Artificial intelligence has turned data into marketing’s most valuable currency. But as privacy norms tighten and access to real-world datasets becomes limited, brands are looking for alternatives that balance accuracy, compliance, and innovation. In 2025, synthetic data, or computer-generated information that mirrors real consumer behavior, is quietly becoming the new training fuel for AI-driven marketing systems.
Unlike traditional anonymization or masking, synthetic data is created through algorithms that simulate realistic user patterns without exposing real identities. This shift is reshaping how marketers build, train, and test AI models for segmentation, personalization, and predictive campaigns. It is not a replacement for real data but a complementary layer that allows innovation in a privacy-first world.
Why Synthetic Data Matters Now
Across industries, data collection has slowed due to regulations such as India’s Digital Personal Data Protection Act (DPDP) and Europe’s GDPR. AI models that once relied on millions of user interactions now face data scarcity. Synthetic data fills this gap by providing abundant, high-quality inputs without breaching consent laws.
A recent McKinsey analysis estimated that global synthetic data production will grow at a compound rate of over 30 percent through 2030. In India, martech companies and data science teams within BFSI, telecom, and retail sectors are among the earliest adopters.
Explaining the appeal, Deepak Oram, Head of Digital Marketing at Titan Company, noted, “Synthetic data is helping us test new personalization models without waiting for months of live data. It gives us speed and compliance in the same workflow.”
How It Works
Synthetic data is generated using machine learning techniques such as generative adversarial networks (GANs) and large language models. These systems learn from limited real datasets to create artificial but statistically accurate samples that resemble genuine consumer behavior.
For example, an AI engine might simulate how different customer personas react to a new ad or discount offer, or how they interact with a website journey. The resulting datasets can then train campaign optimization tools, CRM models, or A/B testing systems.
In marketing, this approach enables teams to run predictive tests, from understanding which product categories will trend to forecasting churn, without exposing real user records. For data scientists, it means building scalable models while staying compliant with privacy laws.
Applications Across Marketing
Synthetic data is finding practical use across every stage of the marketing funnel.
Customer segmentation and targeting
Retail and D2C brands use synthetic datasets to identify micro-segments that might not yet exist in live data. AI-generated profiles can help brands simulate how first-time or high-value customers behave across channels, improving targeting accuracy.
For instance, a leading e-commerce marketplace in India recently used synthetic behavioral models to design campaigns for new Tier-2 markets. The system analyzed patterns of similar demographic groups, enabling accurate regional targeting even before the campaign launch.
Personalization and predictive engagement
AI models trained on synthetic data can anticipate what customers might want before they signal intent. In BFSI, credit and insurance companies are experimenting with synthetic transaction data to design responsible upselling strategies without breaching financial privacy.
Rahul Talwar, Chief Marketing Officer at Max Life Insurance, said, “Synthetic modeling allows us to experiment with new engagement paths responsibly. It helps us understand what kind of content resonates across cohorts while keeping customer data secure.”
Ad performance testing
Synthetic data enables creative testing without the cost of live campaigns. Brands can simulate audience reactions to multiple ad versions, estimate click-through probabilities, and optimize budgets. This practice is being integrated into digital media planning tools by martech firms like Netcore and CleverTap, which have introduced synthetic audience modeling for campaign rehearsal.
CRM and retention modeling
In B2B marketing, synthetic customer relationship management (CRM) data helps companies predict lead conversion rates and renewal behavior without using sensitive client records. SaaS firms are also leveraging synthetic feedback loops to train chatbots and sales intelligence systems that handle inquiries more naturally.
Indian Market Adoption
India’s martech ecosystem is uniquely positioned to benefit from synthetic data. With over 800 million internet users, but only a small portion providing explicit consent for behavioral tracking, brands need alternatives that respect privacy. Synthetic data allows them to keep personalization alive while meeting legal requirements.
Companies like Fractal Analytics and Tredence are helping large enterprises generate realistic data models for customer analytics. Meanwhile, smaller startups such as Yellow.ai and Entropik Tech are exploring emotion-rich synthetic datasets to train conversational and visual AI tools.
