Practical AI resources for understanding customers, planning campaigns, creating content and making better-informed marketing decisions.
For marketers, growth teams, agencies and marketing operations professionals. Choose the job you need to do and start with the resources that fit it.
Move from ideas to usable working material with connected Workflows, editable marketing Templates, realistic Practice Cases and primary-source real cases that help you examine what worked, what failed and what the evidence actually supports.
Open resource shows its normal Amo.ng page. Run in Workplace opens the private workspace using your own supported AI provider. Download saves an editable Markdown file. Read case study opens an account of an actual authority’s decision or settlement.
Understand your customers and market
When a positioning decision needs customer evidence, or existing feedback is hard to interpret.
Bring: A business question, known evidence and gaps, recruitment access, approved contact/data rules and a responsible researcher.
Workflow · AMO-W-000045
Develop Customer Interview Evidence for a Positioning Decision
For a positioning question that customer interviews can responsibly inform.
Produce: A reviewed research question, interview protocol, evidence interpretation and bounded positioning options. Actual interviews must happen outside the AI steps.
When you already have authorised reviews, transcripts or sales notes. Traceable objections and proof gaps; findings describe the supplied corpus, not the whole market.
Your review: A researcher chooses a suitable method and sample. Interviews, consent, fieldwork, human analysis and validation remain manual or external.
Short example: understand your customers and market
Fictional illustration. Eight existing customers volunteer; six mention onboarding confusion. That is six of these eight interviews, not 75% of the market. Preserve contrary views and absent groups before choosing a message.
Define audiences responsibly
Before proposing an audience upload, promotional contact or new use of customer data.
Bring: Audience criteria, customer evidence, intended purpose/destination, permission and restriction records, timestamps and suppression evidence.
Prompt · AMO-P-000364
Review Marketing Audience Activation and Permission Evidence
When a proposed audience use needs an evidence and permission check.
Produce: An audience-use evidence map, conflicts, suppression questions and a handoff for a human decision.
Your review: The appropriate data/privacy and channel owners decide what is authorised. Possession, a CRM flag or hashing alone does not establish permission.
Improve positioning and messaging
When choosing whom an offer serves, what to promise and what evidence supports that promise.
Bring: Offer facts, customer evidence, alternatives, proof assets, brand constraints and the decision to be made.
Prompt · AMO-P-000021
Offer Positioning Workshop Prompt
When you need to compare positioning options against customer needs and proof.
Produce: A buyer/problem/promise decision frame, differentiation, objections and a validation plan.
When you want the claims method as reusable Skill instructions instead of its source Prompt. A reusable claims-review method; this is an alternative format, not additional proof.
Your review: People approve the positioning and inspect claims in their actual placement. Regulated or uncertain claims need an appropriate specialist.
Short example: improve positioning and messaging
Fictional illustration. A battery-duration brief names a test mode, but the mobile ad crop omits it. Inspect the full placement and test evidence; approval of the brief does not approve every crop.
Plan a campaign
When customer evidence and offer facts are available and you need to connect them to a campaign and measurement plan.
When you need a bounded campaign-planning task rather than the full Workflow. A campaign plan linking evidence, messages, channels, calendar and measurement.
Before traffic goes live or a campaign handoff is accepted. A readiness review of tracking, audience rules, consent, routing and unresolved launch risks.
Your review: Campaign owners review the plan, operational checks and experiment proposal before any launch. A completed Workflow is not launch authority.
Create and improve content
When turning a supported strategy into messages for audiences, channels and stages of the customer journey.
Bring: Campaign objective, offer, supported audience insights, channels, proof, voice and production limits.
Prompt · AMO-P-000022
Campaign Messaging Matrix Prompt
When messages need to stay consistent while adapting to different channels.
Produce: A prioritised messaging matrix, draft variants, proof requirements and a validation plan.
For the search-led branch of a wider content strategy. A topical content map, publishing priorities and internal-link plan; it does not cover every distribution channel.
Your review: Humans substantiate claims, review accessibility and context, and approve each final placement. Drafting never means publication.
Improve search visibility
When deciding which existing pages to refresh and what evidence should guide the edits.
Bring: Current pages, dated search/crawl evidence, audience intent and accessible supporting sources.
Workflow · AMO-W-000006
Evidence-Backed SEO and AI Search Content Refresh
When existing content needs an evidence-backed refresh.
Produce: Prioritised pages, current research, content briefs and an internal-link plan.
When assessing a site for AI search and citation-based discovery. An evidence-based visibility review and improvement priorities, with unavailable checks marked.
Your review: SEO and technical owners verify current behaviour and approve edits. Eligibility for indexing or AI answers is not a ranking guarantee.
Short example: improve search visibility
Fictional illustration. A crawl permits indexing, while a dated search-console extract identifies another canonical. Reconcile the dates and signals before diagnosing the current state; an eligible page is not necessarily indexed.
