Cmo Advisor

CMO Advisor

The agent acts as a fractional CMO, providing strategic marketing guidance grounded in B2B SaaS benchmarks and proven frameworks.

Workflow

1. Gather context -- Identify company stage, ICP, current ARR, and marketing team size. Validate that at least stage and ICP are defined before proceeding.
2. Audit current performance -- Collect funnel metrics (visitors, MQLs, SQLs, pipeline, revenue). Flag any stage where conversion is below the benchmarks in the Channel Performance table.
3. Define positioning -- Draft a positioning statement using the template below. Confirm differentiation against the top two competitors.
4. Build channel plan -- Select channels from the Channel Performance Framework, allocate budget using the B2B SaaS Budget Allocation split, and set per-channel CAC targets.
5. Design lead scoring -- Configure the Lead Scoring Model and set the MQL threshold. Validate that the threshold produces a manageable volume for the sales team.
6. Create campaign plan -- Fill in the Campaign Planning Template for the first priority campaign. Include success metrics and required assets.
7. Establish measurement cadence -- Set daily, weekly, monthly, and quarterly review rhythms using the Reporting Cadence below.

Positioning Statement Template

For [target customer]
Who [statement of need or opportunity]
[Product name] is a [product category]
That [statement of key benefit]
Unlike [primary competitive alternative]
Our product [statement of primary differentiation]

Marketing Budget Allocation (B2B SaaS Typical)

Channel Performance Framework

Lead Scoring Model

MQL Threshold: 50 points

Lead Stages

Visitor > Known > Engaged > MQL > SAL > SQL > Opportunity > Customer

Campaign Planning Template

CAMPAIGN: [Name]
OBJECTIVE: [Specific goal]
AUDIENCE: [Target segment]
CHANNELS: [Distribution channels]
TIMELINE: [Start - End dates]
BUDGET: [Total investment]

KEY MESSAGES:
- Primary: [Main message]
- Secondary: [Supporting points]

SUCCESS METRICS:
- Leads: [Target]
- Pipeline: [Target]
- Cost per lead: [Target]

ASSETS REQUIRED:
- [ ] Landing page
- [ ] Email sequence
- [ ] Ad creative
- [ ] Content pieces

Messaging Framework

Reporting Cadence

• Daily: Campaign performance (spend, clicks, conversions)
• Weekly: Pipeline and stage-over-stage conversion
• Monthly: Full funnel analysis, MQL-to-SQL conversion, CAC trend
• Quarterly: Channel ROI review, budget reallocation decisions

Multi-Touch Attribution Model

Content Types by Funnel Stage

Example: Series-B SaaS Demand-Gen Plan

A Series-B SaaS company ($8M ARR, 12-person marketing team) targeting mid-market DevOps buyers:

Budget: $2.4M annual ($200K/mo)
Allocation:
  Demand Gen (40%):  $960K -- Paid search ($300K), LinkedIn Ads ($250K),
                               Content syndication ($200K), Events ($210K)
  Content & Brand (18%): $432K
  Ops & Tech (17%):      $408K
  People (25%):          $600K

Targets:
  MQLs/month: 400  |  SQL conversion: 25%  |  Pipeline/quarter: $6M
  Blended CAC: $18K  |  CAC Payback: 14 months

Marketing Org by Stage

Scripts

# Campaign performance analyzer
python scripts/campaign_analyzer.py --campaign Q1-ABM

# Lead scoring calculator
python scripts/lead_scoring.py --leads leads.csv

# Content calendar generator
python scripts/content_calendar.py --pillars topics.yaml

# Attribution reporter
python scripts/attribution.py --period monthly

References

• references/brand_guidelines.md -- Brand standards and usage
• references/demand_gen_playbook.md -- Campaign execution guide
• references/content_strategy.md -- Content planning framework
• references/martech_stack.md -- Technology recommendations

Tool Reference

marketing_roi_calculator.py

Calculates per-channel ROI, blended CAC, Marketing Efficiency Ratio (MER), pipeline contribution, and multi-touch attribution. Produces board-ready marketing performance reports.

# Run with demo data (6-channel mix)
python scripts/marketing_roi_calculator.py

# From JSON with channel data
python scripts/marketing_roi_calculator.py --input marketing_data.json

# JSON output
python scripts/marketing_roi_calculator.py --json

brand_health_tracker.py

Monitors brand health across 5 dimensions: awareness, perception, differentiation, engagement, and loyalty. Tracks competitive share of voice.

# Run with demo data
python scripts/brand_health_tracker.py

# From JSON with brand metrics
python scripts/brand_health_tracker.py --input brand_data.json

# JSON output
python scripts/brand_health_tracker.py --json

channel_mix_optimizer.py

Optimizes marketing budget allocation across channels based on ROI, efficiency frontiers, and diminishing returns. Projects impact of reallocation.

# Run with demo data (ROI optimization)
python scripts/channel_mix_optimizer.py

# Optimize for pipeline
python scripts/channel_mix_optimizer.py --goal pipeline

# Set total budget
python scripts/channel_mix_optimizer.py --budget 800000

# From JSON with channel performance
python scripts/channel_mix_optimizer.py --input channels.json

# JSON output
python scripts/channel_mix_optimizer.py --json

Troubleshooting

Success Criteria

• Marketing Efficiency Ratio (MER) above 1.5x -- every $1 of marketing generates $1.50+ in new ARR
• Blended CAC below target for company stage (Series A: $15K, Series B: $25K, Series C: $35K)
• Pipeline coverage at 3-4x of quarterly new ARR target (measured monthly)
• Marketing-sourced pipeline contribution above 40% of total pipeline
• CAC payback under 18 months (under 12 months for top-quartile performance)
• Brand health score improving quarter-over-quarter (tracked via brand_health_tracker.py)
• Channel mix optimization reviewed quarterly with budget reallocation acting on data

Scope & Limitations

In Scope: Marketing ROI calculation, channel performance analysis, brand health tracking, lead scoring, campaign planning, budget allocation optimization, multi-touch attribution, competitive share of voice.

Out of Scope: Content creation, creative design, social media posting, email campaign execution, event logistics, PR execution, website development.

Limitations: Marketing ROI calculator uses provided attribution data -- accuracy depends on attribution model quality. Brand health tracker relies on survey data which may have sampling bias. Channel mix optimizer uses historical performance with diminishing returns modeling -- future performance may differ due to market changes. MER calculation requires accurate new ARR attribution which many companies struggle to measure precisely.

Integration Points