A 2025 Nasscom report estimated that nearly 22 percent of Indian enterprises using AI in marketing have experimented with synthetic datasets in pilot projects. That number is expected to cross 40 percent by 2026 as data protection norms mature.
Challenges of Accuracy and Bias
Despite its potential, synthetic data brings its own challenges. Models trained on artificial data may amplify the biases present in their source datasets or produce unrealistic correlations.
Ravi Santhanam, Chief Marketing Officer at HDFC Bank, observed, “Synthetic data will only be as good as the logic that generates it. We need frameworks to validate its fairness and reliability, especially in regulated industries like finance.”
Marketers must therefore maintain a strict validation loop, comparing model predictions on synthetic data with limited real-world outcomes. Ethical concerns also remain, as synthetic datasets can inadvertently mimic sensitive traits if not carefully filtered.
Data Governance and Trust
As synthetic data moves into mainstream marketing, governance is becoming a strategic priority. Experts recommend three best practices for organizations adopting synthetic data:
-
Transparent labeling to distinguish synthetic datasets from real ones in storage systems to prevent confusion during audits.
-
Regular bias audits to test synthetic datasets for skewed outcomes or representational gaps.
-
Human review to evaluate whether model decisions remain consistent with brand values.
In India, data governance frameworks are evolving. The Digital Personal Data Protection Act emphasizes consent and minimal data usage. Synthetic data, when properly documented, aligns with these principles and allows responsible experimentation.
Global Momentum
Globally, major tech players have accelerated investment in synthetic data platforms. Amazon uses synthetic datasets to improve Alexa’s conversational responses, while Meta trains recommendation models using artificially generated social interactions to reduce privacy risks.
In marketing technology, Adobe, Salesforce, and Google Cloud are integrating synthetic simulation modules into their AI studios. These allow marketers to test campaign performance and audience engagement at scale without tapping live databases.
The global synthetic data market is projected to reach over 3.5 billion dollars by 2030, driven by demand from sectors including retail, healthcare, and advertising. As more marketing teams adopt AI for decision-making, synthetic data is emerging as the underlying infrastructure for responsible innovation.
The Link to Generative AI
Synthetic data is also powering the next phase of generative AI in marketing. Generative models, such as image and text-based AI systems, need massive, high-quality datasets for training. Synthetic data helps fill these gaps by generating realistic prompts, customer conversations, and visual references.
In content marketing, for example, AI tools trained on synthetic interaction logs can produce personalized newsletters or chat responses that feel human but are privacy-safe. In advertising, synthetic scenarios can simulate how audiences might respond to new messaging before campaigns launch.
Indian martech firms are already integrating synthetic datasets with generative workflows. For instance, Netcore’s generative marketing suite uses simulated user journeys to guide subject line creation and creative optimization, while CleverTap experiments with synthetic event streams to refine user retention strategies.
Ethical and Future Outlook
As synthetic data scales, marketers will face a defining question: how much simulation is too much? The balance lies in transparency and accountability. Declaring where synthetic inputs are used and ensuring they do not distort real consumer intent will be key to maintaining trust.
Looking ahead, experts see synthetic data as a bridge between privacy and personalization. “The next evolution of marketing AI will depend not on how much real data we collect but on how responsibly we can recreate it,” said Vishal Chinchankar, CEO of Madison Digital and Madison Media Alpha.
Synthetic data will not replace human understanding or live customer insights. Instead, it will give marketers a faster, safer sandbox to innovate, test, and refine. For small businesses, it lowers the entry barrier to AI modeling. For enterprises, it ensures compliance while preserving creativity.
The Road Ahead
By 2026, synthetic data will likely be a core feature of every major CRM and marketing analytics platform. It will underpin audience simulation, cross-channel testing, and generative personalization tools.
For Indian marketers, this transformation offers both efficiency and responsibility. As brands balance privacy laws, customer trust, and performance goals, synthetic data may quietly become the invisible engine that powers marketing innovation.
In the end, the promise of synthetic data is not to replace real human experience but to enhance how marketers learn from it — ethically, intelligently, and at scale.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.