Can synthetic research represent a B2B buying committee?

Specify the decision, not just the audience

Write the intended output into the brief: candidate objections, message ratings by purchasing role, supplier shortlisting or actual purchase predictions. Each needs a different evaluation. Reviewing whether objections are relevant can inform interview preparation; it does not establish how common those objections are among real buyers.

Define the buying situation too. A routine reorder and a first purchase involving a technical review should not silently share the same decision process. Record the product category, decision stage and scope of authority being modeled.

Count accounts and people separately

Ask what one row represents: a company, location, employee or purchasing decision.

The U.S. Census Bureau's business glossary distinguishes a company under common ownership or control from an establishment at a single physical location. Its economic-survey standards separately define the unit selected, the unit supplying information and the unit used for tabulation.

Apply that distinction to your brief. Are you studying people evaluating software at eligible companies, or the companies themselves? How do several stakeholders from one account contribute to a reported percentage? Choose account and person weights to fit the question. Generating more profiles does not increase the number of companies observed in human research.

Define purchasing roles beyond titles

Webster and Wind's organizational buying model identifies users, influencers, deciders, buyers and gatekeepers within the buying center. It also describes organizational resources, authority and interpersonal relationships as influences on purchasing (Journal of Marketing, April 1972, pages 14–16).

Ask who uses the product, evaluates it, approves the supplier and negotiates terms. A title alone does not verify purchasing authority or knowledge.

Require shared account facts to be distinguished from assumptions about budget, existing systems, approval requirements and timing. Keep missing information unknown or test explicitly labeled scenarios. Avoid assigning fixed attitudes to roles, such as making finance oppose every purchase.

Inspect the committee's decision rule

Ask how stakeholder outputs become an account conclusion. Does the system average ratings, count preferences, simulate a discussion or apply approval rules? Which rules came from evidence, and which were chosen for the exercise?

Majority preference does not automatically mean approval. A majority-vote rule or a simulated veto is a modeling assumption unless supported by evidence about that buying process. Retain unresolved questions rather than turning every uncertainty into consensus.

Illustrative example: 36 profiles, 12 accounts

A fictional study models 12 companies with three stakeholder profiles at each: a product user, technical evaluator and purchasing approver. It contains 36 profiles linked to 12 accounts. All counts and outcomes in this example are invented.

Suppose 24 profiles prefer message A. That does not mean 24 companies prefer A, or that two-thirds of companies would buy. The first claim counts the wrong unit; the second substitutes purchasing for message preference and assumes an untested account decision rule.

At one account, the user prefers A, but integration requirements and available budget are unknown. Investigating those conditions with appropriate human stakeholders adds evidence. Inventing favorable answers only completes the scenario.

Test the output at the level you will use

Compare role-level claims with human evidence from people meeting the same purchasing criteria. Evaluate account-level predictions against account-level outcomes. Report both the number of human participants and the number of distinct accounts in the comparison.

If the intended use concerns companies absent from development data, keep each evaluation company's records together and out of development and tuning. This applies the grouped-evaluation principle in scikit-learn's documentation: testing generalization to unseen groups requires separating those groups. Predicting a later decision at a known account needs an evaluation suited to that timing and available information.

Agree on scoring, a relevant baseline and failure conditions before seeing results. If suitable human cases are insufficient, label the claim untested or inconclusive. See how to validate a synthetic research panel for the broader blind-test procedure.

Put these requirements in the brief

• Eligible companies, purchasing roles, category and buying situation.
• What each profile and reported percentage represents, including account weights.
• Permitted inputs, shared facts, assumptions and unresolved information.
• How individual outputs become committee conclusions.
• Human evaluation evidence, baseline, scoring and failure conditions.
• Clearly labeled synthetic deliverables and their permitted uses.

Use human interviews or observed purchasing evidence when the decision depends on what actual stakeholders know, have experienced or are authorized to approve. Generated objections should not be presented as statements made by real buyers.

For a Lewsearch project, discuss the audience and evaluation with the team. Enterprise engagements scope the population, intended use and evaluation before building.