Cracking the Code: How synthetic data can revolutionize marketing in India

Data is the new oil; but collecting data is riddled with its own challenges, especially in an increasingly privacy-first world. Here comes synthetic data, which is artificially created data that mimics real data. It is a powerful tool with the potential to reshape industries, by addressing privacy concerns, data scarcity, and the biases that have long plagued traditional datasets.

In India, the adoption of synthetic data among marketers is still in its infancy, though it’s a fact that businesses will have to turn to such alternative models in their data-driven decision-making processes. Synthetic data holds significant potential for the future, particularly in industries such as marketing, e-commerce, finance, and healthcare. These fields heavily rely on customer data for their operations, but stringent privacy regulations often limit the use of real-world data. By leveraging synthetic data, organisations can overcome these restrictions, enabling innovation while maintaining compliance with data privacy laws.

Here’s what industry experts have to say...

Synthetic data adoption among Indian marketers is still in its early stages but shows promising growth, especially as privacy regulations tighten, points out Riddhi Chhabria Asrani, the Founder & CEO of All Stars Digital.

“It essentially mimics real data without exposing real customer information. It has thus become an essential component in sectors where consumer data privacy and regulatory compliance are of extreme importance. Synthetic data will be designed for simulating the most complex patterns in the preference and interaction of customers, thus allowing marketers to make better predictions about behaviour, improve targeting, and provide a more personalized experience to deliver tailored customer experiences for marketers in India,” Asrani adds.

Aijaz Ansari, VP - Global Marketing, Hoonartek, also says that the adoption of synthetic data by Indian marketers is in its early stages but gaining momentum, particularly in sectors like financial services, e-commerce, and technology. The synthetic data generation market in India is expected to reach a projected revenue of US$ 158.1 million by 2030 – a CAGR of 39%. Indian companies recognize the potential of synthetic data to simulate diverse, privacy-compliant customer scenarios. However, its use is not yet widespread, with many marketers exploring it cautiously due to limited expertise, technological barriers, and regulatory ambiguities, Ansari adds.

Use cases

What can be some successful use cases of synthetic data in Indian marketing?

According to Aijaz Ansari, Indian marketers can leverage synthetic data effectively in the following ways:

Personalized Experiences: Generate synthetic datasets that mimic customer behaviour across demographics and regions to train AI models for hyper-personalized campaigns.
Testing and Optimization: Use synthetic customer profiles to test new features, ad strategies, and interfaces without relying on sensitive real-world data (PII-stripped).
Scenario Planning: Create simulated environments and models to predict customer responses under different market conditions.
Data Privacy Safeguards: Since synthetic data is de-identified, it ensures compliance with data privacy laws like the PDP Bill (similar to GDPR and various other laws in the US) and reduces the risk of exposing personally identifiable information (PII).

Steps to Address Privacy and Security Concerns:

Implement secure synthetic data generation methods like GANs (Generative Adversarial Networks) that ensure high fidelity and realism.
Conduct regular audits to ensure synthetic datasets are devoid of reconstructable or sensitive patterns.
Partner with technology providers offering privacy-enhancing tools.

Successful Use Cases of Synthetic Data in Indian Marketing

Banking and Financial Services:

Use Case: A major Indian bank used synthetic data to train AI fraud detection models.

Challenges Overcome: Resistance from legal teams regarding compliance was mitigated by demonstrating the untraceable nature of synthetic data.

Retail & E-Commerce:

Use Case: An e-commerce giant simulated customer journeys using synthetic clickstream data for A/B testing new features.

Challenges Overcome: Convincing stakeholders of the equivalence of synthetic data to real-world data in predictive power.

Healthcare:

Use Case: A health-tech start-up used synthetic patient data for personalized health recommendations.

Challenges Overcome: Initial concerns over data fidelity were resolved by benchmarking against real datasets.

Telecommunications:

Use Case: A leading Indian telecom operator leveraged synthetic data to optimize its network services. By simulating user behaviour patterns—such as data usage during peak hours and signal performance in rural areas—the telco improved predictive analytics for network management and personalized customer plans.

Challenges Overcome:

Data Scarcity in Emerging Markets: In areas where limited real-world data was available, synthetic data provided a scalable solution.

Regulatory Concerns: Synthetic data ensured privacy compliance by not exposing sensitive subscriber information while still allowing robust analysis.

