For decades, retail has operated on a fundamental trade-off. Physical stores offered the tangible benefit of touch, fit, and trial, but lacked scale. E-commerce has delivered convenience and choice, yet it has introduced a persistent problem: the “try-before-you-buy” gap. The result has been a staggering return rate that costs the industry hundreds of billions of dollars annually.
That trade-off is beginning to shift. A powerful combination of Synthetic Data and Digital Twin technology is bridging the physical-digital divide, moving retail into an era of true, one-to-one, real people product simulation. Rather than relying solely on recommendation engines or historical behavior, retailers are starting to model experiences before they reach the customer. Virtual Try-On (VTON) is the most visible expression of this change, but its recent progress is driven by a less obvious factor: data that is generated, not captured.
Why Personalization Hit a Wall
The dream of virtual fashion is not new. For years, brands have attempted to create VTON systems for clothing, eyewear, makeup, and accessories. Most have failed to gain traction for a simple reason: they were powered by incomplete real-world data.
Training a reliable VTON system requires enormous diversity across multiple dimensions: body shapes and proportions, movement and posture, skin tones and textures, lighting conditions, and product variations such as fabric behavior, layering, and fit. Real-world datasets rarely meet these requirements. They are expensive to collect, slow to scale, constrained by privacy and ethical considerations, and often skewed toward narrow demographics. This data bottleneck is why early VTON usually felt like a gimmick, a 2D sticker crudely pasted onto a live video feed. For the system to be robust, it needs to be trained on more data than reality can provide.
Synthetic Data: The Engine of Infinite Diversity
Synthetic data changes the equation by removing the dependency on real-world collection. Using 3D rendering engines and generative AI, developers can create “Syntesis Humans”: highly controlled, fully labeled digital subjects that reflect far more options than physical sampling allows.
In a retail context, this approach addresses several long-standing challenges:
- Coverage and inclusion: Real-world datasets often miss minority body types or diverse skin tones. Synthetic generation allows developers to create perfectly balanced datasets, ensuring the VTON experience is engaging and accurate for every consumer, not just a statistical average. This is crucial for building inclusive and fair AI.
- Edge-case readiness: A VTON system often fails in “edge cases”: a difficult pose, complex lighting, or an unusual clothing layer. Synthetic data allows developers to model any possible human pose with perfect, pixel-accurate body landmarks. This strengthens system performance where real-world data is scarce.
- Material realism: Fabric behavior, makeup interaction with the skin, and accessory fit can be simulated with a level of detail that photographs often fail to capture or misrepresent.
- Catalog scalability: For fashion, synthetic data can produce millions of unique clothing combinations, modeling advanced layering and the physics of different materials. This allows a brand to create a virtual model of its entire product line without a single photoshoot.
By using synthetic data, the AI model is no longer guessing. It has been trained on a broader, cleaner, and more systematic dataset, allowing it to deliver a truly reliable VTON experience.
Beyond VTON: The “Digital Twin of the customer”
As virtual try-on matures, it is increasingly viewed as a step toward a more comprehensive concept: the Digital Twin of the customer. A digital twin is a persistent virtual representation of an individual, informed by both synthetic training data and real customer inputs. Synthetic data establishes the underlying rules—how fabrics move, how light reflects off skin, and how bodies are shaped—while lightweight scans or images personalize the model for the virtual try-ons. The result is a high-fidelity, one-to-one virtual replica that can be reused across experiences.
This Digital Twin of the customer becomes a persistent, personal model that retailers can interact with. Instead of a customer trying on a single item, their digital twin can try on an entire catalog in milliseconds, receiving instant, accurate feedback on fit, style, and drape. With digital twins, brands can run simulations at scale, shifting key retail decisions from assumption to evidence:
- Predictive recommendations: AI models can analyze a customer’s digital twin to anticipate which items from a new collection will deliver the best fit and feel, increasing conversion rates and overall satisfaction.
- Reduced returns: By validating fit and appearance before purchase, digital twins help address one of e-commerce’s biggest pain points: returns driven by poor fit, significantly lowering operational costs.
- On-demand design and testing: Designers can simulate new garments on millions of synthetic customer twins that reflect real-world diversity, refining sizing, materials, and silhouettes before production begins, and reducing both waste and rework.
Synthetic data, paired with virtual try-on, transforms the Digital Twin of the customer from a static profile into a dependable simulation, grounded in comprehensive, systematically generated datasets rather than incomplete real-world signals.
Toward More Predictive, Efficient, and Waste-Free Retail
Industry analysts increasingly point to synthetic data and simulation as foundational technologies for the next phase of AI adoption. Gartner has projected that synthetic data will surpass real-world data as the primary input for AI models by 2030, driven by scalability, cost, and governance constraints.
In retail, this shift responds to growing pressure to reduce waste, protect margins, and deliver experiences that are both personal and reliable. By reducing fit uncertainty, digital twins tackle one of e-commerce’s most costly inefficiencies: returns. At the same time, they enable more disciplined approaches to production planning, demand forecasting, and sustainability.
The transition from personalization to simulation marks a significant shift in how retail experiences are designed and optimized. Rather than relying solely on observation and iteration, brands can increasingly model and validate outcomes before they reach the market. In this context, the “synthetic customer” becomes an operational layer, enabling decisions grounded in evidence rather than approximation. This evolution also reframes the role of AI in commerce. As The Fashion Law has observed in its analysis of agentic AI, emerging systems will move beyond assisting shoppers to acting on their behalf, using digital twins as a foundation for decisions informed by accurate fit, preferences, and real-world context.
At Globant, this approach is already being implemented. We are advancing the use of synthetic data to power high-fidelity digital twins, enabling more accurate simulations, scalable training, and responsible AI development across various industries, including retail. By combining synthetic humans, virtual try-on, and real-time 3D, Globant’s Digital Twins Studio enables organizations to transition from experimentation to operational impact.
Explore how Globant’s Digital Twins Studio is reinventing how products, experiences, and decisions are designed.
