Bring product feedback from multiple channels together
This workflow combines messages, forms, and customer context into a shared feedback database with AI search. Designed for product teams.
How this works
This workflow combines messages, forms, and customer context into a shared feedback database with AI search. Designed for product teams. Use this to create a reliable searchable feedback repository from multiple incoming channels. The main outcome is normalized, traceable records that later analysis can query without repeatedly rebuilding the collection.
Define a canonical record and stable ingestion keys before adding AI classification. Preserve raw comments, split distinct needs, attach permitted account context and update records idempotently. Search answers should cite stored feedback rather than treat generated summaries as new customer evidence.
Before you start
Channel inventory
Choose forms, messages and support exports with defined access.
Canonical schema
Agree record IDs, source references, timestamps, topics and context fields.
Repository destination
Use an available database or a file-based table with a documented update process.
Prepare the inputs
- channel_records
- Required
source ID, message ID, text, timestamp and author reference
Retain source identity and avoid duplicate imports.
- customer_context
- Required
account segment and permitted metadata
Join through stable IDs and record unknown matches.
- topic_taxonomy
- Required
approved topic definitions
Allow unclassified records instead of forcing a weak match.
- ingestion_state
- Required
last cursor, fingerprints and prior record IDs
Support safe reruns without overwriting original evidence.
- demand_query_rules
- Optional / conditional
Question, observation windows, independent-count unit and permitted segment filters.
Use only for demand-count queries. Distinguish messages, requests, respondents and customer accounts; unknown account matches cannot support a claimed unique-account count.
Implementation, step by step
Define the record contract
- Map each channel's fields to the canonical schema.
- Record timezone, deletion and edit behavior before importing.
- Include observation date, feedback type, verified account ID, permitted segment and a restricted raw-evidence reference. Keep support-ticket state separate from whether a feature need is fulfilled.
Checkpoint: The schema supports all selected channels.
Import raw feedback
- Save source text and immutable references before AI processing.
- Use source-message keys or fingerprints to detect repeated imports.
Checkpoint: The raw layer is complete and deduplicated.
Normalize individual needs
- Extract separate observations while preserving their parent message.
- Classify topics and attach only verified customer context.
- Distinguish requests, praise, complaints and operational questions. Link repeated statements about the same need and account without erasing the individual evidence records.
Checkpoint: Each normalized record points to its original comment.
Build retrieval
- Index permitted text and structured filters for product questions.
- Return evidence-linked results and respect source restrictions in answers.
- For demand queries, return separate mention and distinct-account counts with the time window, segment filter and coverage. Provide representative permitted excerpts linked to the underlying records.
- Compare periods only with compatible coverage and counting units. Preserve positive, negative and minority views; a before/after association does not establish the effect of a release.
Checkpoint: Search can find records by topic and context.
Maintain updates
- Apply source edits as new versions and reconcile missing or deleted records according to policy.
- Log ingestion counts, failures and classification corrections.
- A closed support ticket does not close a feature request. If access is revoked or feedback changes, refresh affected counts and excerpts as well as the searchable record.
Checkpoint: The repository remains auditable across runs.
Decisions along the way
One comment contains two requests
Create two linked observations under one parent message.
Account join is uncertain
Keep customer_context unknown rather than infer from a similar name.
Source record is edited
Preserve version history and refresh affected summaries.
A customer repeats one request across several tickets.
Retain the messages but count one account for that theme and period when the query asks how many customers requested it.
A large segment contributes more messages than another.
Report counts with available segment coverage; do not claim higher prevalence without a suitable denominator.
What you will produce
feedback-records.jsonl
JSONLA reusable searchable feedback dataset.
Include: record_id · parent_id · source_ref · text · topic · account_context · version · observed_at · feedback_type · account_id · source_ticket_state · need_state · access_scope
ingestion-report.json
JSONA reconciliation record for each import.
Include: source · received · inserted · updated · duplicates · failed
feedback-evidence-query.json
JSONOptional saved query over the repository, with reproducible count definitions.
Include: query · period · segment_filter · count_unit · mention_count · distinct_account_count · unknown_account_count · coverage · evidence_refs
Check the result
- Reconcile received records with inserted, updated, duplicate and failed counts.
- Test repeated imports for duplicate prevention.
- Verify search results cite original comments.
- Check source permissions survive normalization and retrieval.
- Check a multi-ticket customer does not inflate a distinct-account count and that unknown identities stay separate.
- Verify query excerpts honor original access and that closed tickets retain their unresolved product needs.
When something goes wrong
Connector cursor expires
Resume from a verified checkpoint and deduplicate replayed records.
Malformed source record
Quarantine it with the raw payload and continue unaffected records.
Search summary invents a need
Remove the unsupported statement and cite only stored observations.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Synthetic: import feedback from a form and support export.
Inputs
Form F8 says 'save filters and export results'; support record S4 repeats the same customer's export request and references F8. Both records match account A2; another export request comes from A3, while a third source cannot be matched to any account.
Expected result
Synthetic result: create two observations under F8, link S4 as corroborating evidence for export, and avoid counting it as a second independent customer request. For an export-demand query, show two verified distinct accounts plus one unknown account record, not four customers. Keep the unresolved request even if its support ticket closes.
Run a small pilot
Start small
Ingest two channels for one product area with a controlled replay.
Record the baseline
Record manual collection effort and missing records.
Agree success criteria
Agree ingestion completeness, duplicate handling and retrieval accuracy before adding channels.
Stop or revise
Stop automated updates if source IDs or permissions cannot be preserved.
Keep the scope clear
- A searchable record is not a verified product requirement.
- The workflow may organize permitted feedback but cannot expand access to restricted source content.
Check before using
Check the meaning of the original comments and look for duplicates. The team sets product priorities.
Business activities
- Resolve customer questions
- Plan products and business changes
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Feedback messages and forms · Customer interactions · CRM data
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow