01
Step 1
Start with customer identifiers, contact records, matching rules and conflicting field examples.
Back to topic
Spark playbook scenarios · Spark: AI Store Assistant
Organize customer contact records for reliable follow-up
Customer records are fragmented or duplicated, making follow-up and reporting unreliable. In this case, the immediate task is to organize customer contact records for reliable follow-up. The workflow should produce a contact cleanup plan and proposed duplicate groups, using the merchant's actual records and constraints.
Topic
CRM and Contact Management
Target keyword
shopify customer contact management
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 organize customer contact records for reliable follow-up.
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: [provide customer identifiers, contact records, matching rules and conflicting field examples]. Normalize fields, propose duplicate groups with confidence reasons and retain source identifiers for review before any merge.
Return a contact cleanup plan and proposed duplicate groups 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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
Customer records are fragmented or duplicated, making follow-up and reporting unreliable. In this case, the immediate task is to organize customer contact records for reliable follow-up. The workflow should produce a contact cleanup plan and proposed duplicate groups, using the merchant's actual records and constraints.
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
Normalize fields, propose duplicate groups with confidence reasons and retain source identifiers for review before any merge.
03
Step 3
Deliver a contact cleanup plan and proposed duplicate groups; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Can matching names prove two records are the same person?
No. Names can be shared. Use stronger identifiers and review conflicting evidence.
Why avoid silently merging ambiguous records?
Incorrect merges can mix purchase history and communications belonging to different customers.
What information is needed to investigate this use case?
Start with customer identifiers, contact records, matching rules and conflicting field examples. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
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.
Identify likely duplicate customer records for review
Customer records are fragmented or duplicated, making follow-up and reporting unreliable. In this case, the immediate task is to identify li…
Organize customer information for practical CRM use
Customer records are fragmented or duplicated, making follow-up and reporting unreliable. In this case, the immediate task is to organize cu…
Design a loyalty proposition with clear eligibility
A merchant wants repeat business but lacks a clear retention proposition or eligibility rules. In this case, the immediate task is to design…
Surface damaged-shipment reports for follow-up
Customers and operators lack a clear view of delayed, lost or damaged shipments. In this case, the immediate task is to surface damaged-ship…
Plan a measurable customer retention initiative
A merchant wants repeat business but lacks a clear retention proposition or eligibility rules. In this case, the immediate task is to plan a…
Design recurring audits for product data quality
A catalog has incomplete or contradictory information that is hard to review product by product. In this case, the immediate task is to desi…
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.