# Create a campaign pack without invented performance Open Practice / Make Mula / curriculum 1.0 Estimated guided practice: 4h estimated. This estimate has not been learner-tested. Original practice material · CC BY 4.0 · Non-degree and non-accredited. ## What you will build Deliver a campaign evidence pack with inputs, tests and a reproducible handoff. ## Before you start - Open entry; inspect the worked fixture before beginning. - A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt. - For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory. ## Learning outcomes - Use a real problem and approved claims, not sensitive-feature targeting. - Link every price, feature and testimonial to permission and evidence. - Reconcile observed denominators and costs before generating commentary. - Separate clicks, sales, profit and causal lift. - Keep a reproducible result and disclose safety, rights and regional access limits. ## Activity 1: Define a truthful offer and audience Estimated time: 35 minutes. ### Outcomes - Use a real problem and approved claims, not sensitive-feature targeting. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. Use a real problem and approved claims, not sensitive-feature targeting. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run. ### Worked example ```text SOURCE (authored fixture): { "impressions": 1000, "clicks": 20, "spend": 10 } INDEPENDENT FIXTURE TARGET: { "ctr": 0.02, "cpc": 0.5, "sales_lift": "not established" } ``` ### Guided activity 1. Use a real problem and approved claims, not sensitive-feature targeting. 2. Open input.json and expected.json from the practice pack; keep an unmodified original. 3. Record a manual prediction and the relevant rule before consulting the answer key. 4. Perform one small change and predict its effect before rerunning. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Define a truthful offer and audience — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - Zero clicks: CPC is undefined, not zero. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## Activity 2: Build a factual creative brief Estimated time: 35 minutes. ### Outcomes - Link every price, feature and testimonial to permission and evidence. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. Link every price, feature and testimonial to permission and evidence. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. ### Worked example ```text MANUAL BASELINE / compare every named field { "ctr": 0.02, "cpc": 0.5, "sales_lift": "not established" } Keep a separate observed-result column; the answer key is not a model response. ``` ### Guided activity 1. Link every price, feature and testimonial to permission and evidence. 2. Open input.json and expected.json from the practice pack; keep an unmodified original. 3. Record expected and observed values separately; explain every mismatch. 4. Perform one small change and predict its effect before rerunning. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Build a factual creative brief — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - More clicks do not establish more profit without sales, costs and attribution evidence. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## Activity 3: Calculate a campaign baseline Estimated time: 35 minutes. ### Outcomes - Reconcile observed denominators and costs before generating commentary. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. Reconcile observed denominators and costs before generating commentary. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. ### Worked example ```text PROJECT CONTRACT: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together. EXPECTED CONSTRUCTED RESULT: { "ctr": 0.02, "cpc": 0.5, "sales_lift": "not established" } ``` ### Guided activity 1. Reconcile observed denominators and costs before generating commentary. 2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original. 3. Record expected and observed values separately; explain every mismatch. 4. Perform An AI-written testimonial is not a customer review. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Calculate a campaign baseline — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - An AI-written testimonial is not a customer review. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## Activity 4: Challenge a flattering narrative Estimated time: 35 minutes. ### Outcomes - Separate clicks, sales, profit and causal lift. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. Separate clicks, sales, profit and causal lift. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. ### Worked example ```text CHANGE TESTS — predict before running: 1. Zero clicks: CPC is undefined, not zero. 2. More clicks do not establish more profit without sales, costs and attribution evidence. 3. An AI-written testimonial is not a customer review. Keep the original expected.json unchanged. ``` ### Guided activity 1. Separate clicks, sales, profit and causal lift. 2. Open failure-tests.md from the practice pack; keep an unmodified original. 3. Record expected and observed values separately; explain every mismatch. 4. Perform Zero clicks: CPC is undefined, not zero. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Challenge a flattering narrative — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - Zero clicks: CPC is undefined, not zero. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## Activity 5: Package approval and disclosure Estimated time: 35 minutes. ### Outcomes - State asset rights, revisions and client approval before publication. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. State asset rights, revisions and client approval before publication. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run. ### Worked example ```text REVIEW BOUNDARY: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit. ``` ### Guided activity 1. State asset rights, revisions and client approval before publication. 2. Open README.md and your evidence log from the practice pack; keep an unmodified original. 3. Record the human decision owner, limitations and any untested service access. 4. Perform one small change and predict its effect before rerunning. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Package approval and disclosure — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - More clicks do not establish more profit without sales, costs and attribution evidence. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## Activity 6: Deliver a campaign evidence pack Estimated time: 65 minutes. ### Outcomes - Provide creative variants, checked metrics and no unsupported success claim. - Explain and retain a testable result and its limits. Advertising work combines creative claims with measurement. A click-through rate is observed clicks divided by impressions; it does not establish sales lift or causality. Use consented assets and fictional performance data until a legitimate real test exists. Provide creative variants, checked metrics and no unsupported success claim. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. ### Worked example ```text HANDOFF MANIFEST: input.json — immutable source expected.json — fixture answer key actual.json — result actually observed evidence-log.csv — baseline plus three changes README.md — reproduction and access route limitations.md — unresolved failures, rights, approvals and scope Never bundle a credential. ``` ### Guided activity 1. Provide creative variants, checked metrics and no unsupported success claim. 2. Open README.md and your evidence log from the practice pack; keep an unmodified original. 3. Record the human decision owner, limitations and any untested service access. 4. Perform one small change and predict its effect before rerunning. 5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live. ### Expected evidence - Deliver a campaign evidence pack — a saved, inspectable artefact tied to the supplied rule. - A baseline or change-test record with expected/observed results and a limitation. ### Common mistakes - An AI-written testimonial is not a customer review. - Treating a supplied answer key, an authored example or self-review as independent field validation. ### Check your understanding Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested? ### Offline route Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run. ## End-of-path review These are self-check questions, not verified examinations. ### Question 1 For the supplied fixture, what is the expected ctr? 1. 0.02 2. It cannot be checked against the supplied input. 3. Whatever value the model reports most confidently. Review answer: 1. The independent answer key only validates this constructed fixture. Preserve the source, rerun counterexamples, and disclose what was not tested. ### Question 2 How should you handle the project rule: Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. 1. Silently change the rule to fit an attractive output. 2. State the rule, test it, and record exceptions or unresolved failures. 3. Treat model confidence as proof. Review answer: 2. Rules, source provenance and counterexamples make an output inspectable; an attractive result does not override policy. ### Question 3 If mainstream providers do not serve your region, what is the honest route? 1. Use a false country or borrowed account. 2. Claim the supplied fixture was a real API run. 3. Check legitimate available alternatives, or label the manual/fixture route and its limits. Review answer: 3. Access includes country, identity, language, cost and data terms. Do not bypass restrictions or invent a live result. ## Capstone review rubric Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment. ### Defined task and preserved input Original input and the project policy are retained; the actual question is clear. ### Checked baseline The supplied baseline reconciles field by field; unsupported live-run claims are absent. ### Three failure/change tests All three specified changes have predictions, observed results and explanations. ### Repeatable handoff Another person can follow the README; supporting practice evidence and unresolved limits are included. ### Safety, rights and access honesty [critical] Calculate CTR and cost per click from the supplied fixture. No protected-characteristic targeting, unauthorised tracking, fabricated testimonials or guaranteed returns. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled. ## References - [ICC advertising and marketing code](https://iccwbo.org/business-solutions/the-icc-advertising-and-marketing-communications-code/) ## Editorial boundary Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated. Use fictional/redacted data and your own authorised provider credentials. Never include credentials in this pack or public work.