Review paid-media decisions
Before accepting a media plan or moving advertising budget on the strength of reported performance.
Bring: Proposed decision, spending authority, comparable exports, business outcomes, margin/capacity information and measurement changes.
Prompt · AMO-P-000365
Challenge a Paid-Media Plan and Performance-Based Budget Recommendation
When a plan or budget change needs to withstand a professional challenge.
Produce: A comparable-evidence review, alternative explanations, bounded options and a human budget-decision handoff.
When creative fatigue is a specific hypothesis supported by exposure and asset evidence. A fatigue diagnosis and controlled refresh ideas, with confounders kept visible.
Your review: An authorised budget holder makes the spending decision. Analysts resolve measurement and causal questions; AI cannot change bids, budgets or campaigns.
Short example: review paid-media decisions
Fictional illustration. CTR falls from 2% to 1%, but qualified outcomes per CU1,000 remain 10 and the attribution window changes. Lower engagement alone does not prove fatigue or justify a budget cut.
Improve conversion
When a website journey may be preventing the intended customer action.
Bring: Authorised screenshots or recordings, target audience, conversion goal, constraints and observed behaviour.
Workflow · AMO-W-000003
Website UX Audit to Experiment Plan
When you need to turn website evidence into testable improvement hypotheses.
Produce: A prioritised UX review and measurable experiment plan, rather than an automatically changed website.
When delivery is impaired and an incident needs investigation. A bounded recovery playbook covering sender, audience, suppression and staged controls.
Your review: Channel/privacy owners approve sending rules and content. Server acceptance, an open or a CRM record is not proof of permission or business impact.
Manage leads and marketing operations
When assignment, response times or CRM handoffs do not match the intended process.
Bring: Routing rules, owner/SLA definitions, approved record extracts, event times and outcome observations.
Prompt · AMO-P-000220
RevOps Lead Routing and SLA Integrity Audit
When you need to check lead routing and service-level evidence.
Produce: A routing/SLA review, exceptions and prioritised checks for accountable owners.
Only for technical teams implementing an approved design with sandbox/staging access, n8n and CRM expertise. A staged implementation and verification journey. Codex/manual/external actions remain explicit; it does not authorise production deployment.
Your review: Operations owners reconcile clocks, identity and restrictions. Routing a record is separate from permission to contact it.
Short example: manage leads and marketing operations
Fictional illustration. A 10:00 withdrawal conflicts with a 09:00 marketing flag. A 10:05 sync acceptance log does not prove destination suppression. Keep the affected promotional use on hold for authorised review.
Measure marketing performance
When conversion counts disagree, attribution shifts, or nobody agrees what an event means.
Bring: Metric definitions, reporting windows, model/settings, dated exports, consent/tracking changes and actual QA observations.
Prompt · AMO-P-000227
GA4 Attribution Anomaly Investigation Brief
When GA4 attribution or conversion reports unexpectedly disagree.
Produce: An investigation brief separating reporting changes from business hypotheses, with verification checks.
Your review: An analyst distinguishes recorded events, attributed credit and business outcomes. A proposed check is not a passed check or proof of causality.
Short example: measure marketing performance
Fictional illustration. One test order emits two purchase events. That supports a duplicate-emission concern; without destination evidence it does not establish two counted conversions or the production error rate.
Run experiments and learn
When proposing a growth test or deciding what an actual study supports.
Bring: Business question, hypothesis, design/assignment plan, defined metrics, guardrails, implementation evidence and results if available.
Prompt · AMO-P-000192
GTM Experiment Design and Measurement Brief
Before running a go-to-market experiment.
Produce: A hypothesis, audience/channel plan, metrics, guardrails and prespecified decision/readout rules.
When observed allocation differs from the expected assignment ratio. An investigation across assignment, exposure and analysis before trusting results.
Your review: Qualified analysts assess design, uncertainty and deviations; business owners decide the next action. Correlation or favourable rates alone do not establish causality.
Short example: run experiments and learn
Fictional illustration. A 50:50 plan has 600 and 400 analysis units, and the decision threshold changed after results were seen. Investigate assignment, exclusions and the post hoc change; a favourable rate does not resolve validity.
Templates
These are editable Markdown working materials. Preview the structure or download the source for your own process. A completed record is not proof of compliance, permission or a valid causal claim.
Template · Markdown
Campaign Evidence and Human Decision Record
Connect campaign evidence, claims, permission and measurement to AI observations, unresolved questions and an actual human decision.
Preview template: Campaign Evidence and Human Decision Record
Campaign Evidence and Human Decision Record
Editable Markdown working material.
Use this record to connect a campaign version, the evidence examined and the decision actually made. Reference detailed resource outputs instead of copying customer data into every section. Replace the italic instructions; duplicate rows as needed. Unknown, not supplied, not performed and not applicable are different states. An empty decision field means no recorded approval.
