How to Run a Query Fan Out Experiment

What is "Query Fan Out Experiment"?

A Query Fan Out Experiment is a systematic, data-driven process for simultaneously testing multiple search terms or "queries" to discover which ones yield the most relevant and high-quality software or service providers. It moves beyond relying on a single, generic search term, which often returns incomplete or biased results.

Businesses waste significant time and resources when their initial search for a vendor is too narrow or based on inaccurate assumptions, leading to missed opportunities and poor procurement decisions.

Search Query Diversification: The practice of using a set of related but distinct keywords to explore a vendor landscape from different angles.
Parallel Testing: Running multiple search investigations concurrently to compare results and identify patterns quickly.
Relevance Scoring: A method to objectively rank vendor results based on how well they match specific, predefined project criteria.
Vendor Discovery Bias: The risk that using only one or two familiar search terms will surface only a limited, often similar, set of providers.
Procurement Funnel: The staged process of moving from initial market exploration to a shortlist of potential vendors.
Decision-Support Data: The concrete evidence—features, client reviews, pricing models—collected during the experiment to inform a final choice.

This approach benefits founders, product managers, and procurement leads who need to make informed, confident purchasing decisions for business-critical software or services. It directly solves the problem of entering a complex market with limited visibility and exiting with a validated shortlist.

In short: It is a structured method to cast a wider, smarter net in your vendor search to ensure you don't overlook the best solution for your needs.

Why it matters for businesses

Ignoring a systematic approach to vendor discovery often leads to suboptimal purchases, wasted budget on ill-fitting tools, and strategic delays that can hinder business growth.

Limited Market View: You may only find the most marketed vendors, not the best-fit ones. A fan-out experiment uncovers niche or emerging providers that better match your specific requirements.
Confirmation Bias in Selection: Teams tend to favor information that confirms their preconceptions. This process introduces objective data to challenge assumptions and validate choices.
Inefficient Use of Time: Serial, one-off searches are slow. Parallel query testing compresses the research phase, getting you to a decision point faster.
High Integration & Switching Costs: Choosing the wrong tool is expensive to fix. Comprehensive discovery reduces this risk by ensuring a thorough evaluation upfront.
Missed Feature Opportunities: A narrow search may miss a solution with a critical, unique feature. Exploring related query sets reveals a fuller spectrum of capabilities.
Poor Negotiating Position: Entering discussions with only one or two options weakens your leverage. A broad, researched shortlist provides alternative choices and strengthens your position.
Team Adoption Risk: A solution that doesn't precisely address user pain points will see low adoption. A thorough search increases the odds of finding a tool your team will actually use.
Strategic Misalignment: A quick choice may solve an immediate symptom but not the underlying business need. This method forces clarity on requirements before evaluating vendors.

In short: It transforms vendor selection from a vulnerable, guesswork-heavy task into a reliable, evidence-based business process.

Step-by-step guide

Starting a vendor search can feel overwhelming, with uncertainty about where to begin and how to ensure thoroughness without getting lost in options.

Step 1: Define your core need and constraints

The pain is a vague project scope, which leads to irrelevant results and scope creep. Start by crystallizing the exact problem you need to solve and your non-negotiable boundaries.

Write a one-sentence problem statement.
List 3-5 mandatory functional requirements.
Define key constraints: budget, timeline, required integrations, and compliance needs (e.g., GDPR).

Step 2: Brainstorm your initial query set

Relying on one keyword is the most common failure point. Generate 5-10 related search phrases that describe your need from different perspectives.

Include: generic industry terms, specific feature names, competitor references ("alternative to X"), and outcome-based phrases ("automate [task]").

Step 3: Execute parallel searches

Manually searching each term in sequence is inefficient. Use multiple browser tabs, a dedicated spreadsheet, or a platform like Bilarna to run these searches simultaneously.

For a quick test, take your top three queries and run them in separate tabs right now. Notice how the results differ.

Step 4: Capture and consolidate results

Data scattered across notes and browser history is unusable. Systematically record every unique vendor found from all queries into a central log.

Log at minimum: Vendor name, source query, and a first-impression relevance score (e.g., High/Medium/Low).

Step 5: Develop and apply scoring criteria

Comparing vendors subjectively leads to biased decisions. Create a simple scoring matrix based on your requirements from Step 1.

Assign weights to categories like core features, pricing transparency, user reviews, and support offerings.
Score each shortlisted vendor (0-5) for each category.

Step 6: Analyze patterns and gaps

Without analysis, a list of vendors is just a list. Look for trends: Which queries yielded the highest-scoring vendors? Are there common strengths or weaknesses among providers?

This analysis identifies if you need to search for more specific features or explore a different aspect of the market.

Step 7: Refine and repeat queries

The first query set is rarely perfect. Use insights from your analysis to create a second, refined set of search terms. Target discovered gaps or dive deeper into promising areas.

For example, if "project management software" yielded broad results, a refined query could be "project management software with native time tracking and Scrum boards."

Step 8: Create a validated shortlist

The final pain is indecision. Your experiment's output is not data, but a clear, ranked shortlist of 3-5 vendors that have been objectively assessed against your needs.

This shortlist, with supporting scores and notes, is your decision-ready artifact for stakeholder review and next steps like requesting demos.

