How Synthetic Data Can Deliver Real Benefits to Professional Services Marketing

Professional services firms operate in data-rich environments—legal outcomes, consulting deliverables, financial models, project analytics. Yet, because of practical realities, marketing teams within these organisations often find themselves data-poor where it matters most.

​​Client work is confidential. Sample sizes are relatively small. Critical moments like major purchase decisions happen infrequently. Valuable information is fragmented, locked up in slide decks and project reports–not in datasets that you can interrogate easily.

Meanwhile, the expectations for data-driven marketing are relentless. Leadership wants evidence-backed strategies and clear ROI metrics. Clients expect relevant, personalised communications.

The standard marketing playbook doesn't fully address these constraints. It often assumes you have the vast customer datasets of a consumer brand or the detailed user analytics of a SaaS business.

Synthetic data can help bridge the gap. It’s not a replacement for client insight or research, but it can provide a way to work effectively with data that exists in small quantities, fragments, or not at all.

What is Synthetic Data?

Synthetic data refers to artificially generated datasets that imitate real-world data in structure and statistical properties. It can include any number of data types, including synthetic CRM data, financial figures, sales pipeline data or survey responses.

Synthetic data isn’t just made up out of thin air. It’s generated by analysing rules, patterns, and relationships in existing data so it can maintain the same distributions, correlations, and trends as the original.

Think of it like upscaling a small image in Photoshop to make it larger. When you upscale an image, the software doesn’t just guess randomly—it fills in the missing details based on what it already knows, ensuring the larger version looks realistic.

How is Synthetic Data Generated?

One method uses generative adversarial networks (GANs)–a type of computer program where two parts (a "generator" and a "discriminator") work together to improve the data. 

The generator tries to create new, realistic-looking data by learning patterns from real data. The discriminator acts like a critic, analysing the generator's output and trying to determine whether it's real or fake. Both keep improving their abilities, going back-and-forth until the generator produces data so realistic that the discriminator struggles to tell it apart from the real thing.

Rule-based simulation is another approach which sets specific rules and patterns for how the data should behave. For example, you might define how often certain events happen, what ranges numbers fall into, or how different pieces of data relate to each other. Once these rules are in place, the system can simulate new data that follows them.

For instance, you could simulate financial transactions by defining rules like:

• "Most purchases are between £10 and £100."

• "People buy more items on weekends."

• "Luxury items are bought less often than everyday goods."

The system can then generate realistic-looking transactions based on these rules.

The result is data that looks and behaves like real-world data but doesn’t contain any actual information from real people or cases. This makes it safe to use for testing, training, or analysis without risking privacy or exposing sensitive information.

Benefits of Using Synthetic Data in B2B Marketing

Access to More Data (at Lower Cost)

Gathering large volumes of real B2B data is time-consuming and expensive, and often impossible for small to medium sized professional services firms. Synthetic data generation sidesteps this by offering a way to produce ample data cheaply and quickly. 

For example, instead of surveying hundreds of corporate clients over the course of many months, you can generate a synthetic dataset in hours. This speeds up analysis and AI training, since models can be trained on thousands of synthetic scenarios without costly data collection. 

One B2B data startup found that for clients like EY, synthetic market research data provided “data where often there was none,” delivered a 95% match to real-world data, and cut research costs.

Bypassing Privacy and Compliance Barriers

Professional services often handle sensitive personal or business data that cannot be used freely due to confidentiality agreements and privacy regulations. Synthetic data offers a privacy-preserving alternative. 

Synthetic data eliminates personally identifiable information (PII) and reduces re-identification risks, making it safer to use than raw data. Some synthetic data generation tools even build in safeguards, such as differential privacy which adds mathematical "noise" to datasets to prevent reverse-engineering of individual identities.

By avoiding use of real personal data, companies can significantly reduce the risk of data breaches when using data to test, train AI, and collaborate with partners.

Handling Rare or Complex Scenarios

B2B markets often have small sample sizes and contain edge cases, like a niche industry segment or an unusual project type, that are hard to study because of limited real examples. Synthetic data can fill these gaps by creating more examples of rare events or customer types are hard to capture in reality. 