1. Decision and version
Field
Working entry
Campaign / version / record date
Complete
Objective and decision requested
State the business outcome and exact proposed action
Audience, purpose, channel and market
Separate each materially different use
Accountable decision owner and authority reference
Name only an actual owner; otherwise Unassigned
Budget, capacity, timing and scope limits
Reference approved limits; do not infer approval
Creative, landing, offer and measurement versions
Record the versions this decision applies to
Permitted data/tool use
Reference actual organisation rules, including AI-provider disclosure
Record what was actually inspected. A link alone does not prove it was accessible; a screenshot may omit context. Preserve conflicting versions. A generated review is working material, not independent proof of the underlying fact.
3. Evidence-to-decision map
Decision item
Proposed position / action
Evidence IDs and contrary evidence
Gap / consequence
Required human review
Customer need and audience criteria
Rationale
Source references and sample limits
What cannot be concluded
Research / audience owner
Claim and whole communication
Exact wording and placement version
Proof, qualification and context references
Unsupported or omitted context
Brand/product and specialist where required
Purpose, permission and recipient
Specific use and downstream destination
Notice, authorised assessment/consent where applicable, restrictions
Do not infer from possession
Privacy/data owner
Exclusion and suppression
Proposed rule and propagation path
Source/destination observations and timestamps
Unobserved sync / conflicts
Channel/operations owner
Channel and spend rationale
Role and proposed allocation
Comparable outcomes, assumptions, capacity
No invented marginal return
Media/finance/budget owner
Measurement
Metric, denominator, horizon and source
Definition and QA references
Attribution/causal limits
Analyst
Roles above are review prompts, not assertions that the organisation has these titles or that each is legally required. Use its actual responsibility arrangement. Reference the separate measurement record for detailed definitions/tests.
4. Assumptions, unresolved questions and AI observations
Item ID
Assumption / question / AI observation
Source or reason it is uncertain
Decision affected
Check / owner / due trigger
Q1
Keep AI interpretation distinct from source evidence
Evidence IDs or Missing
State consequence
No invented deadline or approval
Record AI resource, input/evidence version and output date/reference if used. Record the human correction or rejection of an AI observation; do not treat the existence of a Run Record as validation. Use an organisation-approved location and its actual access/retention rules.
5. Procedure and review evidence
Check ID
Expected behaviour / proposed procedure
Actual supplied result / Not performed
Evidence and scope
Review result / unresolved action
QA1
Example: withdrawal excluded from destination audience
Record observation only after it exists
Date, version, synthetic/live scope
Supported / Conflicting / Incomplete / Not performed
An action marked completed still needs outcome verification. A test using synthetic data does not prove all production records are correct. Missing evidence cannot be marked passed.
6. Human decision — complete only from an actual decision
Field
Working entry
Disposition
Pending / Hold / Rejected / Approved for precisely stated action
Human owner / decision date / authority
Leave Pending until supplied
Exact action and version covered
Approval of a draft is not permission to launch
Rationale and evidence considered
Include unresolved risks and contrary evidence
Conditions, exclusions and required further approvals
State boundaries
Expiry or review trigger
Only if owner supplies/agrees it; no invented period
Operational executor and execution evidence
Separate later action; Not performed initially
Verification of outcome
Actual evidence or Pending
7. Follow-up and change history
Date/version
Change or unresolved item
Effect on earlier decision
Owner / follow-up
Actual closure evidence or Open
Complete
Include changed claims, audience purpose or metric definition
Re-review needed?
Agreed action
Completion alone is not verified closure
Before relying on this record, confirm that the decision references the same campaign/evidence versions, unsupported claims and permissions remain visible, and required humans actually reviewed their parts. This document does not authorise spending, publication, contact or legal compliance. AI output remains working material. Use is subject to the Amo.ng Terms; no new licence is asserted.
Marketing Measurement and Experiment Decision Record
Record metric definitions, comparison/design, observations, uncertainty, competing explanations and the next human decision.
Preview template: Marketing Measurement and Experiment Decision Record
Marketing Measurement and Experiment Decision Record
Editable Markdown working material. Use for a measurement decision whether or not an experiment exists. Complete the experiment branch only for a proposed or actual study, and label its state.
1. Question and authority
Record/version/date:
Business question and decision horizon:
Proposed action and accountable decision owner:
Existing authority, budget/operational limits and conditions:
Reporting question, predictive question or causal question:
Approved data access, use and AI-provider disclosure:
Evidence cut-off, timezone, currency and reporting basis:
An unknown field stays Unknown. A recommendation is separate from an authorised decision.
