Tag: Retail Technology

Adidas: adiVerse Virtual Footwear Wall

A footwear wall that behaves like ecommerce

The future of instore displays is here. With this example you will see how today’s instore displays are evolving to meet our online experiences.

Adidas has created an in-store digital experience that was described at the time as showcasing over 8,000 Adidas shoes. The technology can be easily deployed to allow almost any retailer to sell the entire Adidas product range without having to be a flagship store in a major city.

How the adiVerse wall runs in-store

The experience is defined by a large footwear wall, made of multiple LCD touch screens that use facial recognition to detect a customer’s gender on approach to the wall. The adiVerse virtual footwear wall then customizes the product experience for that gender, and helps guide them to the perfect shoe.

Alternatively it lets them browse the entire range of products, with each shoe rendered in real-time 3D.

Endless aisle is a retail setup where a store sells the full catalogue digitally, even if only a fraction of it is physically stocked on the shelf.

Why it feels like online shopping, only bigger

This is essentially ecommerce browsing translated into a shared physical surface. You can scan, filter, compare, and inspect details, but the store controls the pacing and the context. The mechanism that matters is the blend of quick orientation plus depth on demand, and it works because shoppers can get to “relevant enough” fast, then only spend time on richer 3D detail when they care. In multi-brand sporting goods retail, bridging endless-aisle breadth with guided discovery is the difference between “too much choice” and “the right choice”.

Extractable takeaway: On any shared in-store screen, optimize for fast orientation first, then unlock depth only after the shopper signals intent.

The real question is whether your wall can move shoppers from browsing to a confident shortlist without turning discovery into an endless scroll.

Content depth for the winners, speed for everything else

The most popular products in the range get the full content play, including videos, game stats, product specs and even twitter feeds. Everything else stays light, so browsing does not become slow or confusing.

This “tiered content” approach is a practical way to keep performance high while still making hero products feel premium.

The retail play hiding inside the screens

In the end customers can add their selected product into a virtual cart, and check out via an iPad that the store sales staff would have.

That last step is the business intent. Sell the long tail without expanding floor space, while keeping checkout and assistance inside the store experience. Retailers should treat the wall as an assisted-selling surface, not a self-serve kiosk.

The adiVerse Virtual Footwear Wall is an in-store touchscreen wall that lets shoppers browse a large adidas shoe catalogue, inspect products in real-time 3D, and hand selections to store staff for checkout via tablet.

Patterns worth copying for your digital wall

  • Build an endless aisle that feels curated. Offer the full catalogue, but guide to a shortlist fast.
  • Use tiered content deliberately. Deep media for hero products. Lightweight data for everything else.
  • Make staff checkout the final bridge. Tablets in hand keep conversion human and immediate.
  • Design for “public browsing”. Big screens invite group decisions. The UI should support that.

A few fast answers before you act

What is the adiVerse Virtual Footwear Wall?

It is an in-store wall of touchscreen displays that lets shoppers browse a large adidas shoe catalogue, inspect products in real-time 3D, and pass selections to staff for checkout via tablet.

What does “endless aisle” mean in this context?

It is a retail setup where a store can sell the full catalogue digitally, even if only a fraction is physically stocked on the shelf. It expands choice without expanding floor space.

How does it personalize the experience?

It uses facial recognition to detect gender on approach and adapts the interface to that mode, while still allowing shoppers to browse the full range if they prefer.

Why does real-time 3D matter on a digital wall?

Because it supports confident decision-making in-store. Shoppers can inspect details quickly and compare options without needing a physical sample of every model.

What is “tiered content”, and why is it useful?

Hero products get rich media like video and deeper specs, while the long tail stays lightweight. This keeps browsing fast while still making winners feel premium.

How does checkout work in the flow?

Selections are handed to store staff who complete checkout on a tablet. That keeps conversion human and immediate, instead of pushing shoppers to leave the store journey.

Fits.me: Virtual Fitting Room

One of the main problems with buying clothes online is simple. You cannot feel the fit. So you guess, the parcel arrives, and the return loop starts again.

Fits.me, an Estonian company, builds a Virtual Fitting Room around a shape-shifting robotic mannequin. Instead of trying to “predict” fit with a size chart, the mannequin physically changes form to match your body dimensions, letting you preview how different sizes sit on a body shaped like yours.

A mannequin that changes shape so the garment can do the explaining

The mechanism is a robotic mannequin, often referred to as a FitBot, a shape-adjustable mannequin that can be tuned across a wide range of body measurements. Clothing is photographed on the mannequin in multiple sizes, and the shopper can compare how the same item behaves as size changes, on a body that resembles their own. Because the garment is shown on the same body shape across sizes, the comparison makes fit differences visible and reduces guesswork.

A robotic mannequin providing a Visual Size Guide.

In online apparel retail, fit uncertainty drives returns and suppresses conversion, so anything that reduces sizing doubt tends to outperform its surface-level novelty.

Why this approach feels more “real” than a size chart

What makes it persuasive is that it turns sizing into a visual comparison instead of a number. The real question is whether you can help a shopper see the trade-offs between sizes before checkout, without asking them to trust a black-box recommendation. If you have that problem, this is the right pattern to use. You are not being told “you are a Medium.” You are shown what Small, Medium, and Large look like on a similar shape, which is closer to the in-store decision process.

Extractable takeaway: When a purchase decision depends on a physical sensation you cannot deliver online, replace the missing sensation with a repeatable visual proof that helps shoppers compare options, not just read recommendations.

What the rollout says about where the pain is

At the time, the system is positioned around a male mannequin first, with Fits.me saying it is planning to unveil a female version in November. That sequencing is a reminder that “who we can fit well” is often a product constraint, not a marketing choice, especially when the technology depends on physical ranges and repeatable photography.

For more information visit www.fits.me.

What to steal from Fits.me’s FitBot

  • Make fit a comparison, not a verdict. Let shoppers see multiple sizes side by side on a body-like reference instead of outputting a single “recommended” size.
  • Design for confidence, then measure it. Track size changes after viewing, conversion on fitted items, and return-rate shifts by category.
  • Respect constraint sequencing. If the system only fits certain body ranges well at first, be explicit about where it is reliable and expand the range as the asset library grows.

A few fast answers before you act

What is a “Virtual Fitting Room” in this Fits.me context?

It is a system that uses a shape-adjustable robotic mannequin to model how garments look across sizes on a body shaped to match the shopper’s measurements, so shoppers can compare fit visually before buying.

Why does this reduce returns in theory?

Because it reduces guesswork. When shoppers can see how different sizes drape and sit, they are less likely to buy multiple sizes “just in case,” and less likely to be surprised when the item arrives.

What is the key difference versus typical size charts or recommendation widgets?

This approach is comparison-first. It shows a garment on a body-like reference across multiple sizes, rather than outputting a single recommended size and asking the shopper to trust it.

When does a visual fit tool like this not help much?

It helps most with size uncertainty, but it cannot fully replace tactile judgments like fabric feel or personal comfort preferences, so some returns will still be driven by “feel” rather than fit.

What should retailers measure if they deploy something like this?

Engagement with the fitting experience, size-selection changes after viewing, conversion lift on fitted products, and return-rate reduction by category and by first-time versus repeat shoppers.