Executive Summary
Synthetic data is a type of artificial data generated through algorithms, models, or simulations. It isn’t collected directly from the real world. NIST (the National Institute of Standards and Technology) defines synthetic data generation as the process of creating artificial data that carries some of the statistical properties of source data. AWS describes synthetic data as data produced algorithmically rather than observed from real-world measurements.
In the context of B2B marketing and decision simulation, the value of synthetic data goes beyond technical data production. This isn’t just about generating a dataset.
The real point is modeling which questions decision-makers might ask, which criteria they might use to evaluate answers, and which brands might come out ahead in AI answers — and why.
That’s why, in B2B decision processes, synthetic data isn’t used to replicate customer behavior one-to-one — it’s used to simulate decision questions, evaluation criteria, and AI visibility gaps.
This distinction matters. B2B purchasing processes don’t move in a straight line. A company researching a new supplier, solution partner, or service provider doesn’t just look at product features. Signals like risk, trust, references, sector experience, decision-maker expectations, technical competence, pricing logic, and corporate visibility all get weighed together. Synthetic data helps test that complex decision environment in a controlled way.
What Does Synthetic Data Actually Mean?
Synthetic data is data not taken directly from a real person, customer, or transaction — it’s generated using certain assumptions, source information, statistical structures, or AI models.
In traditional use cases, this data is used to train machine learning models, test software systems, anonymize datasets that carry privacy risk, or run analysis in scenarios where real data is missing. The definitions from NIST and AWS support this technical framework.
In the approach behind the Recro insight model, the logic of using synthetic data changes on the customer research and B2B decision simulation side. The goal here isn’t just generating artificial data. The goal is building a controlled simulation space that represents the actual decision environment.
In this space, synthetic data can take the form of a synthetic customer persona, a synthetic purchasing scenario, a synthetic evaluation criterion, or a synthetic decision dialogue. For example, it can simulate what questions a hospital procurement manager might ask when researching call center services, how a fleet manager might evaluate a sustainability-focused leasing solution, or which brands a B2B technology director might consider when building a vendor shortlist.
The data here isn’t a real customer’s literal answer. But built correctly, it makes visible the questions, criteria, and hesitations that could actually come up in a real decision process.
Why Has It Become Important in B2B Decision Processes?
B2B purchasing behavior isn’t shaped only by Google searches, website visits, and sales meetings anymore. Decision-makers are increasingly asking questions directly to AI tools like ChatGPT, Gemini, and Perplexity. These questions usually look very different from classic keywords.
Instead of typing “best call center company,” a decision-maker might ask:
“Which reliable companies offer KVKK-compliant call center services in the healthcare sector?”
Or:
“What should I look for in sustainability reporting when choosing a corporate fleet leasing company?”
These queries create a far more complex decision space than classic SEO logic. AI doesn’t just evaluate page titles — it weighs a brand’s digital signals, content consistency, sector context, how it’s referenced across sources, and which questions it answers clearly, all together.
This is exactly where synthetic data gains strategic value. Collecting large-scale, continuous, decision-moment-adjacent data from real customers is hard. B2B decision-makers are a busy, hard-to-reach group with limited availability for research processes. On the B2B research side, synthetic data’s potential is discussed specifically in terms of scale, speed, and the ability to model hard-to-reach decision-maker profiles.
That’s why synthetic data isn’t a shortcut that replaces research in B2B. Used correctly, it provides a layer of preliminary analysis, scenario testing, and visibility auditing aimed at understanding decision-maker behavior.
How Is Synthetic Data Used in B2B Decision Simulations?
In B2B decision simulations, synthetic data can be used across four core layers.
1. Defining the Decision-Maker Persona
The first layer is the decision-maker profile. This profile isn’t just a demographic persona. In a B2B context, a persona needs to be defined alongside role, sector, budget responsibility, purchasing risk, decision authority, technical knowledge level, and internal stakeholder pressures.
“General manager” alone isn’t a sufficient persona, for example. A more accurate definition looks like:
“A deputy general manager at a mid-sized healthcare group evaluating outsourced call center services in terms of patient experience, operational efficiency, and KVKK compliance.”
This definition is more powerful, because it determines which questions the simulation will generate.
2. Generating Purchase-Intent Questions
The second layer is the questions a decision-maker might put to AI. In a B2B purchasing process, a valuable question isn’t a general-information question. A valuable question carries decision intent.
Weak question:
“What is a call center?”
Strong decision question:
“What criteria should be considered when getting call center services for patient appointment management in the healthcare sector?”
Even stronger question:
“How do you compare call center companies in Turkey that fit the healthcare sector, are KVKK-compliant, and are strong in patient experience management?”
This is where synthetic data helps generate hundreds of possible question sets for different decision-maker types. These questions are the first step in testing whether a brand is visible in these contexts.
