01
Step 1
Collect the control and variant landing pages, the single change that separates them, the assignment rule and traffic split, the primary metric with its guardrails, and the merchant's stated observation window and stopping rule.
Back to topic
Spark playbook scenarios · Spark: AI Store Assistant
Design a controlled landing-page experiment
A rebuilt landing page is ready, but the change and the measurement rule were never written down. In this case, the immediate task is to design a controlled landing-page experiment. The workflow should produce an A/B test plan and decision framework, using the merchant's own page records, traffic split and observation window.
Topic
Page A/B Testing
Target keyword
shopify landing page ab testing
Copyable AI prompt
A prompt you can drop directly into Spark
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me design a controlled landing-page experiment.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the control and variant landing pages, the single change separating them, the assignment rule and traffic split, the primary metric with its guardrails, and the merchant's stated observation window and stopping rule]. Assign each visitor by a stable rule that keeps them in one variant for the whole window, measure the primary metric with one definition throughout, and stop early only on a stated guardrail breach or a confirmed setup error, recording any other early stop as invalid.
Return an A/B test plan and decision framework for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Assign each visitor by a stable rule that keeps them in one variant for the whole window, measure the primary metric with one definition throughout, and stop early only on a stated guardrail breach or a confirmed setup error, recording any other early stop as invalid.
03
Step 3
Deliver an A/B test plan with the decision framework including the rule for inconclusive results, verify the assignment and metric definitions against the supplied page records, and report the outcome with the audience actually tested named.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
How are visitors kept in one variant?
Assignment uses a stable rule recorded per visitor so they see the same variant for the whole window, because reassigning mid-run mixes the two experiences and neither result can be trusted; visitors who clear their identifier are counted once and those switching variants are excluded from the analysis.
Can the test cover only new visitors?
It can, provided the plan says so, since returning visitors arrive with expectations the variant may not meet and behave differently; the finding then applies only to the audience actually tested, and the plan states that boundary rather than letting it be read as store-wide.
When is an early stop allowed?
Only for a stated guardrail breach or a confirmed setup error such as a variant that does not render, because stopping when the result merely looks favourable captures a random high and the decision does not hold; any other early stop is marked invalid and the run is repeated.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Next step
Want these AI scenarios embedded directly into daily operations?
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.