It’s Monday, I’m Nithya Sudhir. I collect words, chase patterns, and write about whatever makes me curious.
Try-on is becoming the new check-out
Zara has just released a new virtual try-on feature that’s being positioned as a glimpse into the future of fashion retail.
But the idea behind it isn’t new at all.
It’s retail’s oldest solution to doubt.
Remember when shopping meant flipping through catalogs, offline or online, ordering blind, and just hoping a tablecloth didn’t arrive looking like a kerchief?
It’s Monday. Let’s talk about Try Before You Buy.
The original “Try Before You Buy” wasn’t tech. It was trust.
Long before it became a conversion tactic, Try Before You Buy was a risk-reduction strategy.
In the U.S. in the 1870s, Aaron Montgomery Ward popularized mass mail-order catalog shopping, sending price lists to customers who couldn’t inspect products in person. Other retailers quickly followed.
And they all ran into the same problem:
People don’t like buying what they can’t touch.
The fix was radical for its time — a money-back guarantee (the precursors to the modern “Try Before You Buy”).
Customers could receive products at home, inspect them, and send them back for a full refund if they didn’t meet expectations.
And more than a century later, e-commerce didn’t invent Try Before You Buy.
It simply brought it back.
Why we love try before you buy
People avoid purchases because the potential “loss” feels heavier than the potential gain. But consumers are more likely to buy a product if they know they can return it and get their money back if they’re not satisfied. In this case, the fear of losing money is diminished, making the purchase feel safer.
TBYB allows consumers to visualize themselves using the item in their daily lives. The endowment effect makes people place a higher value on items they own simply because they possess them, even temporarily. This can transform hesitant shoppers into confident buyers.
Power of visualization in decision-making:
65% of people are visual learners, processing information better when it's presented visually rather than in text. So, providing a visual, interactive try-on experience can significantly influence decision-making.
TBYB works because it turns a risky guess into a vivid preview.
Your camera roll, the new fitting room.
In 2018, Amazon launched what became Prime Try Before You Buy (originally “Prime Wardrobe”). And then Amazon ended that service in 2025, saying customers were increasingly using AI features like virtual try-on, personalized size recommendations, review highlights/summaries, and improved size charts instead.
Which makes full sense because that’s where we’ve headed. In 2026, “try before you buy” increasingly means “simulate before you commit.”
What’s changing in 2026 and forward
Shoppable AI try-on feeds (discovery-first shopping): Google’s Doppl is explicitly built around a personalized discovery feed where you can try looks and shop what you see.
“Try it on” with your own photo (not a model): This feature allows customers to virtually try apparel on yourself, just by uploading a photo. It's powered by a custom image generation model for fashion, which understands the human body and nuances of clothing - like how different materials fold, stretch and drape on different bodies.
3D avatars / virtual fitting rooms: Zalando has expanded virtual fitting to let customers create a 3D avatar using body measurements.
When customers know what to expect, return rates drop significantly and shoppers spend more time interacting with products, exploring variations, and building outfits.
Try before you subscribe: WHOOP’s risk-reversal flywheel
WHOOP is a screenless wearable and membership platform focused on health and performance insights, especially sleep, recovery, strain, and long-term behavior change.
WHOOP offers a free trial (typically 30 days / one month) so new customers can experience the product before committing.
It is a product whose value increases with time.
A trial period is perfect for products where: the buyer needs proof, the benefit is behavioral (sleep/training decisions) and the product gets “stickier” as the user builds context and trends.
Instead of asking you to purchase a wearable on faith, WHOOP hands you a free trial long enough to build a real relationship with your own data—sleep, recovery, and strain patterns you can’t unsee once you’ve tracked them for a few weeks.
How brands can do this without literally shipping “trial boxes”
Remove the sign-in wall
Let shoppers explore first; asking for commitment before value creates friction and distrust.
Show proof, not persuasion (reviews + UGC that matches the shopper)
Surface reviews by body type, skin tone, use case, and “people like you” outcomes to reduce uncertainty.
Make fit predictable (size confidence tools + clear return promise)
Size guidance + transparent returns isn’t generosity—it’s risk reversal with fewer steps (and fewer returns later).
Offer “simulation” layers (AR try-on, photo try-on, or 3D avatars)
The more realistically shoppers can visualize themselves, the less the purchase feels like a leap.
From mail-order guarantees to AI try-ons, the goal has stayed the same: remove the fear of choosing wrong.
And now that we can simulate the experience before we commit, shopping feels less like a gamble and more like a preview.
So the real question isn’t whether people will keep using Try Before You Buy.
It’s this: why would anyone go back to guessing?
How's the depth of today's edition?
As always, hit reply if something in here hits home.
See you next week,
Nithya
P.S. If you want to get a case study about your own brand, reply to this email. If you’d like to reach our newsletter audience (founders, creators, and marketers), click the button below.
If you’re new here, I’m over the moon you’ve joined us! To help me craft content that’s actually useful (and not just noise in your inbox), I’d love it if you took 1 minute to answer this quick survey below. Your insights help shape everything I write.
Insane Media is more than one voice
💡 Dive into our other newsletters - where psychology meets the founders, creator economy, Human resources and AI trends.