Using this data, marketers can simulate “what-if” situations. For example, an architectural practice might simulate how a new market might respond to their services. By designing datasets covering phenomena seldom seen in real-life, synthetic data lets marketing teams war-game strategies for low-frequency events in a risk-free way. This can inform content and messaging and train quick response scenarios.

Diversity and Bias Reduction

Because synthetic data is under our control, we can engineer it to address imbalances and underrepresentation in historical real data. If used thoughtfully and carefully, it can counteract bias and be a step toward more balanced marketing analytics.

For example, you might use synthetic data to create more inclusive customer personas, balancing preferences, locations, job types, and industries to give a more complete picture of the market.

This is an enormous benefit in marketing, where biased data can lead to skewed targeting or messaging. By augmenting real datasets with synthetic entries, we gain the opportunity to better represent all segments and prevent strategies that ignore minority groups. 

Foundation for AI and Automation

Synthetic data can turbo charge marketing automation and AI projects. Marketing increasingly relies on AI for lead scoring, personalisation, chatbots, and campaign optimisation – all of which require training data. By generating abundant synthetic training data–for example, simulated customer interactions or lead behaviours–firms can feed their algorithms without hitting data bottlenecks. 

This is especially useful for professional services firms that might have only a few hundred high-value clients. With synthetic data, they can amplify that to thousands of data points reflecting similar patterns for model training. This lowers the barrier to adopting advanced analytics, since lack of data is no longer a limiting factor. Once you’re up and running, synthetic data can be updated based on new real-world inputs to keep models improving.

Challenges of Using Synthetic Data

When used correctly, synthetic data can offer many advantages. However, it is not a silver bullet. It also introduces a new set of considerations: validating its realism, mitigating biases, and blending it correctly with real-world feedback among them.

B2B marketers should know potential pitfalls to use it responsibly and effectively.

Validity and Accuracy

A fundamental challenge is ensuring that synthetic data is a reliable reflection of reality. Synthetic data by nature is an approximation. The insights derived from it are only as good as the assumptions it is based on.

If the process or model used to generate data is flawed or limited, the synthetic output will be unrealistic and lead to wrong conclusions. For example, if you generate synthetic client accounts based on a small biased sample, it can skew your entire analysis. 

Another concern is around synthetic data’s ability to capture dynamic, real-time market changes. If patterns shift rapidly, a synthetic dataset based on last year’s patterns will miss the new trends. 

Synthetic data should supplement, not outright replace, real data analysis. Businesses should continuously calibrate synthetic data with real-world results to ensure accuracy.

The Human Element

Synthetic data, especially for market research or consumer behaviour, can reveal the “what” (patterns, preferences) but not the “why” behind customer behaviours.

Synthetic personas or survey answers are generated by algorithms. They cannot capture the emotions, biases, and context that influence real buyers and throw a curveball into real-world results. Synthetic data can only mimic, it cannot reflect the irrationality and unpredictability of human respondents.

Consider a CFO choosing a consulting firm. Synthetic data will predict logical selection criteria, but it will miss how a real CFO might be swayed by a personal referral or gut feeling. This limitation can produce marketing messages that appear logical but fail to resonate emotionally.

The solution isn't abandoning synthetic data but using it strategically. Form initial hypotheses with synthetic insights, then validate through actual customer interactions, interviews, or pilot campaigns.

Cementing Bias

If the data you build your synthetic dataset on contains biases, those biases could still persist or even be amplified during generation. A generative model might overfit to majority patterns and essentially ignore minority data, producing an even more homogeneous synthetic dataset.

Simply adding synthetic diversity without addressing the root causes of bias in real-world data also leads to "diversity-washing," where the appearance of inclusivity masks underlying issues.

Marketers need to check for bias in synthetic outputs and remain cautious when interpreting it. To mitigate the risks, teams should regularly audit synthetic data distributions against real-world demographics, work to address issues that lead to underrepresentation, and validate insights derived from synthetic data against feedback from actual representatives of underrepresented groups before finalising marketing strategies.

The Synthetic Echo Chamber

There's a growing concern about feedback loops when models train on synthetic data produced by other models. If marketers use AI-generated data to train marketing AI which then generates more data, quality can degrade.

