marketing-ideas

When a user asks for marketing ideas:

1. Establish context first (ask if missing)

* Product type & ICP

* Stage (pre-launch / early / growth / scale)

* Budget & team constraints

* Primary goal (traffic, leads, revenue, retention)

1. Shortlist candidates

* Identify 6–10 potentially relevant ideas

* Eliminate ideas that clearly mismatch constraints

1. Score feasibility

* Apply the Marketing Feasibility Score (MFS) to each candidate

* Recommend only the top 3–5 ideas

1. Operationalize

* Provide first steps

* Define success metrics

* Call out execution risk

> ❌ Do not dump long lists

> ✅ Act as a decision filter

---

Every recommended idea must be scored.

MFS Overview

Each idea is scored across five dimensions, each from 1–5.

| Dimension | Question |

| ------------------- | ------------------------------------------------- |

| Impact | If this works, how meaningful is the upside? |

| Effort | How much execution time/complexity is required? |

| Cost | How much cash is required to test meaningfully? |

| Speed to Signal | How quickly will we know if it’s working? |

| Fit | How well does this match product, ICP, and stage? |

---

Scoring Rules

• Impact → Higher is better
• Fit → Higher is better
• Effort / Cost → Lower is better (inverted)
• Speed → Faster feedback scores higher

---

Scoring Formula

```

Marketing Feasibility Score (MFS)

= (Impact + Fit + Speed) − (Effort + Cost)

```

Score Range: -7 → +13

---

Interpretation

| MFS Score | Meaning | Action |

| --------- | ----------------------- | ---------------- |

| 10–13 | Extremely high leverage | Do now |

| 7–9 | Strong opportunity | Prioritize |

| 4–6 | Viable but situational | Test selectively |

| 1–3 | Marginal | Defer |

| ≤ 0 | Poor fit | Do not recommend |

---

Example Scoring

Idea: Programmatic SEO (Early-stage SaaS)

| Factor | Score |

| ------ | ----- |

| Impact | 5 |

| Fit | 4 |

| Speed | 2 |

| Effort | 4 |

| Cost | 3 |

```

MFS = (5 + 4 + 2) − (4 + 3) = 4

```

➡️ Viable, but not a short-term win

---

When recommending ideas:

• Always present MFS score
• Never recommend ideas with MFS ≤ 0
• Never recommend more than 5 ideas
• Prefer high-signal, low-effort tests first

---

> Each idea is a pattern, not a tactic.

> Feasibility depends on context — that’s why scoring exists.

(Library unchanged; same ideas as previous revision, omitted here for brevity but assumed intact in file.)

---

When recommending ideas, always use this format:

---

Idea: Programmatic SEO

MFS: +6 (Viable – prioritize after quick wins)

• Why it fits

Large keyword surface, repeatable structure, long-term traffic compounding

• How to start

1. Identify one scalable keyword pattern

2. Build 5–10 template pages manually

3. Validate impressions before scaling

• Expected outcome

Consistent non-brand traffic within 3–6 months

• Resources required

SEO expertise, content templates, engineering support

• Primary risk

Slow feedback loop and upfront content investment

---

Use these biases when scoring:

Pre-Launch

• Speed > Impact
• Fit > Scale
• Favor: waitlists, early access, content, communities

Early Stage

• Speed + Cost sensitivity
• Favor: SEO, founder-led distribution, comparisons

Growth

• Impact > Speed
• Favor: paid acquisition, partnerships, PLG loops

Scale

• Impact + Defensibility
• Favor: brand, international, acquisitions

---

• ❌ No idea dumping

• ❌ No unscored recommendations

• ❌ No novelty for novelty’s sake

• ✅ Bias toward learning velocity

• ✅ Prefer compounding channels

• ✅ Optimize for decision clarity, not creativity

---

• analytics-tracking – Validate ideas with real data
• page-cro – Convert acquired traffic
• pricing-strategy – Monetize demand
• programmatic-seo – Scale SEO ideas
• ab-test-setup – Test ideas rigorously