutes for 95% of Hot leads
Match score distribution · Manual review queue size · Prospect-to-sequence conversion · ICP dimension breakdown
Revenue Attribution
Closed deals by originating source · Sequence that preceded first reply · Score band at time of conversion · Time from capture to close
AI Suggestions Requirements
ID
Requirement
Priority
AI-01
Suggestions ranked by potential revenue impact, not recency
P1
AI-02
Each suggestion includes confidence level and the data signal that triggered it
P1
AI-03
Suggestion marked as "acted on" or "dismissed" — feedback used to improve future suggestion quality
P2
AI-04
Scoring recalibration suggestions surface when a parameter shows weak conversion correlation over 90 days
P2
AI-05
Sequence retirement suggestions fire automatically when performance thresholds are breached
P2
User Stories
US-001Automatic lead classification on captureMust Have▼
As a marketing ops manager, I want every lead from HubSpot to be automatically classified as Hot, Warm, or Cold so that I don't need to manually review each submission.
✓Lead classified within 2 minutes of HubSpot webhook receipt
✓Business email = Hot if domain resolves to a company
✓Gmail + form context = Warm; Gmail + no context = Cold
✓Classification reason stored and visible in dashboard
✓Duplicate leads detected and merged before classification
US-002Lead score explanation per contactMust Have▼
As an SDR, I want to see why a lead received their score so I can prioritise my outreach intelligently and personalise my approach.
✓Score breakdown visible per lead showing contribution of each dimension
✓Score history shows all recalculations with timestamps and reasons
✓Natural language explanation generated ("High score because: Director-level at 200+ property chain in MENA")
✓Missing data fields that could improve score are surfaced
US-003Immediate rep alert on email replyMust Have▼
As an SDR, I want to be notified immediately when a prospect replies to a sequence so I can follow up while interest is high.
✓Reply event from Klenty/Outplay triggers Slack message within 2 minutes
✓Slack alert includes: contact name, company, score, which email they replied to, reply preview
✓Sequence automatically paused on reply
✓CRM task created in HubSpot and assigned to rep
✓If no rep action within 4hrs, escalation alert fires to manager
US-004Automatic CRM campaign trigger on deal stage changeMust Have▼
As a marketing ops manager, I want deal stage changes in HubSpot to automatically trigger the correct outbound campaign so that no segment goes un-nurtured.
✓HubSpot deal stage change triggers evaluation within 60 minutes
✓Correct campaign assigned based on deal stage mapping table
✓Suppression list checked before any sequence fires
✓Rep can override or pause campaign from dashboard within 30 minutes of trigger
As a growth lead, I want the system to automatically research and enrich new prospects that match our ICP above 30% so that we always have a qualified pipeline without manual prospecting.
✓Prospects from Apollo/Seamless scored against ICP before research begins
✓Match ≥30%: contact research auto-runs (C-suite/Director filter applied)
✓Match <30%: routed to manual review queue with mismatch reasons
✓Researched contacts pushed to Klenty/Outplay with match score attached
As a marketing ops manager, I want the AI panel to tell me when my scoring weights are producing poor conversion predictions so I can recalibrate without waiting for quarterly review.
✓System monitors correlation between score dimensions and deal conversion weekly
✓Suggestion fires when a dimension shows <15% predictive accuracy over 90 days
✓Suggestion includes recommended new weight and supporting data
✓Admin can accept suggestion (applies weight change) or dismiss
Mitigation: 2-week scoring workshop with sales team before Phase 1 begins. Use historical deal data to validate initial weights.
HIGH
Enrichment API rate limits slow hot lead pipeline
Mitigation: Implement async enrichment queue. Cache domain-level data (company info reused for multiple contacts from same domain).
HIGH
HubSpot webhook reliability — missed lead events
Mitigation: Implement hourly HubSpot API poll as backup to webhooks. Dead-letter queue for failed deliveries.
MED
Klenty/Outplay API changes break sequence triggers
Mitigation: Abstract sequence delivery behind internal adapter layer. Switching tools requires only adapter change, not system rebuild.
MED
Low enrichment match rate for MENA region companies
Mitigation: Supplement Clearbit with regional data providers. Manual enrichment fallback for high-score leads with gaps.
MED
Rep adoption — alerts ignored, handoff system underused
Mitigation: Track rep alert action rate from Day 1. Monthly review. Escalation chain to manager if action rate below 60%.
LOW
A/B test results invalidated by small sample sizes
Mitigation: Only surface A/B results at n≥100 per variant. Flag inconclusive tests clearly in dashboard.
Compliance Requirements
Automated email sequences to enriched contacts carry legal obligations across GDPR, CAN-SPAM, and CASL. These are non-negotiable and must be implemented in Phase 1.
ID
Requirement
Regulation
Priority
CO-01
Every contact in an automated sequence has a logged consent basis before first send
GDPR / CASL
P1
CO-02
Unsubscribe processed within 24 hours and synced to HubSpot, Klenty, Outplay
CAN-SPAM / GDPR
P1
CO-03
EU-flagged contacts receive GDPR-compliant footer and opt-out in every email
GDPR
P1
CO-04
Enrichment data purged after 18 months of no engagement
GDPR Art. 5
P2
CO-05
Data processing agreement in place with Clearbit, Apollo, and Seamless before go-live
GDPR Art. 28
P1
CO-06
Audit log of all automated emails sent, with sender, recipient, timestamp, and sequence ID