If models are trained only on other models' outputs, the results progressively lose connection to reality. In marketing terms, if each subsequent generation of strategy or content is based on synthetic inputs rather than real customer interactions, you risk drifting away from your customers without noticing.

To prevent this drift, keep humans and real data consistently in the loop. Use synthetic data to augment, not replace, real data. Regularly re-ground AI models with actual observations and maintain a healthy skepticism toward AI outputs, regardless of how plausible they appear. When synthetic data fails basic reality checks, be prepared to discard it and either regenerate or, better yet, collect authentic data instead.

The Trust Barrier

A significant challenge is likely to be convincing stakeholders to trust insights derived from "fake" or “made-up” data. This represents a cultural hurdle rather than a technical one.

You will need to educate people on how synthetic data is generated based on real patterns and has proven accurate in many contexts. Demonstrating your own validation cases where synthetic results matched real data can build confidence.

You should also be transparent about its limitations. Framing synthetic data as a tool rather than an absolute truth encourages appropriate stakeholder expectations and increases buy-in.

External trust poses another concern. While synthetic data works well for internal analysis, its presence in client-facing materials might damage credibility. Consider how you will create a firewall between internal and external communications, and maintain ethical practices by clearly labelling any synthetic examples as illustrative rather than experiential. 

The fundamental principle remains: synthetic data should enhance understanding without misrepresenting reality, preserving your organisation's trustworthiness in all communications.

Technical Hurdles and Quality Control

Generating high-quality synthetic data can be technically challenging. Available tools may not perfectly capture all requirements, resulting in data that statistically looks correct but contains implausible scenarios that human experts would immediately flag. These anomalies require filtering or model refinement, often demanding several iterations before producing usable results. 

Cost considerations also come into play. While some tools are free or open-source, enterprise-grade solutions can be expensive, though typically still more economical and timely than collecting large real datasets.

Plan for a bit of R&D when first adopting synthetic data. Start with small-scale pilot projects that allow for experimentation and troubleshooting. With experience, teams become more adept at anticipating and addressing these challenges, making the process progressively more efficient and reliable.

Regulatory Implications  

On the regulatory front, synthetic data exists in a grey area. In many cases, high-quality synthetic data is exempt from privacy laws because it contains no real personal information. This can be an immense advantage.

However, regulatory relief is not automatic. If there’s any chance synthetic data can be mapped back to a real person or if it wasn’t properly anonymised, it might still be personal data under laws like GDPR. Regulators caution that synthetic data generation using personal data must ensure individuals are not re-identifiable, otherwise privacy laws continue to apply. 

In highly regulated sectors such as finance and healthcare, synthetic data might need to meet higher standards or undergo audits to be trusted.

Marketers should take advice and implement privacy best practices, such as differential privacy techniques, removing any direct identifiers before synthesis, to solidify the compliance benefits of synthetic data. 

Overall, when done right, synthetic data can be a compliance-friendly way to use data, but organisations must still handle it carefully to truly sidestep regulatory pitfalls. Governance policies should define how synthetic data is generated, validated, and used to ensure it remains on the right side of privacy regulations.

Making the Most of the Data You Have

Synthetic data isn't a magic solution for all your marketing challenges, but it offers professional services firms a practical way to bridge the gap between data expectations and data realities. When real data is scarce, sensitive, or inaccessible, synthetic data could provide a viable alternative that preserves privacy while enabling data-driven marketing strategies.

The key is approaching synthetic data as a complement to—not a replacement for—real insights. Use it to amplify your existing knowledge, test hypotheses more quickly, and explore scenarios that would otherwise be off-limits. Be transparent about its limitations, vigilant about bias, and consistent in validating its outputs against real-world experience.

As marketing teams face increasing pressure to deliver personalised, data-backed strategies, synthetic data offers an interesting space to explore—one that honours client confidentiality while still enabling the analytics, testing, and modelling that modern marketing demands. It's not about working with perfect data; it's about making smarter decisions with the data you can access to today.

Need a data-driven marketing strategy that works with the realities of professional services? 1827 Marketing helps firms like yours create compelling campaigns that connect with decision-makers. Let's make your expertise visible to the right people, in the right places.