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Revenue hypothesis engine for B2B companies
Find the revenue patterns hiding inside your customer data.
Upload your website and customer revenue file. AutoResearchRevenue identifies your highest-value customer patterns and turns them into testable GTM hypotheses.
You can upload a larger file, but the free diagnostic only processes the first 50 rows.
Sample diagnostic
High-value cluster
Mid-market healthcare
Signal strength
68% sample revenue
Hypothesis
Messaging may be under-positioned for regulated buyers.
Fact 34 of 50 rows tagged healthcare or compliance-adjacent.
Inference Regulated buyers may have higher willingness to pay.
Uncertainty Sample size is limited and may be biased.
The problem
Most GTM decisions are made from anecdotes, not revenue evidence.
Teams often guess their ICP, messaging, and next campaign based on intuition, a few memorable deals, or the loudest recent customer. The useful patterns are usually already present in the customer base, but they are scattered across revenue data, website positioning, sales notes, segments, sources, products purchased, and churn history.
AutoResearchRevenue does not claim to produce definitive strategy. It helps surface patterns, contradictions, missing data, and testable hypotheses so your next GTM move is easier to evaluate.
How it works
From customer data to testable revenue hypotheses.
The free diagnostic is intentionally limited: we review the first 50 rows so you can see whether the pattern-discovery approach is useful before sharing more data.
Upload customer revenue data
CSV, XLSX, or XLS files work best when they include customers, domains, revenue, segment, source, churn, and product or service purchased.
We analyze the first 50 rows for free
Larger files are allowed, but rows after 50 are not included in the free review.
Receive ranked hypotheses
You get a concise diagnostic with evidence, inferences, uncertainty, and recommended GTM experiments to test next.
What the diagnostic looks for
Pattern discovery, not magic strategy.
Each finding should separate what the uploaded sample actually shows from what might be true and what still needs validation.
Best customer clusters
Which segments, industries, deal sizes, sources, or use cases appear overrepresented among high-value customers.
Revenue concentration
Whether a small number of customer types or accounts drive a large share of sample revenue.
ICP hypotheses
Evidence-backed guesses about who may be most valuable, easiest to win, or most likely to expand.
Website-positioning mismatch
Where the website appears to target one audience while the revenue sample suggests another.
Expansion opportunities
Customer groups that may support upsell, cross-sell, vertical plays, partner channels, or account expansion tests.
Outbound test ideas
Concrete 50 to 200 account experiments based on observed clusters, lookalike patterns, and buying signals.
Missing data warnings
Fields that are absent, messy, biased, or too thin to support a strong conclusion.
Example output
A good diagnostic makes the next test obvious.
The output is written like a decision memo, not a generic report. It distinguishes facts from inferences and gives you a specific experiment to run.
Sample hypothesis
"Your website positions around startups, but 68% of revenue in the uploaded sample comes from mid-market healthcare and compliance-heavy customers. Hypothesis: the company may be under-positioning for regulated mid-market buyers. Recommended test: create one healthcare/compliance-specific landing page and run a 100-account outbound test."
Fact: 68% of sample revenue came from healthcare and compliance-heavy customers.
Inference: These buyers may value trust, security, and compliance more than startup speed.
Uncertainty: The sample may not represent the full customer base.
Test: Build one vertical page and run a controlled outbound test.
Free diagnostic limitation
Free diagnostic: first 50 rows only.
You may upload a larger file, but rows after 50 are not included in the free review. Larger files can still be useful if you later request a paid deeper diagnostic or manual review.
Upload your filePrivacy and trust
Your data stays under your control.
Revenue data is sensitive. The upload flow is designed to ask for only what is needed for the diagnostic.
We only need company-level customer data, not personal contact data.
You can remove names, emails, phone numbers, and other personal data before uploading.
The free diagnostic reviews only the first 50 rows.
Your data is used only to prepare your diagnostic unless a separate agreement says otherwise.
Do not upload data you do not have permission to share.
Start here
Get a free 50-row revenue hypothesis diagnostic.
Submit your website and upload a customer revenue file. The free diagnostic reviews the first 50 rows and returns a ranked set of evidence-based GTM hypotheses.
Upload guidance
- CSV, XLSX, and XLS files are accepted.
- You can upload a larger file, but the free diagnostic only processes the first 50 rows.
- Files over 20 MB may require manual review depending on the form backend provider.
- Do not upload personal data unless it is necessary and you have permission.
FAQ
Questions before you upload.
What file format should I upload?
CSV, XLSX, or XLS. CSV is usually easiest for a first diagnostic because it is simple to review and validate.
What columns work best?
Helpful columns include customer name, company domain, revenue, segment, industry, source, acquisition channel, churn status, renewal status, product or service purchased, employee count, region, and notes.
Do you need personal data?
No. Company-level customer data is preferred. You can remove names, emails, phone numbers, and other personal data before uploading.
What happens if my file has more than 50 rows?
You may still upload it, but the free diagnostic only reviews the first 50 rows. Rows after 50 are not included unless a separate deeper review is agreed.
Is this a strategy recommendation?
No. It is a hypothesis diagnostic. The output should help you decide what to test next, not replace strategic judgment or customer research.
How accurate is the diagnostic?
Accuracy depends on the quality and representativeness of the uploaded data. The diagnostic should call out uncertainty, missing fields, and weak evidence instead of pretending the data proves more than it does.
What happens after I submit?
Your submission is routed through the configured form backend. Once reviewed, you receive a concise diagnostic with observed patterns, ranked hypotheses, uncertainty notes, and suggested GTM experiments.
Start with evidence
Get a free 50-row revenue hypothesis diagnostic.
Upload your website and customer revenue data. We will help identify patterns worth testing next.
Get a free 50-row diagnostic