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Customer research for Workflow automation

Use specific search queries, discovery questions, and an evidence worksheet. A practical workflow automation playbook with buyer-specific examples and next steps.

An acquisition guide for founders selling workflow automation. All worked scenarios are illustrative.

Define the question before collecting names

For workflow automation, investigate whether the same customer information is retyped between forms, spreadsheets, and a CRM. Research is useful when it explains a workflow, its owner, and an unresolved constraint. A list of companies in a sector cannot establish that those companies have this problem.

Begin with coordinators copying records between business applications and check how their work relates to an operations manager responsible for repeatable processes. Map the current process around manual copy-and-paste supported by a checklist. You are looking for the step where the workaround stops being adequate, rather than asking people to validate an abstract product idea.

Search for the language of the problem

Try the queries below in a search engine or the search feature of a relevant professional community. Read the original discussion and its date. Results are starting points for research; these query examples do not claim any particular search volume or the existence of a matching prospect.

Query to adaptWhat to extract
"manually copying" "CRM" "forms"The failed task, current workaround, author role, and unresolved question.
"automate" "spreadsheet" "duplicate entry"An evaluation request, required capability, and reason to change.
"workflow automation" "recommend" "alternative"Specific constraints in recommendation requests; separate vendor promotions from user experience.

Write an evidence note you can revisit

Illustrative source text: “Every form submission needs three manual updates. Is there a reliable way to stop rekeying the same request?” Save the actual source URL and date when researching a real example. Keep a short relevant excerpt and your interpretation in separate fields. Do not rewrite your interpretation as something the author said.

The working hypothesis is that request volume has increased beyond one coordinator's capacity. The missing information includes whether the author is an operations manager responsible for repeatable processes, whether the need is still open, and which implementation requirements remain unverified. Keep this constraint visible: connector availability, retry behavior, and duplicate prevention must be demonstrated.

  • Observed: the original words, the visible date, the task described, and any explicit requirements.
  • To verify: whether the current process is manual copy-and-paste supported by a checklist.
  • Disqualify when the need belongs to processes whose required application has no supported access method.
  • Next step: one context-appropriate question, or no contact when there is no suitable route.

Use a short discovery interview

Ask about a recent event before showing your product. Let the person explain the sequence in their own words; do not treat agreement with a leading question as validation.

  • What are the exact input, decision, and output of the last completed request?
  • What should happen if the destination system rejects an update?
  • What would need to be true for you to automate one reversible data transfer with a visible failure queue?
  • How would you verify that an approved sample request reaches the destination with its fields verified?

Turn notes into a research decision

Compare independently sourced accounts of the same task. Keep an example that contradicts your hypothesis, such as this ambiguous scenario: A broad discussion celebrates automation without identifying a repetitive task. It may justify more reading, but it is insufficient to label someone a buyer.

At the end of a bounded research session, decide whether to narrow the audience, ask a specific follow-up, change the pilot, or stop. Keep unknowns visible and store only the information needed for that decision. Research quality is measured by a better decision, not by how many names are collected.

About these playbooks and examples

Prepared by DemandBolt with AI assistance as practical planning resources for people building and selling software. The category scenarios and message examples are illustrative, not quotations from real prospects or claims of customer results. Adapt the hypotheses to your product, verify original sources, and review capabilities before making an offer.

The workflow follows our first-customer guide and buying-signal review method. Our PyInvesting and Rae records distinguish actual internal work from targets. Published .

Turn a hypothesis into a reviewed next step

Keep your research, prospects, and outcomes in one project.

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