The startup due diligence checklist founders actually need.

Startup due diligence checklist: the framework, the common mistakes, and the evidence that separates a defensible answer from a confident one.

Due diligence is the process of testing whether the assumptions behind a decision are actually true. Investors run it on founders. Founders should run it on their own ideas. The structure is the same: identify the highest-risk assumptions, find the best available evidence for each one, and reach a conclusion about whether the assumption holds.

Most founders skip this step entirely, or do a partial version that covers market size and competitors while ignoring the dimensions that actually kill startups. According to CB Insights' 2023 post-mortem analysis of 300 failed startups, 42 percent failed due to no market need, 19 percent due to being outcompeted, and 18 percent due to pricing and cost issues. All three causes are detectable through structured diligence before building begins.

This checklist covers the five dimensions that matter most: market, customer, competitive, economic, and team.

Start with market definition. What is the specific segment you are targeting, not the broad category? "HR software" is a category. "Scheduling tools for healthcare staffing agencies with 50 to 200 employees in the United States" is a segment. The more specific your definition, the more useful your market analysis will be.

For your defined segment, answer four questions. First: how many buyers exist? Use the US Census Bureau, industry association membership data, or LinkedIn company counts with appropriate filters. Second: what do they currently spend on solving this problem? Look at competitor pricing pages, job posting salaries for roles your product would replace, and consultant hourly rates for the work your product would automate. Third: is the segment growing, flat, or shrinking? Find a trend data source: Statista, IBISWorld, or the relevant industry association's annual report. Fourth: what is the minimum penetration needed to reach a viable business, and is that penetration realistic given your go-to-market constraints?

A market dimension that produces a specific segment size, a specific current spend figure, a sourced growth rate, and a required penetration percentage is far more useful for decision-making than a headline TAM from a research firm.

The customer dimension tests whether the buyer you have in mind is real, reachable, and capable of making a purchase decision for your product.

For each buyer persona, document the following. Their job title, level, and company size. The budget they control directly, not the budget their department has. The procurement process they navigate for a software purchase of the size you intend to charge. The one or two metrics their manager uses to evaluate their performance, because your product needs to improve one of those metrics if it is going to survive an internal budget review.

Then test those assumptions against five actual conversations with people who match the persona. Ask them to walk you through the last time they bought a software tool in the price range you are targeting. Who else was involved? How long did it take from first demo to signed contract? What almost caused the deal to fall through?

According to Gartner's 2023 B2B Buying Survey, the average B2B software purchase involves 11 stakeholders and takes 10.2 months from initial consideration to contract signature for deals over $50,000 annually. For deals under $10,000 annually, the median buying time drops to 2.3 months, but still involves an average of 4 stakeholders. Understanding where your price point sits on that curve determines your cash flow projections for the first 18 months.

The competitive dimension is not about who your direct competitors are. It is about what the buyer is currently doing to solve the problem your product addresses, and what it would cost them to keep doing that instead of switching to you.

Every buyer has a current solution. It might be a spreadsheet, a consultant, a legacy tool, or a manual process. Identify the current solution for at least five representative buyers in your segment. Then calculate the total cost of that current solution: software license cost, staff time spent on manual steps, and cost of errors or failures.

Your product needs to either cost less than the current solution, or deliver enough incremental value that the cost difference is justified and the switching cost is bearable. If neither is true, you do not have a competitive offer even if your product is technically superior.

Research what it would take for a buyer to switch: integration work with existing systems, training time for staff, migration of historical data, and political capital spent convincing stakeholders. A switching cost above $25,000 (all-in) for a product that costs $10,000 per year means a buyer needs more than two years to break even on the switch. Most buyers will not make that calculation in your favor unless the pain of staying is acute.

The economic dimension tests whether the business model works at the price the market will bear. This requires building a unit economics model before you have revenue.

The key metrics are customer acquisition cost (CAC), lifetime value (LTV), and payback period. For a pre-revenue startup, these are estimates, but they should be grounded in comparable benchmarks rather than optimistic assumptions.

For CAC, estimate based on your planned go-to-market motion. If you plan a direct sales motion with an account executive closing deals, benchmark CAC against similar B2B SaaS companies. According to OpenView Venture Partners' 2023 SaaS Benchmarks report, the median CAC for B2B SaaS companies with an average contract value between $5,000 and $25,000 per year is $3,200 per new customer for a sales-assisted motion. For a purely self-serve product at the same price point, median CAC is $940.

For LTV, estimate based on your planned price and a realistic churn assumption. The average annual churn rate for B2B SaaS companies serving small and mid-market customers was 14 percent in 2023, according to the same OpenView report. At 14 percent annual churn, the average customer stays for approximately 6 years, and lifetime value at $8,400 annual contract value is approximately $50,400. A CAC of $3,200 against an LTV of $50,400 is a healthy ratio. A CAC of $8,000 against an LTV of $16,000 is not viable.

The team dimension is the one most founders either skip or evaluate last. It deserves to be evaluated early, because team gaps are the hardest to fix quickly and the most likely to cause execution failure even when the market, customer, competitive, and economic dimensions all check out.

For each of the three to five highest-risk execution requirements in your plan, answer honestly: who on the current team has done this before, not in a similar context, but in a directly comparable one? If the plan requires building enterprise sales from scratch and nobody on the founding team has done enterprise sales before, that is a material execution risk. The gap itself is not disqualifying. Unacknowledged gaps are.

Document the two or three hires that are critical to executing the plan in the first 12 months. For each one, estimate the time to hire (typically three to five months for senior technical or sales roles in 2024 according to LinkedIn's Global Talent Trends report), the cost, and the risk if the role goes unfilled. A plan that depends on a hire you have not started recruiting for is not a 12-month plan.

Work through each dimension in order. For each dimension, write down the three highest-risk assumptions, the best available evidence for each assumption, and your confidence level in that evidence. When you finish, you will have a structured picture of where your diligence is strong, where it is weak, and where you need more data before making a build decision.

The purpose is not to produce a passing grade. It is to identify the specific assumptions most likely to be wrong, so you can test them before the market tests them for you. Verdikt's research reports run this exact structure: market, customer, competitive, and economic dimensions, each with named assumptions, cited evidence, and a stated verdict on whether the assumption holds.