Run your first AGI marketing experiment with a clear learning goal

Choose one uncertainty worth resolving

Your first experiment should help you make a specific decision about your marketing. Choose one uncertainty that currently blocks progress: whether a segment recognizes a problem, whether a message earns a relevant response, or whether an offer attracts qualified interest. An agent can help assemble the evidence, prepare the work, and coordinate the review.

That is a practical step toward Taploop’s AGI marketing vision. It is still a bounded experiment using available capabilities, with human approval for publishing, outreach, and spending. Give one person responsibility for the final decision so the learning leads somewhere.

1. Write a hypothesis you can inspect

Use this structure: for a defined audience, a specific change should improve a named outcome because of an observed customer problem. State the evidence behind the “because.” Keep the audience, offer, and distribution reasonably stable if you want to learn about the message; changing everything makes interpretation harder.

If you cannot compare versions fairly, call the work an exploratory pilot and describe what it can reveal. A pilot can uncover objections and measurement problems without proving that one message caused better performance.

2. Define the baseline and measurement

Choose one primary metric and write its numerator, denominator, reporting window, and qualification rules. For the example, that might be qualified demo requests divided by eligible landing-page visitors. Define how duplicate requests, internal testing, and irrelevant submissions will be handled before reading results.

Record the baseline with its dates, counts, and traffic sources. If you have no comparable history, say “no baseline” and use the first pilot to establish one. Historical comparisons can be affected by seasonality, channel mix, or other changes. A side-by-side randomized comparison can help isolate a message change when your traffic and setup support it.

• Primary metric: the customer action that answers the experiment question.
• Diagnostic metrics: arrivals, engagement, and form completion that help explain the path.
• Guardrails: spending, lead quality, operational capacity, and customer feedback.

3. Prepare the campaign and its approvals

Ask the agent for a compact launch package: audience definition, final draft, substantiated claims, destination, distribution plan, measurement specification, owners, and proposed costs. If a designer or other specialist is needed, include a clear brief and acceptance criteria. Verify which tasks the available tools support and assign the rest explicitly.

The approver reviews the exact material to be published, any outreach audience and messages, and the spending limits. Record approval against that version. A change to the offer, audience, destination, or budget may change the decision and needs review before execution.

4. Set stop and review rules before launch

Choose a review date and an exposure target that fit your traffic and decision. Define the maximum approved cost separately. Stop immediately for broken tracking, an incorrect offer, or another material execution problem. Reaching a budget or time limit without enough evidence should produce an inconclusive result, not an automatic winner.

Agree what level of improvement would matter to the business and how much uncertainty is acceptable. Avoid checking a small sample repeatedly until it looks favorable. For a decision requiring statistical confidence, involve someone who can plan sample size and analysis around the baseline and effect you care about.

5. Fill in this experiment brief

Help me prepare one reviewable marketing experiment.

Business decision: [decision]
Audience and exclusions: [definition]
Evidence: [sources and observations]
Hypothesis: [change, outcome, and reason]
Control or baseline: [version, dates, counts, or none]
Primary metric: [numerator, denominator, qualification, window]
Diagnostics and guardrails: [definitions]
Proposed channel and assets: [details]
Maximum spend and duration: [limits]
Exposure target and review date: [values]
Business-relevant improvement: [threshold and reasoning]
Immediate stop conditions: [conditions]
Owner and approver: [roles]

Identify gaps, propose the smallest useful setup, and prepare a launch checklist and results template. Explain what the design can and cannot establish. Do not invent a baseline or guarantee a result. Prepare drafts only; require explicit approval before publishing, outreach, or spending.

6. Verify the journey, then review the evidence

Before launch, follow the visitor journey yourself. Check that the destination loads, the form works, the intended action reaches your records, and test activity is identifiable. For Google Analytics, campaign parameters such as utm_source, utm_medium, and utm_campaign help identify referring campaigns. Keep names consistent and verify that the reporting setup captures them.

At the agreed review, report counts as well as rates, actual cost, execution issues, and departures from the plan. Distinguish attributed activity from evidence that the campaign caused an outcome. Choose continue, revise, stop, or collect more evidence; explain why and require approval for any additional spend. Save the result beside the original brief so the next experiment begins with what you actually learned.