Typography ROAS Campaign Strategy for SaaS B2B PM Tool

# Typography ROAS Campaign Strategy for SaaS B2B PM Tool **Category:** Design → Typography | **Audience:** Professionals or practitioners using AI for typography tasks. | **Delivery:** Primary professional application **Job:** Improve the named business/performance outcome using a measurable, testable intervention. ## SOURCE INPUT Use this source brief as the authoritative task/scenario. Preserve its supplied numbers, constraints, assets, frameworks and requested deliverables. Separate facts from assumptions; never invent missing evidence. > Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims. > > > Act as Senior Typography Strategist for Design (experienced, scaled SaaS B2B PM Tool to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: SaaS B2B PM Tool - $2M ARR, team 50, targeting B2B founders, stage bootstrapped profitable Current: CAC $227, LTV $3588, churn 13%, traffic 52986/mo, conversion 1.2% Assets: Shopify 120 SKUs Goal: achieve 4.8 ROAS in 90 days Constraint: Budget $8k/mo, no dev Tone: bold contrarian Hormozi FRAMEWORK: ICE + structured 4-step. TASK: Complete Typography system for Design / SaaS B2B PM Tool. - common failure modes about typography for SaaS B2B PM Tool - 5 Whys root cause for achieve 4.8 ROAS - Hidden cost of failure - Contrarian insight advanced practitioner insight ## Visual design experiment variants > Variant A — FORM / COMPOSITION: Change the primary composition or visual hierarchy while preserving brand/object identity. > Variant B — VISUAL LANGUAGE: Change the design language, typography/shape system or material treatment while preserving the strategic message. > Variant C — APPLICATION / CONTEXT: Change the real-world application or viewing context while preserving the core concept. > For each variant state exactly what changes, what remains invariant and how the design will be judged. ## METHOD **Workflow:** Communication objective → audience → design system → hierarchy → visual language → application → constraints → QA **Execution** - Diagnose the actual task before prescribing when diagnosis changes the answer. - Make the key strategic/technical/creative decision explicit. - Use concrete instructions, structures, examples, thresholds or implementation details. - Distinguish supplied facts, assumptions and validation needs. - Do not invent capabilities, results, claims or evidence. **Model / delivery** Target model(s): ChatGPT-4o, Claude 3.5, Gemini 1.5. Keep the core solution portable; adapt only relevant model behavior. Delivery context: Primary professional application; respect its real format, audience and constraints. **Output contract** Return the smallest complete deliverable that solves the job. Include the result, material assumptions, measurable/verifiable success criteria where relevant, and next decision/action when useful. **QA** - design objective is explicit - hierarchy is intentional - system is consistent - accessibility/production constraints are respected - application is realistic Also check for contradictions, generic recommendations, unusable outputs and constraint violations. ## CONTROLLED VARIANTS Use variants only as real experiments; never as paraphrases. **A — Form/hierarchy:** change composition and information hierarchy. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **B — Visual language:** change typography/shape/material/color language. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **C — Application/context:** change real-world application while preserving system. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. ## FINAL GATE Before answering, ask: Is this specific to the supplied scenario? Is every important instruction actionable? Are assumptions labeled? Is the output genuinely usable? Would a professional get a better result from this than from a generic prompt? If not, revise the weakest part before delivering.