2. Metric and source definitions
Metric ID
Definition / formula / unit
Numerator and denominator
Population, grain, window and exclusions
Source/version/owner
M1
Distinguish event, customer, order and revenue
Specify each
Include maturity and returns where relevant
Actual reference
Source ID
Evidence/locator and extraction time
Observed / modelled / attributed
Identity/deduplication and permission limits
Quality or coverage gap
E1
Actual supplied source
Do not combine silently
What can/cannot be linked
Missing does not mean zero
3. Baseline and comparison
Comparison
Baseline value/period
Compared value/period
Definition compatibility / maturity
Calculation and limits
Metric ID
Supplied value or Unknown
Supplied value or Unknown
Compatible / Conditional / Not comparable, with reason
Show arithmetic; no unsupported inference
Record attribution model, lookback windows, interaction-date versus conversion-date reporting, consent/reporting changes, refunds/cancellations, cohort age and source reconciliation differences. Do not sum credited channel conversions as unique incremental sales. Keep prior definitions and later changes visible.
4. Checks: proposed versus performed
Check
Expected result
Actual supplied result / Not performed
Source, date and version
Consequence / owner
Tracking, duplication, missingness or reconciliation check
Agreed expectation
No invented test pass
Actual evidence
Blocked interpretation or next check
5. Study-design branch
Select: No experiment / Proposed experiment / Conducted study with supplied evidence / Incomplete record.
If no experiment exists, record the comparison's limitations and skip claims about randomisation, significance or causal lift. Observational causal inference also needs an explicit credible design and specialist review; it is not obtained by relabelling a dashboard comparison.
Design element
Prespecified plan/reference
Actual implementation/evidence
Deviation or unresolved issue
Hypothesis, decision and target effect
Before results where applicable
Record
Do not backdate
Population, assignment/exposure unit and comparison
Design rationale
Actual counts/logs
Selection, spillovers or interference
Primary outcome and guardrails
Metric definitions, horizon
Observed results
Metric changes
Sample/information and uncertainty approach
Inputs and specialist rationale
Actual method/results
Do not invent power or validity
Duration, stopping rule and exclusions
Original rule and approval
Actual timing and exclusions
Peeking, post hoc choices, missingness
Assignment/exposure/analysis integrity
Required checks
Actual checks, including imbalance where relevant
No automatic pass from a favourable result
6. Interpretation and alternatives
Proposition
Observed result / attribution / causal claim / hypothesis
Evidence and contrary evidence
Uncertainty or assumption
Smallest discriminating check
One proposition per row
State category
Use source IDs
Design limits, intervals only if supported
Proposed, not completed
Separate average from marginal return; revenue from contribution/profit; credited from incremental outcomes; absence of statistical significance from evidence of no effect. If a causal interpretation is proposed, state identification assumptions and what evidence supports them. Do not choose a universal significance threshold or decide legal compliance.
7. Human interpretation and next decision
Analyst review and actual competence/scope, or Pending:
What the evidence supports:
What it does not support:
Decision boundary / agreed criterion and whether it was set before results:
Alternatives and trade-offs, including Hold / Investigate / Bounded test:
Human disposition, rationale, date and evidence reference, or Pending:
Approved action/version/ceiling and operational owner, or Not authorised:
Monitoring, stop condition and review trigger to be agreed:
Actual action and later verification evidence, or Not performed:
8. Change and learning record
Date/version
New evidence or definition change
Effect on interpretation/decision
Owner and re-review status
Complete
Preserve the original decision record
Explain
Open until evidenced
This is a working record, not statistical validation, approval to spend or evidence of causality merely because a form is complete. AI output requires professional review. Apply the organisation's actual access, retention and deletion rules; no period is supplied here. No new licence is implied.
Both are synthetic practice cases: the organisations, data and events are fictional. Analyse the learner evidence and explain your reasoning. Facilitator guidance and answer keys are not included.
Synthetic practice case · learner version · Markdown
Ambiguous Campaign Performance and Measurement
Challenge a proposed budget move when attribution windows, tracking evidence and business outcomes conflict.
Preview learner evidence: Ambiguous Campaign Performance and Measurement
Ambiguous Campaign Performance and Measurement
Synthetic practice case — learner version. All businesses, records, amounts and events below are fictional. They are not a real campaign, client, platform finding or assurance engagement. Currency units (CU) are invented accounting units, not a benchmark.
Your decision
A fictional retailer sells desk organisers at CU100 per order. On 29 September 2026 its growth lead asks to move CU3,000 of a proposed CU12,000 next-fortnight budget from paid search to paid social. The reason given is that social's attributed ROAS increased. Finance has authorised no reallocation yet. The decision deadline is tomorrow; the analyst has only the following extracts. Prepare an evidence-based challenge and the smallest useful next check.
