In the world of private equity, reliable data is the cornerstone of sound investment decisions. A robust market research methodology can be a critical tool for gathering actionable insights during the due diligence research process. However, the complexities of financial data—such as varying accounting practices alongside topic and industry-specific nuances—demand careful planning to ensure the accuracy and relevance of your findings.
By prioritizing clarity, context, and rigorous validation, you can transform survey findings into precise, high-impact insights that strengthen your deal conviction and empower confident, informed decision-making.
To ensure your surveys yield actionable data and insights that support informed investment decisions while minimizing risk, consider these best practices as a starting point, keeping in mind there are many other factors to explore when designing research strategy and surveys themselves:
1. Understand the Respondent’s Context
Start by framing your survey with questions that set the stage for the respondent; focus on clarity and minimize misinterpretation. This approach helps respondents provide accurate data for their specific situation and allows for more reliable comparisons between answers.
For instance, if you are considering an investment in a payroll software company, begin by asking which billing models they use before jumping to a direct question of how much they pay.
Here’s a simplified example to illustrate how to apply this best practice:
- How is your organization’s payroll software billed?
- Per employee
- Per transaction
- Other
- I don’t know
- How much does your organization spend annually on payroll software?
- <$1K
- $1K-$10K
- $10K to $50K
- More than $50K
- Other
- I don’t know
This approach improves accuracy by linking the respondent’s spending to their current billing model, and ensures responses can be analyzed consistently on an apples-to-apple basis. In real-world data collection, there are many additional questions you will want to ask your respondents to fully understand and contextualize their spend data.
2. Structure a Logical Question Flow
Craft a question flow that engages respondents while gathering actionable data. Correctly using a series of questions that funnel down to a primary ask will effectively balance the level of detail needed to get accurate data with a level of ease that reduces cognitive load and improves data accuracy.
For example, when exploring payroll costs, qualify the respondent by asking:
- What are your responsibilities at your employer?
- Would you be a decision-maker if your company were to purchase payroll software?
You can then gather context to frame the primary ask:
- How many employees are on your payroll?
- How many payroll solutions does your company use?
- How often is each bundle of products used?
- What are your annual spend expectations for payroll software?
All of these questions combined provide the full story regarding your primary question:
- How did your company select its primary payroll provider?
This funneling technique provides clarity, ensures logical progression, and captures detailed financial insights without overwhelming respondents. Ultimately, this approach delivers an accurate and comprehensive view of the market while directly addressing the primary question.
3. Account for Memory Limitations
Respondents struggle with recalling precise figures after long periods—especially in the moment. To mitigate this challenge, design questions around recent, specific timeframes using easy-to-recall techniques and place memory-intensive questions early in the survey to minimize fatigue.
Encourage respondents to use their best estimates and provide broad, intuitive ranges to make answering easier and more accurate:
- What was your department’s annual budget for 2024? Please use your best estimate.
- Less than $50,000
- $50,001 to $250,000
- $250,001 to $1M
- $1.1M to $5M
- $5.1M to $25M
- Greater than $25M
Additionally, ensure quantitative questions are framed in a way that allows respondents to recall and provide accurate information without unnecessary cognitive strain:
- How has your company’s number of full-time employees changed over the past 12 months? Please use your best estimate.
- Decreased significantly (Greater than 25% decrease)
- Decreased moderately (11% to 25% decrease)
- Decreased slightly (3% to 10% decrease)
- Remained approximately the same (+/- 2% change)
- Increased slightly (3% to 10% increase)
- Increased moderately (11% to 25% increase)
- Increased significantly (Greater than 25% increase)
This approach makes it easy for respondents to answer and ensures that insights are reliable and inform critical investment decisions, from evaluating growth potential to assessing market dynamics with confidence.
4. Avoid Artificial Constraints
Answer ranges should accommodate all scenarios to avoid bias and missing data. For example, when asking about startup costs, ensure the ranges you provide as answer options start low enough (i.e., in the example below: $0) and go high enough to capture accurate data from the entire potential survey population. Determine how the ranges will play into your analysis to ensure the chosen buckets are each the appropriate size. Finally, consider whether it makes sense to include a variety of ranges and allow for “Other” or “I don’t know” responses to maintain data integrity:
- How much did your company spend on third-party contractors in 2024? Please use your best estimate.
- $0 – $999
- $1,000 to $50,000
- $50,001 to $250,000
- $250,001 to $1M
- $1.1M to $5M
- $6M to $25M
- More than $25M
- I don’t know
- Which of the following cloud service providers have you ever used? Select all that apply.
- Amazon (AWS)
- Microsoft (Azure)
- Google (GCP)
- Oracle
- Other (please specify):
- We don’t use cloud service providers
By including options like “I don’t know” and “Other,” you give respondents flexibility to provide high-fidelity data, without forcing them to choose an incomplete or inaccurate answer.
5. Validate and Investigate Outliers
Outliers in survey data often emerge due to unique circumstances, respondent errors, or misunderstandings. To ensure data accuracy, cross-check unexpected results against industry benchmarks and, when possible, follow up with respondents to confirm their validity.
Validated outliers can uncover valuable insights, such as potential value-creation opportunities or underlying market risks. However, it’s important to approach these findings with curiosity rather than assumption, avoiding premature conclusions about respondent errors or inaccuracies.
For example, imagine a survey on enterprise software adoption where a mid-sized company reports a budget significantly higher than its peers. While this could indicate a simple error, further investigation might reveal the company has recently undergone a merger and is allocating substantial resources to IT integration.
This discovery may not only validate the data point but also highlight a broader trend–such as increased IT spending following mergers–that could shape strategic recommendations (e.g., targeting specific market segments) or prompt follow-up studies to further explore the trend.
Thoroughly stress-testing outliers may uncover unique insights that might otherwise be dismissed, adding depth and context to the overall findings.
The Bottom Line
Accurate and well-structured survey design is essential to providing conviction in due diligence research decisions. While the principles discussed—such as prioritizing clarity, aligning questions with respondent context, and rigorously validating data—are foundational, survey creation is a highly complex process. Ensuring data integrity requires careful consideration of numerous other factors, from questioning sequencing to avoiding bias to managing respondent fatigue.
These examples represent just a fraction of the elements involved in crafting surveys that produce reliable, actionable insights. By adopting a disciplined and sophisticated approach, your team can extract valuable data to evaluate risk-adjusted returns, uncover growth opportunities, and drive portfolio value creation with confidence and precision.