3. Mapping Evaluation Criteria
The third layer is decision criteria. A B2B customer doesn’t just ask “who exists?” They operate on deeper criteria: “who can I trust?”, “which company understands my sector?”, “which company reduces my risk?”, “which company offers a value proposition I can present to my board?”
These criteria usually cluster around:
- Sector expertise
- Reference credibility
- Operational competence
- Regulatory compliance
- Scalability
- Reporting and transparency
- Total cost of ownership
- Clarity of corporate communication
A synthetic decision simulation shows which order and context these criteria surface in. That reveals not just whether a brand is visible, but on what decision reasoning it’s visible.
4. Identifying AI Visibility Gaps
The fourth layer is the most critical area from Recro’s perspective: the AI visibility gap.
An AI visibility gap is a lack of digital signal that prevents a brand from being recommended, mentioned, or positioned as a trustworthy option on a decision-maker’s questions.
A brand can be strong. It can have good customers, good operations, a good product. But if it doesn’t show up in AI answers, the reason is usually a lack of digital signal.
For example, a brand might have published content about sustainability on its website. But if that content doesn’t answer the exact question a decision-maker might ask —
“What criteria should be considered around carbon footprint reporting and contribution to sustainability goals when choosing a fleet leasing company?” — the signal stays weak.
In this example, the problem isn’t a lack of content — it’s a missing link between the decision question and the content.
Synthetic data makes this gap visible. Which questions is the brand invisible on? On which criteria are competitors mentioned more strongly? Which topics aren’t connected to the brand’s decision context? Which content exists technically but doesn’t produce a meaningful signal in AI answers?
The answers to these questions usually don’t show up in classic SEO reports. Because the goal here isn’t just getting traffic — it’s being citable at the moment of decision.
What Does Synthetic Data Deliver?
Synthetic data creates five core forms of value in B2B decision simulations.
First, it provides speed. While real customer research can take weeks or months, synthetic simulation can multiply decision questions in a short amount of time.
Second, it provides scale. Instead of a single persona, many decision scenarios can be generated across different sectors, roles, priorities, and risk profiles.
Third, it surfaces blind spots. The focus shifts to the questions a decision-maker asks from the outside, not the topics a company already knows about itself.
Fourth, it makes content strategy concrete. Instead of a generic recommendation like “let’s produce more content,” it identifies exactly which decision question needs to be answered with which content.
Fifth, it makes AI visibility measurable. A brand gets tested in decision context not just in search engines, but in AI answers too.
Limits: How Should Synthetic Data Be Read?
Synthetic data is powerful — but it shouldn’t be read as absolute truth. B2B International, discussing synthetic data’s potential in B2B research, emphasizes that a skeptical eye still matters, and that it shouldn’t be misread as a full replacement for real human responses.
That’s why a synthetic decision simulation doesn’t produce the conclusion “the customer will definitely behave this way.” The more accurate reading is:
“This decision profile, in this context, may tend to ask these kinds of questions and evaluate them against these criteria.”
That distinction matters. A good simulation doesn’t produce an absolute answer — it helps you read the decision environment better.
In Recro’s approach, synthetic data should be treated not as a replacement for real customer research, but as an analysis layer that multiplies decision questions and identifies digital visibility gaps.
How Does the Recro Insight Model Use This Approach?
The Recro Insight Model simulates the critical purchasing questions B2B decision-makers might ask AI tools. The core goal of these simulations is understanding under what conditions a brand gets recommended in AI answers, under what conditions it doesn’t show up, and which competitors pull ahead — and why.
The model’s logic rests on three questions:
First: what questions is the decision-maker asking?
Second: which brands, sources, and criteria does AI use to answer those questions?
Third: where do the digital signal gaps that block a brand from being recommended actually form?
This approach gives B2B marketing teams a clearer action space. Because the problem isn’t just missing content. The problem is often content that isn’t tied to a decision question, unclear sector expertise, weak reference signals, insufficient explanation of methodology, or a scattered digital presence AI simply can’t connect the dots on.
That’s why Recro treats synthetic data not as a technical data-generation tool, but as a strategic simulation ground that models the B2B decision environment.
Conclusion: Synthetic Data Is a New Decision Laboratory for B2B
For B2B companies, competition no longer plays out only on a website, in a sales meeting, or on a Google results page. It’s also playing out in the questions decision-makers ask AI.
Being visible in this new environment doesn’t just mean being findable. A brand needs to be mentioned on the right decision question, with the right criteria, in the right context, and as a trustworthy option.
That’s why synthetic data functions as a new decision laboratory in B2B marketing. It doesn’t produce a one-to-one copy of a real customer. It does something more valuable: it makes decision questions, evaluation logic, and visibility gaps systematically testable.
That’s where the real value of synthetic data sits in B2B decision processes.