Evidence A — platform summaries
Extracted 29 September, 09:00 UTC. P1 = 1–14 September; P2 = 15–28 September, inclusive. Spend is CU, excluding tax, on delivery dates. Each row is the relevant platform's own report; cross-channel credit may overlap. Clicks and impressions are supplied delivery counts. Attributed orders/revenue are report credit, not deduplicated business sales.
Channel / period
Spend
Impressions
Clicks
Attributed orders
Attributed revenue
Search P1
6,000
100,000
5,000
120
12,000
Search P2
6,000
120,000
4,800
132
13,200
Social P1
4,000
200,000
4,000
80
8,000
Social P2
6,000
400,000
4,000
150
15,000
The search export is on an interaction-date basis with a seven-day click window in both periods. Social P1 uses seven-day click/one-day view; P2 uses 28-day click/seven-day view after an export-setting change. Both social exports use interaction dates. No matched-window re-export is supplied. Late credited conversions may still change recent values. These fictional settings describe this exercise, not any platform's default policy.
Evidence B — analytics and order summaries
Analytics purchase-event extract, generated 29 September 09:30 UTC, uses event dates and session-channel grouping. It is not the same attribution surface as Evidence A. The analyst supplied the following counts: Search P1 112, Search P2 125; Social P1 65, Social P2 160. Order-level reconciliation and modelling/filter details were not supplied. Do not treat all purchase events as unique orders.
The CRM order export below was generated 29 September 10:00 UTC. It counts unique confirmed orders by order date, using the last captured source recorded at checkout. Other includes direct/unknown sources. Source assignment is not a causal measurement. Each order has CU100 gross booked value, before the refunds shown; no partial refunds occur in this fictional extract.
CRM source / period
Confirmed orders
Refunds recorded by extract time
Gross booked value
Search P1
100
10
10,000
Social P1
60
6
6,000
Other P1
40
4
4,000
Search P2
110
2
11,000
Social P2
65
2
6,500
Other P2
45
1
4,500
Finance reports a preliminary 40% contribution rate on net sales before media, service overhead and fulfilment-capacity changes. Its calculation detail is absent. Refunds can arise up to 30 days after an order under this fictional business's supplied policy. Neither period is fully mature at the extract time; P2 is especially recent. No channel-level variable-cost or lifetime-value evidence is supplied.
Evidence C — change log and actual limited QA
Date
Supplied record
15 September
Social spend allocation increased; broader placements introduced. Placement-level outcome export is missing.
18 September
Purchase-tag release added a browser callback. Release note says deduplication was planned; no full release QA record is attached.
20 September
Consent-banner text changed. No before/after acceptance-rate or event-coverage analysis is attached.
22 September
A delivery-delay warning was added to one landing-page variant. Exposure and order effects have not been analysed.
28 September
Controlled staging checkout T17 generated two browser purchase events and one server event for one test order; all three lacked a shared event identifier in the supplied debug trace. This is one staging test, not a production error-rate estimate.
The creative inventory lists the same three social concepts in P1 and P2. Comparable frequency, reach, exposure by asset and outcome-quality data are absent. The media lead says “people are tired of the ads”; this is an assertion, not a supplied study. No randomised incrementality or controlled creative experiment was run in this scenario.
Your working output
State the decision and which quantities can be compared or calculated safely. Show arithmetic and definitions.
Separate observed delivery, attributed outcomes, business records, hypotheses and unsupported causal claims.
Identify conflicts and plausible explanations, including what the staging check does and does not show.
Request a small, prioritised set of evidence that could change the decision; do not request every possible report.
Compare a hold, investigation and bounded test/change proposal within actual human authority. Address the deadline without pretending certainty.
Complete the Marketing Measurement and Experiment Decision Record with unanswered fields visible. Record the human decision as Pending.
Useful methods: AMO-P-000365; AMO-P-000254 for a fatigue branch, AMO-P-000226 for tracking checks, AMO-P-000227 for attribution scope, AMO-P-000192 for a proposed experiment, AMO-P-000059 only when an actual experiment has results. None executes a campaign or authorises spend.
There is no hidden correct budget allocation. Your task is to make a defensible next decision under uncertainty. Do not invent an experiment, causal lift, permission or profitability proof. Discuss your reasoning before consulting a separately controlled debrief.
Synthetic practice case · learner version · Markdown
Lifecycle and Lead Handling with Conflicting Permission and Outcome Records
Review conflicting permission, suppression and routing records without treating possession as authority to contact leads.
Preview learner evidence: Lifecycle and Lead Handling with Conflicting Permission and Outcome Records
Lifecycle and Lead Handling with Conflicting Permission and Outcome Records
Synthetic practice case — learner version. All record keys, organisation rules, events and values are fictional. No real personal data appears. This is practice material, not a real organisation, legal rule or audit finding.
