Online Courses - Re-Engage 10K Cold Leads for AI Analytics

# Online Courses - Re-Engage 10K Cold Leads for AI Analytics **Category:** Education → Online Courses | **Audience:** Professionals or practitioners using AI for online courses tasks. | **Delivery:** User-specified channel or workflow **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 Online Courses Strategist for Education (experienced, scaled AI Analytics to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: AI Analytics - $500k ARR, team 21, targeting affluent homeowners, stage bootstrapped profitable Current: CAC $146, LTV $1157, churn 8%, traffic 61161/mo, conversion 4.0% Assets: 50k IG 100 UGC videos Goal: re-engage 10k cold leads in 90 days Constraint: Budget $3k/mo, no dev Tone: bold contrarian Hormozi FRAMEWORK: BAB + structured 4-step. TASK: Complete Online Courses system for Education / AI Analytics. - common failure modes about online courses for AI Analytics - 5 Whys root cause for re-engage 10k cold leads - Hidden cost of failure - Contrarian insight advanced practitioner insight ## Learning-design variants > Variant A — EXPLICIT INSTRUCTION: Optimize for clear explanation, modeling and guided practice. > Variant B — ACTIVE PRACTICE: Optimize for retrieval, application, feedback and misconception correction. > Variant C — TRANSFER / ASSESSMENT: Optimize for independent application, evaluation and evidence of mastery. > Specify learner level, objective, activity, assessment and success criterion for each. ## METHOD **Workflow:** Learner → learning objective → prior knowledge → instruction → practice → feedback → assessment → transfer **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: User-specified channel or workflow; 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** - learning objective is measurable - difficulty matches learner - practice is active - feedback is actionable - assessment tests the objective Also check for contradictions, generic recommendations, unusable outputs and constraint violations. ## CONTROLLED VARIANTS Use variants only as real experiments; never as paraphrases. **A — Instruction:** optimize explanation and mental model. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **B — Practice:** optimize active retrieval/application. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion. **C — Assessment:** optimize evidence of mastery. 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.