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)

| Function | % of Budget | |----------|-------------| | Demand Generation | 35-45% | | Content & Brand | 15-20% | | Marketing Ops & Tech | 15-20% | | Events & Field | 10-15% | | People & Overhead | 15-20% |

Channel Performance Framework

| Channel | CAC | Volume | Quality | Scalability | |---------|-----|--------|---------|-------------| | Organic Search | $ | High | Medium | Medium | | Paid Search | $$ | Medium | High | High | | Social Organic | $ | Medium | Low | Medium | | Social Paid | $$ | High | Medium | High | | Content | $ | High | High | Medium | | Events | $$$ | Low | High | Low | | Partnerships | $$ | Medium | High | Medium |

Lead Scoring Model

| Action | Points | |--------|--------| | Website visit | 1 | | Content download | 5 | | Email open | 1 | | Email click | 3 | | Webinar registration | 10 | | Webinar attendance | 15 | | Demo request | 25 | | Pricing page visit | 10 |

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

| Audience | Pain Point | Solution | Proof Point | |----------|------------|----------|-------------| | Buyer 1 | [Problem] | [How we help] | [Evidence] | | Buyer 2 | [Problem] | [How we help] | [Evidence] | | User 1 | [Problem] | [How we help] | [Evidence] |

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

| Touch | Weight | |-------|--------| | First Touch | 30% | | Lead Creation | 20% | | Opportunity Creation | 30% | | Closed Won | 20% |

Content Types by Funnel Stage

| Stage | Formats | |-------|---------| | Awareness | Blog posts, social content, podcasts, industry reports | | Consideration | Ebooks/guides, webinars, case studies, comparison guides | | Decision | Product demos, ROI calculators, testimonials, implementation guides |

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

| Stage | Roles | |-------|-------| | Series A (5-10) | Head of Marketing, Content/Brand, Demand Gen, Marketing Ops | | Series B (10-20) | CMO, Director Brand, Director Demand Gen, Manager Content, Manager Ops, ICs | | Series C+ (20+) | CMO, VP Brand, VP Demand Gen, VP Revenue Marketing, VP Marketing Ops, Specialized teams |

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

| Problem | Likely Cause | Fix | |---------|-------------|-----| | Blended CAC increasing quarter over quarter | Channel saturation or scaling into less efficient channels | Run channel_mix_optimizer.py; cut lowest-ROI channels; increase investment in highest-ROI | | Marketing sourced pipeline below 40% of total | Over-reliance on outbound/sales-sourced; marketing underinvesting in demand gen | Shift budget: target 40-60% marketing-sourced pipeline; invest in content + paid channels | | Brand awareness below 30% in target market | Insufficient top-of-funnel investment; brand treated as afterthought | Allocate 15-20% of budget to brand; measure aided awareness quarterly | | MQL-to-SQL conversion below 20% | Lead scoring threshold too low or ICP mismatch | Recalibrate MQL threshold; audit scoring model; tighten ICP definition