Your decision
A fictional software trial business plans a promotional onboarding email and an advertising audience upload at 12:00 UTC on Monday 5 October 2026. A manager wants every CRM lead included because it was “already collected”. Marketing operations has received conflicting exports and a complaint about slow lead handling. Decide what must be held, clarified or separately authorised; do not contact or upload anything.
Evidence A — scenario rules, not law
The fictional organisation's approved rule v3 says promotional email requires a traceable affirmative email choice for the stated offer and no later withdrawal. A trial-service request alone is not promotional permission. Advertising matching requires a separate recorded owner assessment and approved destination; email permission does not cover it automatically. A known suppression must not be overridden by an older extract. Ambiguous records are held for the privacy/channel owners. This fictional rule is not a universal account of legal permission.
Routing rule v2: newly accepted trial requests go to the trial queue. SLA = assigned to a responsible owner within two business hours, Monday–Friday 09:00–17:00 UTC, excluding no additional holidays in this exercise. This SLA measures assignment, not first contact. Automated promotional contact remains subject to permission checks. All timestamps below are UTC; do not invent a different timezone or clock.
Evidence B — minimal permission and source records
No withdrawal in supplied source history; complete destination check not supplied
Marketing=True
L02
1 Oct 11:00 offer-email choice Yes; notice v3
4 Oct 16:00 withdrawal acknowledged in permission service
Marketing=True
L03
2 Oct 15:00 webinar attendance list from partner
Partner says contacts are “approved”; underlying notice, choice and onward-use terms absent
Marketing=True
L04
2 Oct 16:30 trial request; offer-email choice No; notice v3
No later promotional choice supplied
Marketing=True
L05
5 Oct 08:30 offer-email choice Yes; notice v3
Not yet present in the earlier CRM extract
Missing
L06
1 Oct 12:00 offer-email choice Yes on key A; withdrawal 3 Oct 09:00 on key B
CRM says A/B may be one person; approved merge/match evidence absent
Marketing=True on A
Keys are artificial exercise labels, not hashes of real identifiers. No phone, address, email or demographic attribute is supplied. The destination upload specification includes offer-interest and a hashed contact field, but no separate advertising-use assessment or destination approval is supplied. Do not infer that hashing resolves this gap.
Evidence C — suppression and export chronology
Sending system snapshot at 3 October 08:00 shows none of L01–L06 suppressed. It predates the L02 withdrawal and the L06 key-B event.
Integration log at 5 October 09:00 says “sync job accepted”. It supplies no per-record result, payload scope or destination acknowledgement.
Promotional audience file assembled at 5 October 09:15 contains L01, L02, L03, L04 and L06-A from the 08:00 CRM export. L05 is not included.
No send or advertising upload has occurred in this case. The manager's requested noon launch is a proposal.
Evidence D — routing and service observations
Trial key
Accepted trial time
Assignment record
First human-response record
L01
Thu 1 Oct 10:00
Thu 1 Oct 10:30
Thu 1 Oct 12:30
L02
Thu 1 Oct 11:00
Thu 1 Oct 14:00
Thu 1 Oct 14:20
L04
Fri 2 Oct 16:30
Mon 5 Oct 09:45
Mon 5 Oct 10:10
L05
Mon 5 Oct 08:30
No assignment observation by Mon 5 Oct 10:00
Not supplied
The rules do not say webinar attendance is a trial request. L03 has no acceptance timestamp, so the assignment SLA cannot simply be applied. L06's routing identity cannot be resolved from the supplied keys. Service-response evidence does not establish a later marketing right.
Evidence E — message and outcome summaries
A previous, separate test cohort—not the six leads above—had 100 send attempts: 92 accepted by receiving servers, 5 rejected and 3 deferred. The dashboard reports 60 unique opens and 8 unique clicks out of those 100 attempts. Inbox placement and human-only opens were not measured. The message was promotional despite an account-related subject. No outcome attribution study exists.
A retention slide compares two different start cohorts: 100 trials starting 1 September had 28 paid accounts by day 30; 100 trials starting 1 October had 20 paid accounts by day 4 as of 5 October. Outcome definition is a paid account at the stated observation point. No matched-maturity or mix-adjusted comparison is supplied. The manager calls this “proof that the new routing hurt retention”.
Your working output
Prepare a source-and-time permission map for each proposed use. Distinguish plausible evidence from final authorisation, absence and conflict.
Identify which records/uses require containment and which evidence the human owners need before deciding. Preserve withdrawals and ambiguity; do not invent a consent result.
Reconstruct the routing clock separately from response time. Explain which SLA conclusions are supported, pending or inapplicable.
Distinguish server acceptance, inbox placement, opens, clicks and business outcomes. Challenge the retention assertion without inventing a cause.
Complete the Campaign Evidence and Human Decision Record. Keep promotional email, trial service and advertising matching as separate decisions. Name review roles, with actual people left Unassigned.