In short: You move from a fuzzy need to a validated vendor shortlist by systematically generating queries, collecting data, scoring options, and refining your search based on evidence.

Common mistakes and red flags

These pitfalls are common because they often feel like shortcuts, but they systematically compromise the quality of your discovery process.

Anchoring on the First Result: It creates bias and halts further exploration. Fix it by committing to testing your full query set before evaluating any single vendor.
Using Jargon-Heavy Queries: This may exclude vendors who solve your problem but use different terminology. Fix it by including plain-language, problem-focused phrases in your query set.
Skipping the Scoring Matrix: It leads to emotional or hype-driven decisions. Fix it by defining and applying objective criteria before you fall in love with a vendor's website.
Ignoring "Alternative To" Searches: You miss valuable competitive context and potential disruptors. Fix it by always including queries like "software similar to [Market Leader]" and "[Market Leader] alternatives".
Not Checking for GDPR/Compliance Early: It wastes time on vendors you can't legally use. Fix it by making compliance a mandatory filter in your initial scoring criteria.
Neglecting Long-Tail Keywords: You only find broad, expensive solutions. Fix it by including specific, multi-word phrases that describe your exact use case.
Confusing Marketing with Capability: It leads to purchasing a well-branded but functionally weak tool. Fix it by verifying features through trials, demos, and third-party reviews, not just sales copy.
Forgetting Internal Stakeholders: The chosen tool may not address user needs, causing low adoption. Fix it by involving key team members in defining requirements and reviewing the shortlist.

In short: The most common errors involve cutting corners on query variety, objective scoring, and compliance checks—each has a simple, procedural fix.

Tools and resources

Choosing the right support tool can streamline the experiment, but the wrong one can add unnecessary complexity.

Spreadsheet Software (e.g., Excel, Google Sheets): The fundamental tool for logging vendors, creating scoring matrices, and analyzing results. Use it from day one to maintain a single source of truth.
B2B Software Marketplaces: Platforms that aggregate vendor data. They address the pain of fragmented searches by offering centralized databases, filters, and comparison tables. Use them for the initial discovery phase.
Note-Taking/Project Management Apps: Tools to capture brainstorming ideas, meeting notes, and initial requirements. They solve the problem of losing track of early-stage thoughts and decision rationales.
Review and Comparison Sites: Third-party sites offering user reviews and feature comparisons. They help validate vendor claims and uncover strengths/weaknesses not evident on sales pages. Use them during the scoring phase.
AI-Powered Search Assistants: Tools that can interpret natural language queries and suggest related terms or providers. They address the challenge of query exhaustion by helping to brainstorm new search angles.
Data Visualization Tools: Simple charting functions within spreadsheets. They solve the problem of interpreting complex scoring data by making trends and leader gaps visually clear during analysis.

In short: Effective tools for this experiment help you capture data systematically, search efficiently, score objectively, and analyze trends clearly.

How Bilarna can help

Finding and comparing truly relevant, trustworthy software providers is a time-consuming and uncertain process for any team.

Bilarna is an AI-powered B2B marketplace designed to support a Query Fan Out Experiment directly. Instead of juggling multiple browser tabs and spreadsheets, you can enter your core requirement and related query terms into a single platform. Our system simultaneously fans out these searches across our verified provider database, presenting consolidated, comparable results.

This approach reduces manual effort and mitigates discovery bias. Our AI-powered matching goes beyond simple keyword matching to understand the intent behind your queries. Furthermore, our verified provider programme means each company listed has undergone checks, giving you greater confidence in the legitimacy of your discovered options.

Frequently asked questions

Q: How much time does a proper Query Fan Out Experiment take?

For a standard software procurement, a focused experiment can take 4-8 hours of active work spread over a week. The time investment is front-loaded but saves dozens of hours later in demos, negotiations, and potentially correcting a poor choice. The next step is to block a half-day on your calendar to complete Steps 1-4.

Q: Isn't this overkill for a simple, low-cost tool purchase?

The scale of the experiment should match the risk of a wrong decision. For a low-cost, non-critical tool, a simplified version with 3 queries and a basic checklist is sufficient. The core principle—testing multiple search angles—applies at any scale to avoid the trap of the first result.

Q: How do I know my initial query set is good enough?

A good query set yields a diverse mix of vendor names (some familiar, some new) and covers different aspects of your problem. A quick verification method: if your first 3 queries all return the same 5 vendors in the same order, your queries are too similar. You need to add more specific or alternative phrasing.

Q: What if the experiment reveals that no vendor fits our needs perfectly?

This is a valuable, common outcome. It means you have three clear next steps: 1) Re-prioritize your requirements, 2) Investigate if a combination of simpler tools could work, or 3) Consider custom development. The experiment has succeeded by preventing you from buying an inadequate solution.

Q: How do we handle conflicting scores from different team members?

Disagreement often highlights unclear criteria or differing priorities. The fix is to return to your scoring matrix. Discuss and agree on the weight of each category first. Then, score vendors individually before averaging the scores. This makes the debate about the criteria, not the vendors.

Q: Can this method be used for services (like agencies or consultants) as well as software?

Yes, the process is identical and often more critical. Service procurement is less standardized. You would fan out queries based on specialty, industry expertise, project methodology, and location. The scoring criteria would shift to emphasize case studies, client references, and team profiles over feature lists.