What is Synthetic Data: Everything You Need to Know

Published On: 27 Feb 2024


Discover how synthetic data revolutionizes marketing analytics, addressing privacy concerns and enabling risk-free experimentation. Learn about its generation, use cases, and impact on marketing mix modeling.

Data is a critical aspect of any decision-making process. But how often do you manage to leverage it effectively? This is especially true when considering existing concerns like data scarcity, privacy, and regulatory issues. Enter synthetic data which refers to artificially created data that mimics real-world datasets while preserving privacy and confidentiality.

When it comes to marketing, where privacy concerns may constrain access to real-world consumer data, synthetic data offers a viable solution for generating realistic yet privacy-preserving data for analysis and experimentation.

Continue reading to know how synthetic data will redefine the way marketers analyze data and enhance the decision-making process.

This article looks at the meaning of synthetic data and ways to use it effectively.

How is synthetic data generated using algorithms and models?

Synthetic data generation uses advanced algorithms and models that produce data mimicking real-world datasets. These methods use techniques like generative models and statistical approaches. These practices help in creating synthetic data that exhibit similar statistical properties and patterns to authentic data.

The four synthetic data generation approaches with algorithms and models are:

1) Generative adversarial networks (GANs)

GANs comprise two neural networks: a generator and a discriminator. The synthetic data generator produces samples that are indistinguishable from actual data. On the other hand, the discriminator learns to differentiate between natural and synthetic data.

GANs use an adversarial training process to iteratively refine the generator's ability to generate realistic data by competing against the discriminator. GANs excel in capturing complex data distributions and generating high-quality synthetic data. For example, you can generate everything from images to structured data.

2) Variational autoencoders (VAEs)

VAEs learn the underlying distribution of input data. They consist of an encoder network that maps input data into a latent space. A decoder network reconstructs the input data from the latent space.

Sampling from the learned latent space helps VAEs generate new data points that follow the learned distribution. VAEs offer the advantage of providing a probabilistic framework for generating synthetic data while enabling data interpolation and exploration.

3) Statistical approaches

Statistical methods, likeMonte Carlo simulations and bootstrapping techniques, also help with synthetic data generation. It involves generating random samples from probability distributions to simulate scenarios.

Conversely, bootstrapping involves resampling from the observed data with replacement to generate synthetic data with similar statistical properties.

4) Hybrid and domain-specific approaches

Some synthetic data generation methods combine multiple techniques, like combining generative models with statistical approaches for improved performance.

Additionally, domain-specific algorithms can cater to the unique requirements of specific industries and applications to provide more accurate and relevant information for various synthetic data use cases.

Differences between synthetic data and real-world data

Synthetic data and real-world data serve distinct purposes and possess inherent differences. These distinctions impact their applicability and utility in various contexts.

Here's a comparative table highlighting their key characteristics.


Synthetic Data

Real-World Data


Generated artificially

Collected from authentic sources

Variability and Complexity

May not capture all nuances

Reflects inherent complexity and variability

Privacy and Confidentiality

Designed to preserve privacy

May contain sensitive information

Generalization and Bias

May exhibit biases inherent in algorithms

Reflects biases from data collection methods

Scalability and Cost

Cost-effective and scalable

Costly and resource-intensive

The role of synthetic data in marketing analytics

Synthetic data is transforming marketing analytics by ensuring privacy and enabling risk-free experimentation. It addresses data privacy concerns effectively, a critical issue when personal customer information is involved in 44% of data breaches. By mimicking real consumer behavior without using actual customer data, it allows marketers to analyze and gain insights while complying with privacy regulations. This data not only enriches marketing datasets by adding diversity but also enhances understanding of consumer segments and behaviors.

Furthermore, synthetic data is invaluable for training predictive models, offering a reliable alternative to real-world data that might be limited or biased. It supports testing different marketing strategies in simulated environments, helping predict outcomes, and adjusting campaigns for better performance. Its compatibility across various platforms aids in creating personalized marketing efforts seamlessly.

By leveraging synthetic data, marketers can adapt strategies based on the latest consumer trends and market dynamics, optimizing engagement and conversion rates. This crisp approach to using synthetic data underscores its importance in developing effective, privacy-compliant marketing strategies.

Use cases in marketing mix modeling and incrementality testing

Marketing mix modeling (MMM) and incrementality testing are integral to marketing analytics as they help assess the effectiveness of marketing strategies. The diverse applications and use cases of MMM in improving marketing performance include:

Attribution modeling

Is accurately attributing sales or conversions to marketing channels a problem your organization faces? This is a common concern, especially when we look at multi-channel environments and cross-device behaviors. Marketing mix modeling helps overcome the attribution modeling issue by analyzing historical data and including advertising spend, promotional activities, and external influences to quantify the impact of each component on overall performance.

Budget allocation

Proper budget allocation for different marketing campaigns can be challenging due to reasons like overlapping touchpoints, uncertainties with ROI, and limited resources. Marketing mix modeling guides decision-making by identifying the channels and strategies contributing to key performance indicators (KPIs) like customer acquisition cost (CAC) and lifetime value of a customer (LTV).

Campaign optimization

Compare the outcomes between synthetic test data and control groups to test the incremental impact of marketing campaigns. Incrementality testing helps through randomized control trials (RCTs) or quasi-experimental designs for campaign optimization. Such interventions isolate the causal effect of specific marketing interventions on consumer behavior and business outcomes.