Useful methods: AMO-P-000364; AMO-P-000025 for lifecycle logic, AMO-P-000220 for routing/SLA review, AMO-P-000255 for deliverability questions, AMO-P-000267 for cohort interpretation. AI cannot approve permission, overwrite a CRM record or send a message.
Do not treat higher conversion as permission to ignore restrictions. More than one proportionate investigation plan may be defensible. The separate facilitator debrief is not part of the evidence supplied here.
These original summaries connect a practical marketing question to primary authority records. Read the questions before opening the outcome. Allegations, settlements, undertakings and findings remain distinct; each case has its own jurisdiction and status.
Real case study · Messaging, content and campaign review
Lord & Taylor: review paid relationships and the final placement
A creative approval can miss how viewers understand who paid for a message.
Read case study: Lord & Taylor: review paid relationships and the final placement
Background and issue
The FTC complaint concerned the retailer’s 2015 Design Lab promotion, including influencer posts and paid Nylon material.
The complaint alleged that commercial relationships were not disclosed. Those allegations are distinct from findings after a contested trial.
Questions for your review
What relationship and benefit records would you request?
How would you verify the disclosure in the actual published placement?
Read the authority’s outcome and professional lesson
What was found, agreed or ordered
The FTC issued its final consent Decision and Order in C-4576 on 20 May 2016. It imposed controls on independence representations, material-connection disclosures and endorsement oversight. The settlement records non-admission/non-denial of allegations except as stated, including jurisdictional admissions.
Professional lesson
Review the relationship record and the whole communication, then record who must resolve any missing disclosure. An internal label is not proof that an audience saw it.
United States · FTC administrative consent order / settlement, not a trial finding of fraud. Official sources rechecked 11 October 2026; no later modification or reversal identified in the focused check. This is not an exhaustive litigation-status search.
This learning summary does not determine your legal compliance or establish current compliance by the organisation discussed. Lessons are Amo.ng’s interpretation, not authority endorsement. Apply relevant local requirements with appropriate professional advice.
Real case study · Positioning, claims and brand trust
Shell: assess each advertisement in its full context
A claim’s overall impression depends on its wording, imagery, qualifications and surrounding context.
Read case study: Shell: assess each advertisement in its full context
Background and issue
Two UK ASA rulings address different Shell advertisements: 7 June 2023 (G22-1170842) and 9 April 2025 (G24-1248246).
The 2023 case examined the impression created by lower-carbon promotions. The later case concerned a different TV advertisement showing both gas activities and lower-carbon initiatives.
Questions for your review
What would you preserve from each complete ad before comparing them?
Which evidence supports a narrow product claim, and which would be needed for a broader impression?
Read the authority’s outcome and professional lesson
What was found, agreed or ordered
In 2023 the ASA upheld the material-omission issue: the ads gave a misleading impression of the balance of Shell’s activities. A separate renewable-electricity issue was not upheld. In 2025 the complaint about the different ad was not upheld; the ASA considered its wider creative and qualifying investment information sufficient in that context.
Professional lesson
Check the actual version and overall impression. Neither an isolated fact nor the outcome for another advertisement settles whether a new claim is supportable.
United Kingdom · ASA advertising self-regulatory decisions, not court judgments. These are separate advertisements and decisions; the 2025 ruling is not presented as an appeal or reversal of 2023. Rechecked 11 October 2026; no different status identified in the focused official-source check.
This learning summary does not determine your legal compliance or establish current compliance by the organisation discussed. Lessons are Amo.ng’s interpretation, not authority endorsement. Apply relevant local requirements with appropriate professional advice.
Real case study · Audience permission and customer-data handling
GoodRx: trace data use beyond collection
Collecting information for one service does not settle whether it may be disclosed for advertising.
Read case study: GoodRx: trace data use beyond collection
Background and issue
The FTC/DOJ action concerned health and personal information handled by GoodRx. The complaint alleged unauthorised disclosures, misleading representations and notification failures.
Review the distinction between what the complaint alleged and what the settlement order required. Do not infer advertising permission from technical access or a vendor connection.
Questions for your review
Which purposes, recipients and disclosures would an audience-use review trace?
What record would establish authority for a proposed downstream use?
Read the authority’s outcome and professional lesson
What was found, agreed or ordered
A federal court entered the stipulated order on 17 February 2023. The settlement includes a $1.5 million civil penalty, restrictions on advertising-related health-data disclosure and corrective privacy obligations. The complaint allegations are not described here as contested trial findings.
Professional lesson
Map intended use to actual authority, restrictions and recipient evidence. Ask the responsible privacy specialist to resolve material uncertainty before any activation.
United States · Northern District of California, 3:23-cv-00460. Entered stipulated order; the FTC catalogue still labels the matter Pending. That label is not proof the order was unentered. Rechecked 11 October 2026; no later reversal identified in the focused sources; no exhaustive docket search claimed.