In Indian marketing, says Riddhi Chhabria Asrani, synthetic data has used its might in order to enhance personalization and refine the segmentation of the customer. “Telecom operators simulate multiple interactions between customers for better retention strategies and churn predictions without exposing sensitive data about the customer. Retailers use synthetic data through better recommendations and ad positions where produced data portray the purchasing habits of a customer without revealing confidential data.”

Strategies

When it comes to the adoption of synthetic data, marketers should deploy certain strategies to ensure data quality, accuracy, and compliance with evolving regulatory frameworks.

Riddhi Chhabria Asrani suggests that in order to ensure quality and compliance with synthetic data, Indian marketers should implement rigorous data validation processes that test for accuracy against real-world patterns.

“Synthetic data techniques, particularly Generative Adversarial Networks (GANs), have advanced significantly over time, enabling the creation of high-fidelity data while minimizing biases. Regular audits of synthetic data models are essential to detect inaccuracies and address errors, ensuring consistent quality control. Additionally, marketers must stay informed about evolving regulations, such as India's Digital Personal Data Protection Act, and implement robust data governance frameworks to ensure compliance with these laws,” says Asrani.

According to Aijaz Ansari, the strategies for ensuring data quality, accuracy, and regulatory compliance include:

Invest in High-Quality Generation Tools: Use advanced algorithms (e.g., GANs or Variational Autoen coders) to create synthetic data that closely resembles real-world variability.

Validation Frameworks: Set up processes to validate the quality and performance of synthetic datasets by comparing outcomes against real-world scenarios.

Compliance Checks:

Stay updated with global and local regulatory developments (e.g., GDPR, PDP Bill).

Engage legal and compliance experts to ensure datasets align with data residency and usage rules.

Collaboration with Experts: Partner with academic institutions and data scientists to refine methodologies and maintain high ethical standards.

The balancing act

In an era where data drives decision-making, synthetic data has emerged as a game-changer for marketers, offering cost savings, enhanced model accuracy, and scalability. However, its adoption comes with challenges, including potential biases, data integrity issues, and evolving regulatory landscapes. Striking the right balance requires a nuanced approach—leveraging the benefits while mitigating risks through careful governance.

It is important to understand how Indian marketers can maximize the potential of synthetic data by implementing best practices, such as rigorous model audits, bias detection, and compliance with data protection laws like the Digital Personal Data Protection Act.

Indian marketers must carefully balance the benefits of synthetic data with the potential risks it may pose, says Riddhi Chhabria Asrani.

“The foundation of this balance lies in ensuring that synthetic data reflects real-world patterns while avoiding inherent biases. Regular and systematic testing of data models for consistency and accuracy is crucial to maintaining data integrity and reducing errors. Using diversified datasets and incorporating fairness constraints during data creation can help mitigate bias risks. Adopting best practices, such as fostering collaboration across cross-functional teams, implementing robust bias-detection mechanisms, and staying updated on evolving data privacy laws like India's Digital Personal Data Protection Act, is essential. By aligning data practices with regulatory guidelines, marketers can ensure compliance, build consumer trust, and unlock the full potential of synthetic data,” adds Asrani.

According to Aijaz Ansari, marketers can balance the benefits of synthetic data with potential risks by:

Balancing Benefits and Risks of Synthetic Data

Benefits:

Cost Savings: Reduces dependency on expensive data collection and annotation.
Improved Model Accuracy: Helps create diverse datasets that account for underrepresented customer groups.

Risks:

Bias: Synthetic data may inherit or amplify biases from the training data.
Integrity Issues: Poor-quality synthetic data can lead to flawed insights or decisions.

Best Practices:

Bias Audits: Conduct regular checks for bias in the original datasets before generating synthetic versions.
Iterative Testing: Continuously refine datasets to reflect market changes and customer behaviour accurately.
Stakeholder Training: Ensure teams understand synthetic data's scope, benefits, and limitations.
Transparency: Clearly communicate to stakeholders how synthetic data is generated and its intended use.

“By adopting a strategic and ethical approach, Indian marketers can harness synthetic data to transform customer experiences while safeguarding privacy, maintaining regulatory compliance, and building consumer trust,” Ansari concludes.

Also Read: Cracking the Code of Effective Mobile Marketing Campaigns