Channel performance analysis

Analyze the performance of each channel to evaluate their effectiveness in driving desired outcomes. Use the following KPIs to identify high-performing channels: customer acquisition cost (CAC), return on ad spend (ROAS), and customer lifetime value (CLV).

Seasonality and trend analysis

Seasonality in marketing mix modeling allows the modern marketer to understand the relationship between seasonal trends, marketing activities, and sales. Analyze the root cause behind seasonal peaks using an automated and unified marketing measurement platform such as Lifesight.

Product portfolio management

Incrementality testing examines the incremental impact of new product launches or changes in pricing strategies. Controlled experiments or A/B tests help gauge consumer response to product innovations or pricing changes. Insights from this testing help guide data-driven decisions regarding product portfolio management.

Long-term strategic planning

Keep tabs on future trends to adapt your marketing strategies. Marketing mix modeling helps extrapolate historical trends and conduct scenario analysis to promote robust strategies that align with your marketing objectives.

The advantage of using synthetic data as a control in marketing experiments

Synthetic data generates realistic yet artificial datasets that enhance the reliability of research efforts. Let's understand the three benefits on offer and how they hold promise for your marketing campaigns.

1) Standardizes and controls the environment

Control variables and replicate experimental conditions accurately with synthetic data. A standardized process eliminates confounding factors to create a reliable foundation for conducting experiments.

For example, imagine if you want to test the effectiveness of email marketing strategies across demographic segments. Generating synthetic data that accurately reflects the demographic profiles ensures a controlled environment for your experiments

2) Enables large-scale experiments

Synthetic data helps with large-scale experimentation through the flexibility to generate vast quantities of data quickly and cost-effectively. Scalability allows you to test multiple hypotheses and gather significant insights without the constraints of limited sample sizes.

For example, a retail company uses synthetic data to simulate customer interactions across sales channels for enhanced analysis of the impact of marketing initiatives on revenue.

3) Mitigates privacy concerns

Synthetic data overcomes privacy concerns through privacy-preserving alternatives that mimic the underlying patterns and distributions of real data without compromising individuals' privacy.

Consider the example of a healthcare company conducting market research on patient preferences for a new medical device. They can use synthetic data to simulate patient demographics and treatment outcomes without accessing actual patient records.

Challenges of Market Matching and Synthetic Data's Solution

Here are some common challenges associated with market matching.

  • Difficulty in finding comparable control groups: It can be a struggle to identify suitable control groups that accurately reflect the characteristics and behaviors of the target audience. Access to comparable control groups will gauge the effectiveness of your marketing strategies and attribute outcomes to specific interventions.
  • Risk of selection bias and confounding variables: Market matching techniques based solely on observable characteristics may introduce selection bias. Furthermore, they cannot adequately account for confounding variables influencing consumer behavior. Incorrect comparisons can lead to flawed conclusions.
  • Limited causal inference: A campaign cannot yield desired results if marketing teams fail to establish causal inference that determines the true impact of marketing interventions on consumer outcomes. Without randomized control groups or rigorous experimental designs, it can be a struggle to attribute changes in results to specific marketing activities.

Now that we know the challenges, let's examine how synthetic data helps overcome them.

  • Enhances data augmentation and representation: Synthetic data provides a controlled environment for generating datasets that resemble real-world scenarios to augment existing datasets with additional variables that improve the granularity of analyses.
  • Mitigates bias and confounding variables: Synthetic data generation algorithms replicate the distribution of relevant characteristics and demographics in the synthetic datasets. Use this data to control confounding variables for a more accurate comparison between treatment and control groups and reduce the likelihood of biased results.
  • Facilitates controlled experiments and causal inference: If you're looking to assess the impact of marketing campaigns on consumer behavior, synthetic data simulates randomized control groups and manipulates variables of interest.

Synthetic data and marketing mix modeling

In marketing mix modeling (MMM), synthetic data addresses key challenges and enhances the effectiveness of analytical processes. Synthetic data supports data augmentation efforts to supplement existing datasets with additional variables. The result is enriched datasets with improved model representativeness and enhanced accuracy of forecasting.

However, using synthetic data also presents potential risks of overfitting. The model may learn from noise in the data rather than genuine patterns. Evaluate the performance of your data models and validate their output against real-world observations.


Synthetic data overcomes issues like data scarcity and privacy to mitigate challenges associated with data-driven decision-making and presents challenges such as potential biases and risks of overfitting. Harness the power of synthetic data to gain deeper insights into your target audience and improve your forecasting accuracy.


1) What is synthetic data in AI?

Synthetic data in AI refers to artificially generated data that trains and tests AI models. There are several benefits on offer like resembling real-world data while preserving privacy.

2) What is synthetic data in machine learning?

Synthetic data in machine learning is artificially created data that mirrors real-world datasets. Such datasets train machine learning models and address data scarcity and privacy issues.

3) What is a synthetic data example?

An example of synthetic data is generating fake customer profiles with demographic information for market analysis.

4) Is synthetic data from generative models ready for image recognition?

Synthetic data generated from generative models like GANs can be suitable for image recognition tasks, provided that the models produce realistic and diverse images that capture relevant features.

5) What is synthetic test data?

Synthetic test data is artificially generated data explicitly used for testing purposes. Synthetic data finds common applications in training machine learning models and conducting research experiments.

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