This learning summary does not determine your legal compliance or establish current compliance by the organisation discussed. Lessons are Amo.ng’s interpretation, not authority endorsement. Apply relevant local requirements with appropriate professional advice.
Real case study · Conversion, claims and campaign review
Wowcher: test urgency and choice against evidence
Countdowns, scarcity messages and default selections can change a customer’s decision.
Read case study: Wowcher: test urgency and choice against evidence
Background and issue
The UK CMA investigated urgency claims and pre-selected paid membership in Wowcher’s online selling.
The CMA raised concerns that some timers and scarcity/popularity claims could mislead or pressure consumers, and that pre-ticked membership choices could be misunderstood.
Questions for your review
What actual stock, availability and timer behaviour would support the message?
How would you inspect the full choice and payment journey rather than just its conversion rate?
Read the authority’s outcome and professional lesson
What was found, agreed or ordered
The CMA accepted voluntary undertakings on 15 July 2024 and published them on 19 July. They cover clearer timers and claims, changes to pre-ticked VIP sign-ups and refunds. They were given without admission of wrongdoing or liability; this was not a court finding that Wowcher broke the law.
Professional lesson
Review the evidence behind urgency and the customer’s actual choice. A conversion increase alone does not validate the practice.
United Kingdom · CMA consumer-enforcement investigation closed with undertakings. Official case status rechecked 11 October 2026. Undertakings do not prove current compliance, and the 2024 outcome should not be restated as a court judgment.
This learning summary does not determine your legal compliance or establish current compliance by the organisation discussed. Lessons are Amo.ng’s interpretation, not authority endorsement. Apply relevant local requirements with appropriate professional advice.
Real case study · Lifecycle communication and permission
Experian Consumer Services: classify the message, not just the account
Having a service relationship with someone does not by itself settle how a promotional message should be handled.
Read case study: Experian Consumer Services: classify the message, not just the account
Background and issue
The United States alleged that ConsumerInfo.com, doing business as Experian Consumer Services, sent marketing email without required opt-out facilities.
The complaint’s allegations must be distinguished from the terms of the settlement. An account-related subject line does not alone establish that the message is purely transactional.
Questions for your review
What would you examine to establish a message’s actual purpose?
How would you verify the opt-out mechanism and its effect on later promotional sending?
Read the authority’s outcome and professional lesson
What was found, agreed or ordered
The signed order, Document 10 filed 21 August 2023, requires commercial-email controls and a $650,000 civil penalty. Sections I–II address message classification, a functioning opt-out mechanism and notice. The findings record non-admission/non-denial except as stated, jurisdictional admissions and waiver of challenges to the order.
Professional lesson
Review purpose, content, recipient context and observed preference handling. Keep a service relationship separate from assumptions about promotional contact.
United States · Central District of California, 8:23-cv-01494-FWS-JDE. Court-entered stipulated order, not just the 14 August proposed order. Signed/filed 21 August; official publication followed on 22 August 2023. Rechecked 11 October 2026; focused sources did not identify a later reversal. No exhaustive docket search claimed.
This learning summary does not determine your legal compliance or establish current compliance by the organisation discussed. Lessons are Amo.ng’s interpretation, not authority endorsement. Apply relevant local requirements with appropriate professional advice.
Work with your preferred AI
Open a resource to read its instructions and use them with the named tool. Supported Prompts and Workflow steps can run in Workplace using your own supported provider. Skills provide reusable instructions; they do not run themselves.
If you run supported resources in Workplace, your selected AI provider may charge for its own API usage.
Review each result before carrying information to the next step. Actual interviews, external tools, Codex and operational changes remain manual or external where specified. There is no silent provider substitution. Only use information authorised for disclosure to your selected provider; prefer synthetic or minimised material and never paste credentials.
AI output is working material. It must not automatically approve claims, activate audiences, authorise customer-data use, spend advertising money, launch or pause campaigns, publish content or send marketing messages. It does not determine legal compliance, fabricate market research or establish causality without evidence.
Humans retain responsibility for claims, permission and consent, targeting, spending, publication, customer data, measurement interpretation and business decisions. A generated result or Run Record is not proof that evidence is sufficient, a test passed or an approval occurred.
This Pack supports general marketing decision work. Specialist brand valuation, crisis command, advanced causal modelling and regulated, children’s or electoral marketing require an appropriate specialist process. Product, pricing, legal and spending authority remains with your organisation.
For organisations
Bring the Pack to your marketing & growth team
RichlyAI offers facilitated training, private cohorts and organisation-specific adaptation for teams that want to apply these methods across customer research, campaigns, content, paid media, lifecycle, measurement and growth decisions.
The public Amo.ng Pack remains free. Institutional services add facilitation, adaptation and organisation-specific support.
