The word synthetic sparks mixed reactions. To some, it means fake or artificial, such as synthetic fabrics or artificial flavors. But in many fields, from healthcare to computer vision, synthetic has become synonymous with innovation. We rely on it to power safe simulations for pilots, self-driving cars, and even the AI models that help doctors identify disease faster than ever.
Now that same story of transformation is showing up in how we understand shoppers. Instead of waiting months for real-world data to accumulate, teams can now explore ideas through synthetic datasets that behave just like real data, only faster. At InContext, this evolution begins in virtual stores that use artificial intelligence and simulation to capture how customers browse and buy, well before a single product hits a real-life shelf.
So before diving into how synthetic data works, it helps to rethink what synthetic really means in the context of modern shopper research.
Synthetic Data Is Redefining Shopper Research
In modern analytics, synthetic describes something carefully engineered to behave like reality without the risks and constraints of the real world. Synthetic data mirrors the statistical properties and correlations found in real-world datasets without exposing any sensitive information. When it comes to shopper research, synthetic data generation entails thousands of virtual shoppers walking virtual store aisles, generating high-volume, high-quality behavior patterns grounded in original data yet safe to share and model across teams.
For shopper insights teams, this framing is vital because the goal is not to completely replace human shoppers with robots. Instead, it’s to create an experimental playground where those shoppers’ choices can be explored from every angle. Instead of running one or two tests a year, brands can spin up countless synthetic journeys and see how different layouts or price points change shopper behavior.
At InContext, that playground starts with years of observed behavior in virtual stores. This behavior provides the raw material synthetic models need to stay grounded in reality. Just as data scientists use generative models and neural networks to improve data quality in AI training, InContext’s virtual environments produce synthetic shopper journeys that help brands anticipate decisions with remarkable accuracy. These journeys are encoded, repeatable behaviors that give teams something they have never really had before: the ability to ask “what if?” over and over again, without waiting months for an answer or putting real shoppers at risk of a bad experience.
What Is Synthetic Data?
Synthetic data is new data created by artificial intelligence systems trained on real-world data. The AI models are built using machine learning techniques and study existing datasets to understand how shoppers behave. They then generate new data that reflects the same underlying patterns and relationships, resulting in data that behaves like real shopper data without reproducing any individual record.
Rather than copying rows from an original dataset, synthetic data is generated through models that learn how variables interact, such as how changes in price or placement influence decisions.
Types of Synthetic Data Used in Shopper Research
There are several approaches to synthetic data generation, but two are most relevant for shopper insights.
Fully simulated data is created using predefined rules or assumptions. For example, a model might simulate how shoppers would navigate a store if all aisles were the same length or if pricing were uniform across categories. This type of simulation is useful for stress-testing ideas or exploring edge cases, but it lacks the behavioral nuance of real-world data.
Partially synthetic data, by contrast, is built by training AI models on real datasets and then generating new records that preserve the statistical properties of the original data. These models learn correlations and patterns, such as how shoppers move between categories or how price sensitivity changes by segment, without copying individual records, resulting in data that behaves realistically.
This second approach is where synthetic data becomes especially powerful for shopper research. It allows teams to work with large, representative datasets that reflect how people actually shop, without being constrained by sample size or limited test windows.
How Synthetic Data Is Created and Used
These systems rely on techniques such as generative adversarial networks (GANs). In simple terms, one machine learning model generates synthetic shopper data while another evaluates whether it behaves like real data. Over time, this feedback loop improves the quality of the output, producing datasets that closely match the statistical patterns of real-world behavior.
This process allows teams to:
- Generate new datasets that preserve key relationships found in original data
- Explore scenarios that haven’t yet occurred in the market
- Test hypotheses without exposing sensitive information
- Validate ideas before committing resources to physical execution
Because the data is synthetic, it can be shared more freely across teams and used for experimentation without the compliance risks associated with real customer data. That makes it especially valuable for organizations looking to move faster.
Why Synthetic Data Is Emerging in Retail
Retail decisions are being made in an environment that changes faster than traditional research cycles can support. According to Marketing Dive, more than 80% of purchase decisions are made in the store, with over 60% of shoppers making impulse decisions at the shelf. In other words, the most critical moments influencing conversion happen in real time, in front of the product.
Yet category strategies are still built using backward-looking data. Many teams rely on historical sales and past performance reports to predict what will happen next. While that data does have value, it reflects what already happened under conditions that may no longer exist.
