Why Synthetic Data is the Future of Customer Insights

Personal data collection provides rich detail and enables highly personalized communication tailored to user preferences. However, this approach is costly and requires stringent privacy governance. Marketers and media organizations face challenges such as incomplete data sets, consumer opt-outs, and the complexities of cleaning and de-duplicating records. It's impractical to gather every data attribute for everyone in a national population.

To maximize consumer insights, marketers often use both aggregate and personal data, understanding their respective limitations and potential. Many organizations aim to improve their insights by linking individual records across databases to identify potential customers or address data gaps. Yet, this process is costly, involving not only technological expenses but also ongoing costs for privacy compliance and the risk of fines for data breaches.

Given the constraints and expenses associated with aggregate and personal data, marketers may wonder if there's an alternative for obtaining individual-level insights on a national scale. The answer is yes - synthetic data offers a viable solution.

In this video, Arima's Founder, Winston Li, discusses AI and synthetic data in marketing.

What is Synthetic Data?

Synthetic data is generated through algorithms rather than being sourced from real-world events. It replicates the statistical patterns and relationships of actual data without including any personal information. This type of data is useful for testing and training models and is increasingly utilized to enrich consumer insights.

Synthetic data is produced using several techniques. Arima’s data product uses the SynC Method, which involves:

1. Preprocessing: Remove outliers from the source data.
2. Modeling: Use Gaussian copula models to capture underlying patterns and relationships.
3. Sampling: Create detailed data subsets based on these models.
4. Merging: Integrate these subsets with neural networks.
5. Scaling: Adjust the combined data to fit the constraints of the original dataset.

This method ensures the generation of detailed synthetic data while preserving accuracy and maintaining privacy.

In the video below, Winston Li explains how synthetic data is created.

Applications of Synthetic Data

Synthetic data can be applied in various ways in marketing and research:

• Data Augmentation: Enhance existing data to gain insights about potential customers not visible in current CRM systems.
• Enhanced Audience Understanding: Achieve a deeper understanding of your target audience's preferences, motivations, and behaviors.
• Privacy-Compliant Insights: Make informed decisions while adhering to privacy regulations.
• Strategy Simulation: Test different strategies under simulated market conditions.
• Data Enrichment: Supplement existing datasets for a more comprehensive analysis.

Summary

As privacy laws become more stringent and 1st party data collection methods face challenges due to increased user opt-outs, synthetic data is emerging as a vital tool for marketers. Its adoption is likely to grow, offering new ways to gather and analyze consumer data effectively.

To remain competitive in this dynamic environment, it's essential to start exploring synthetic data now. Whether you're a small business looking to level the playing field or a marketer eager to harness cutting-edge technology, synthetic data provides valuable solutions for enhancing consumer insights.

Posted at MediaVillage through the Thought Leadership self-publishing platform.

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The opinions expressed here are the author's views and do not necessarily represent the views of MediaVillage.org/MyersBizNet.