Executive Summary: Synthetic data and digital twins are enabling B2B tech research teams to overcome the limitations of hard-to-reach audiences, high costs and limited sample sizes. By extending the value of human data, these approaches enable researchers to scale insights, simulate decision-making scenarios and explore new questions more efficiently. Together, they support faster, more flexible and actionable research, without replacing the critical role of real respondents.
Why is B2B technology research becoming harder to scale and sustain?
B2B technology research has always depended on reaching the right people. But today, that’s becoming increasingly difficult.
The audiences that matter most (e.g., senior Information Technology Decision Makers (ITDMs), cloud architects, specialized developers) are not only hard to find, they’re expensive and time constrained. It’s not uncommon for highly targeted B2B recruits to cost hundreds, even thousands, per interview. And even with that investment, research teams can struggle to secure enough qualified participants to support deeper analysis.
At the same time, expectations are rising. Research is being asked to move faster, go deeper and deliver more actionable insights, often within tighter timelines and budgets.
Traditional research remains essential for technology brands. But it’s being stretched.
And that’s where synthetic data and digital twins are beginning to play a meaningful, complementary role.
How synthetic data extend the value of human insights
Synthetic data are artificially generated information designed to replicate the structure and statistical properties of real-world data—without being tied to any individual respondent.
Synthetic data are created using models trained on real datasets and are best understood as a complement to human insight, not a replacement.
That distinction matters. The goal isn’t to move away from real respondents, it’s to extend their impact by augmenting B2B tech research samples with synthetic data. When used correctly, synthetic data allow researchers to fill gaps, explore scenarios and scale insights beyond the limits of traditional fieldwork.
Synthetic data are most powerful when they extend, not replace human insight, enabling researchers to scale learning while preserving the nuance and impact that only real human respondents provide.
Use case 1: How can synthetic data solve the “N of 50” problem in B2B research?
Consider a common B2B research scenario.
As part of a larger recruit, you’ve identified one segment of 40-50 highly qualified respondents that are particularly important to you—perhaps IT decision makers at enterprise organizations using a specific cloud platform. The overall data are rich, but the segment is limited. You want to explore results, compare behaviors across cohorts or dig deeper into patterns, but the sample size constrains what you can confidently say.
Traditionally, the answer would be more fieldwork—more time, more cost and no guarantee of success.
Synthetic data offer another path.
By leveraging patterns within your existing dataset, synthetic approaches can augment your sample, expanding those 50 respondents into a larger, statistically representative dataset building off the strength of the rest of your data. This enables:
- More stable directional insights
- Deeper segmentation across smaller B2B cohorts
- Greater confidence in emerging behavioral patterns
All without requiring a proportional increase in recruitment cost or timeline.
This type of sample survey augmentation is particularly valuable in B2B environments, where scaling through traditional recruitment alone is often impractical.
Use case 2: What are digital twins and how do they improve decision modeling?
A second emerging application is digital twins.
Imagine you’ve completed a quantitative study and want to explore “what if” scenarios—how different audiences might respond to a new feature, pricing change or a shift in messaging. Running another wave of research could take weeks.
Digital twins offer a way to explore these questions more dynamically.
Using observed response patterns, digital twins create modeled presentations of survey respondents that can be used to simulate reactions, test ideas and explore scenarios in near real time.
Today, their strongest application is not in replacing primary research, but in:
- Summarizing and synthesizing existing research findings
- Exploring directional reactions to new ideas and products
- Enabling more iterative, interactive insight development workflows
In this way, they help extend the life and value of existing research.
Digital twins don’t replace research, they extend it, enabling faster scenario testing and more dynamic decision-making without having to restart the research process from scratch.
What are the best practices for using synthetic data effectively in B2B research?
Through multiple and extensive pilots Escalent Group—Escalent, C Space, Hall & Partners—has conducted, a clear pattern has emerged. Synthetic data deliver the most value when:
• They are anchored in strong, high-quality human data.
• They are used for directional insight and exploration, not precise measurement.
• They are applied to specific, well-defined use cases, rather than as a general replacement.
• They remain time-relevant, reflecting current conditions and trends.
• They incorporate trusted external sources to maintain human-like realism.
• They are supported by an always-on feedback loop for continuous model improvement.
Synthetic data are particularly effective for rapid testing, simulation and expanding niche samples, especially where access, cost or time create constraints.
At the same time, it is not a shortcut. It requires thoughtful design, validation and a clear understanding of where it adds value, and where it does not.
A more flexible, scalable future for B2B research with synthetic data
The future of B2B research isn’t about replacing respondents with replicas. It’s about recognizing that the demands on research have evolved and adapting accordingly.
Human insight remains foundational. It provides the depth, nuance and real-world grounding that no model can replicate.
Synthetic data in market research adds a new layer of capability, enabling teams to scale, iterate and explore in ways that were previously constrained by time, cost and access. Together, they create a more flexible, resilient research model—one that can better keep pace with the complexity of modern B2B decision-making.
In that sense, the shift isn’t from respondents to replicas.
It’s from working within limits… to thoughtfully expanding beyond them.
What’s next: How should you apply synthetic data to your B2B research strategy?
At Escalent Group, we’ve spent the past year running multiple pilots across synthetic data, sample augmentation and digital win applications—testing not just what these approaches can do, but where they truly add value in real-world B2B research.
That experience has shaped a clear POV: synthetic data works best when they’re purpose-built, grounded in strong human insight and applied to the right problems. Not as a shortcut, but as a strategic extension of traditional research methodologies.
If you’re navigating hard-to-reach audiences, rising costs or increasing pressure to deliver faster insights, we’d welcome the opportunity to explore how synthetic approaches can complement your current research strategy.
Because the question isn’t whether to use synthetic data. It’s where—and how—to use it effectively.