Most experts agree that we can be exposed to as many as 4,000 ads a day. Combine that with new brands popping up almost daily and shoppers’ favorite influencers keeping up with their brand deals, and you have shopper behavior shifting faster than traditional reporting cycles can keep up with. This is especially so when you add in pricing pressures that leave customers ditching loyalty for sales.
This creates a growing gap between insight and execution, where decisions are made with confidence in the data but uncertainty about how shoppers will actually respond once a new planogram or assortment hits the shelf. And that gap is where risk creeps in.
When teams rely solely on historical data, they’re forced to make high-stakes decisions without a clear view of how shoppers will behave in the moment. This is why modern shopper insight needs to evolve from analyzing the past to testing the future.
The Benefits of Synthetic Data for Shopper Insights Teams
For shopper insights teams, testing with synthetic data translates into real advantages.
Faster, better decisions
Synthetic shopper intelligence allows teams to test multiple scenarios at once without waiting weeks or months for real-world results. Instead of debating which option might work, teams can see how shoppers are likely to respond before committing resources. This accelerates decision-making cycles and replaces gut instinct with evidence.
Reduced execution risk
Every change on the shelf carries risk, especially when it involves large resets or new product introductions. Synthetic testing reduces that risk by allowing teams to validate ideas before rollout. Rather than discovering issues after inventory is deployed, teams can identify friction points such as confusing layouts or low-visibility placements early and correct them before they become costly mistakes.
Stronger alignment across teams
One of the biggest challenges in category management is alignment. Merchandising, insights, sales, and operations often interpret the same data differently. Synthetic shopper insights provide a shared, visual foundation that brings teams onto the same page. When everyone can see how shoppers actually behave in a simulated environment, decisions are grounded in evidence rather than opinion.
Smarter, forward-looking planning
Traditional reporting explains what happened last quarter, but synthetic insights help teams see what is likely to happen next. By modeling how shoppers respond to different scenarios, teams can stress-test plans in advance and react quickly to coming changes.
Built-in data responsibility
Because synthetic data is generated from modeled behavior rather than real individuals, teams can explore insights freely without compromising privacy or compliance. This enables collaboration across functions and allows teams to share findings with partners without exposing sensitive customer information.
How InContext Turns Synthetic Data into Real Insight
For InContext, synthetic data is a natural extension of years spent observing shoppers inside virtual stores. ShopperMX is the virtual testing ground where brands and retailers run virtual shopping studies with shopper respondents in digital twins of real aisles and merchandising plans, creating realistic scenarios that can be explored at scale. Every study becomes both test data and training data for InContext’s AI systems, building a behavioral foundation rich in path, dwell, comparison, and purchase detail.
Each trip through a virtual store produces a stream of data points, including where shoppers enter, which displays they notice, how long they dwell, which products they compare, and what they finally place in their baskets. Over time, millions of these trips accumulate into a powerful behavioral dataset that supports a wide range of shopper insight use cases, from item introductions and promotions to full category resets.
Our Arrangement AI platform sits on top of this dataset as InContext’s predictive engine, using machine learning models trained on more than 2 million virtual shopping trips to simulate how shoppers are likely to respond to new planograms. These models apply advanced generative AI techniques to generate synthetic shopper data for new situations, projecting outcomes such as expected sales or item performance across different shelf arrangements. The forecasts are continuously validated against new virtual studies and in‑market results so they stay closely aligned with real behavior.
Because Arrangement AI builds on aggregated virtual behavior rather than directly on identifiable individuals, the overall approach preserves privacy, giving teams freedom to explore more ideas without increasing risk. At the same time, the models improve their functionality and support better optimization of shelf and assortment strategies as more trips flow through the generation process.
Synthetic Data, Real Understanding
Synthetic data does not replace the need to understand real people. In fact, it’s just the opposite. The goal is to use it to amplify the ways teams can explore and serve real shoppers. It allows researchers to move beyond the limits of historic data and test more ideas in less time.
At InContext, we help shopper insights and category teams move from guessing to anticipating with speed and accuracy. Synthetic may sound artificial at first, but when it is built from real behavior and refined through sophisticated models, it delivers a truer, more actionable picture of how shoppers are likely to act in the moments that matter most.
Looking to change the way you adapt to your shoppers? Reach out.
