Synthetic Data in Marketing: Using the "What If" Machine for Better Segmentation
I was recently asked in my CMO group chat about the practical reality of using AI for marketing segmentation. The question sparked a lively debate about privacy, budgets, and the "cold start" problem. Realising that many of these insights could benefit the wider community, I decided to compile my responses from that thread into this post to share with the larger marketing fraternity.
In marketing, segmentation is the cornerstone of strategy. It’s how we ensure the right message reaches the right person at the right time. But traditional segmentation has a significant flaw: it relies entirely on past behaviour.
What if you want to explore a market you haven’t entered yet? Or test a risky strategy without alienating your current database?
This is where Synthetic Data comes in. It is the creative engine that allows marketers to move from "what happened" to "what could happen."
What Is Synthetic Data?
Think of synthetic data as a flight simulator for marketers.
The Concept: Synthetic data is artificially generated information that mirrors the statistical properties of real-world data without containing any actual personal information.
It allows you to build a sandbox environment. A realistic but fictional dataset, where you can crash the plane (or the campaign) safely, learn from it, and then fly the real mission with confidence.
Why It Changes the Segmentation Game
Using synthetic data isn't just about having more data; it's about having safer, more flexible data.
1. Solving the "Cold Start" Problem (The Founder’s Hack)
Startups and new product lines often lack historical data. If you don't have the budget for enterprise data providers, you can use a technique called Extrapolation.
Instead of needing millions of data points, you can conduct just 5-10 deep interviews with real prospects. By feeding these anonymised transcripts into an LLM as "seed data" (the Ground Truth), the AI can extrapolate the psychographics and generate hundreds of synthetic personas that mirror those core traits. This allows bootstrapped founders to scale a handful of honest conversations into a statistically significant testing pool without the heavy price tag.
2. Privacy-First Innovation
With regulations like GDPR and PDPA tightening, using customer data for experimental testing is risky. Synthetic data contains zero PII (Personally Identifiable Information). You can share these datasets freely with third-party vendors or internal teams and stay within compliance guardrails.
3. How It Works: Building the "Synthetic Engine"
So, how do you actually construct a synthetic persona? Based on my experience building these extensions, the process isn't about replacing research; it's about stitching your data pipes together to create a holistic view.
Here is the blueprint for a Living Synthetic Model:
Ingest the Ground Truth: You start by feeding the model your internal qualitative data, Focus Group Discussions (FGDs), In-Depth Interviews (IDIs), and digital behaviour logs.
Layer External Context: To make the persona robust, you layer in external market intelligence. You can plug in publicly available macro-trends or industry reports to ensure the persona understands the broader market landscape.
The Interrogation Phase: Once built, you don't just look at the data; you talk to it. You can ask: "Will this creative messaging resonate with you?" or "How would you react to this price point?" The model provides viability scores, allowing you to test frameworks before spending ad dollars.
Fueling the System: This is not a "set it and forget it" tool. It is a dynamic intelligence that needs to be fueled. To keep the synthetic personas in tune with reality, you must continuously update them with the latest research.
Crucially, this complements your insights team. It doesn’t make current practices redundant. Instead, it scales your human insights, allowing you to run hundreds of simulations based on the hard work your researchers have already done.
4. The Landscape: Who Is Building This?
The technology behind synthetic data is maturing rapidly. Several players are making these tools accessible for marketing and business use cases. These are the ones I am aware of, and I am sure there are many more.
Global Leaders
Mostly AI: A heavyweight in the sector, they specialise in generating highly accurate synthetic data. Their platform is designed to unlock customer insights for segmentation and behavioural analysis without compromising privacy.
Hazy: Based in the UK, Hazy focuses heavily on privacy-compliant solutions for the fintech sector, allowing enterprises to unlock siloed data for safer testing.
The Asian Ecosystem
The synthetic data wave is also rising in Asia with highly innovative players.
Betterdata (Singapore): Ideal for marketing, they focus on data sharing and privacy engineering. Their tools help businesses simulate data for new market expansions or train models on sensitive data without exposing user details.
BridgeAI Tech (Singapore): BridgeAI is a custom AI solutions firm (Singapore (Headquarters) + India (Subsidiary)) that designs and builds AI agents, RAG pipelines, and automation workflows for Market Insights/Analytics, Marketing, Sales, and Events/Experiences. They focus on outcomes such as faster research cycles, higher content throughput, and lower operating costs, with governance and security built in.
DataGrid (Kyoto): Taking a visual approach to segmentation, DataGrid generates synthetic digital humans. This allows marketers to populate campaigns with diverse, AI-generated models (avatars) to test visual strategies across different demographics without the cost of physical photoshoots.
The Bottom Line
Synthetic data transforms marketing from a reactive discipline into a proactive one. By simulating the future, you can refine your segmentation strategies today, ensuring that when you do launch to real people, your message lands perfectly.
Adopt AI with Confidence and Clarity. Are you struggling to build a business case for AI or unsure about governance and compliance? AIdeate Solutions guides organisations through practical, responsible AI adoption. We help you move beyond the hype to implement workflows that create real value.
