Customer surveys for commercial due diligence
Commercial due diligence surveys put quantitative evidence behind an investment thesis: structured customer research that measures retention risk, competitive position, pricing power, and market outlook while the deal clock is running. Razberri builds, pretests, fields, and analyzes those studies on one platform, with AI personas stress-testing the instrument before a single real respondent sees it.
What is a commercial due diligence survey?
A commercial due diligence survey is fielded to a target company's customers, prospects, and former customers during the diligence window, to quantify the assumptions the investment case rests on. Expert calls explain how a market works. The survey measures how much, covering how likely customers are to stay, what they would pay, and how the target compares with its alternatives.
The two evidence streams are complementary, not competing. Qualitative calls find the questions worth asking: the language of the market, the rumored competitor, the pricing gripe. The survey then answers them at sample sizes calls cannot reach: retention quantified across the base rather than recalled by a dozen friendly references, competitive position measured on the criteria buyers actually weigh, and the risks that could affect the investment sized instead of listed.
What can a diligence survey quantify that customer calls can't?
Calls with a dozen customers surface themes; a survey across the customer base puts numbers behind them. The classic diligence questions (will the customers stay, does the target have pricing power, where is it losing deals) are distribution questions, and answering them takes structured measurement across many respondents, not a handful of conversations.
| Diligence question | Survey methodology | Deliverable |
|---|---|---|
| Will the customer base stay after close? | Retention and switching modules, NPS with key driver analysis | Ranked drivers of renewal and churn risk: what actually moves retention, instead of asking customers to guess |
| How does the target compare with competitors? | Competitive benchmarking with gap analysis | Importance-versus-performance map showing where the target is ahead, behind, and exposed |
| Does the target have pricing power? | Willingness-to-pay modules: Van Westendorp, Gabor-Granger | Acceptable price ranges and pricing headroom, compared across segments |
| Which segments carry the growth thesis? | Segmentation | Respondents grouped by needs, behavior, and spend, with every metric compared across segments |
Study depth flexes with the deal stage. A short, targeted expert pulse tests an assumption or market direction during early thesis validation; a structured market study carries the full diligence (detailed segmentation, fraud checks, and analysis ready for the readout); and a survey with deep-dives uses dynamic questions to dig into the answers worth a closer look.
How does AI persona pretesting de-risk a one-shot diligence survey?
A commercial due diligence survey fields once, inside a deal timeline, with no second chance: a routing defect discovered mid-field is a defect the deal team keeps. Razberri runs AI personas drawn from the target audience through the draft instrument first, so dead branches, leading phrasing, and questions respondents cannot answer are found while a fix still costs nothing.
The personas are one half of pre-launch QA; automated structural checks are the other, catching answer options that overlap or leave gaps, piping variables that were never captured, branches that lead nowhere, and screens no respondent can reach. How the persona side works is on the AI persona pretesting page.
How does Razberri keep diligence data clean?
Every response is scored on completion behavior, internal consistency, speed, duplication, and open-text quality, 30+ automated checks in all. Flagged responses are quarantined as they arrive rather than found in a clean-up pass after fieldwork closes, so the sample the deal team analyzes is the sample it can defend.
That in-field discipline matters because bot fraud can skew results by up to 40% when it goes unfiltered. Razberri also clarifies while fieldwork is open rather than after it closes: when an answer is ambiguous or contradicts an earlier one, a dynamic follow-up fires in the moment and the clarification lands in the same response record, not in a footnote written three weeks later.
Analysis keeps pace with the deal. Key driver analysis, gap analysis, segmentation, and NPS deep dives run against the data the moment responses land, and the deal team can ask for a cut in natural language and get it back charted, weighted, and sourced to the underlying responses. No exports, no analyst queue.
Frequently asked questions
Where does a survey fit alongside expert calls in a diligence?
Run them in sequence. Calls and management sessions generate the hypotheses: what might drive churn, who the real competitor is, where pricing feels soft. The survey then tests those hypotheses quantitatively across customers, prospects, and former customers, so the deck says "measured," not "heard."
Why pretest a diligence survey instead of just reviewing it?
Because review alone provably misses defects: in the classic comparison of pretesting techniques, different methods surfaced genuinely different problems, and no single method was sufficient (Presser & Blair, Sociological Methodology, 1994). On a one-shot deal-timeline study, an AI persona run plus structural checks adds a second and third layer at near-zero cost.
Can a diligence survey reach niche B2B audiences?
Diligence work usually means specialist respondents: IT buyers and technical decision-makers, physicians and payers, engineers and procurement leaders, banking and insurance professionals. Screeners qualify the roles, firmographics, and behaviors that make a respondent worth listening to, so the read comes from the people the thesis is actually about.
What analyses does Razberri run on diligence data?
Four prebuilt methods (key driver analysis, gap analysis, segmentation, and NPS deep dives) run without exports or rebuilt models, and natural-language queries cut live results on demand. Every chart traces back to the underlying responses, which is exactly the auditability an investment-committee readout needs.
Talk to us about your research.
Book a 30-minute walkthrough and see a study built on your own case context: designed, pretested with AI personas, and ready to field.