Turning an emotional heritage product into a trustworthy digital experience

House of Ishaani is an ethnic fashion brand specializing in handwoven Banarasi sarees, entering digital retail with no existing UX structure and no established online trust signals for a category where authenticity is everything.

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Role
UX Designer & Website Developer
Timeline
2 months
Type
UX ResearchTaxonomy DesignShopify E-Commerce
Tools
FigmaShopify
The two-minute version
Problem

Customers struggled to understand weave types and distinguish similar-looking sarees, had authenticity concerns about handwoven claims, and hit decision paralysis while browsing, on a site with no prior UX structure to build from.

My role

UX Designer & Website Developer, responsible for both strategic design decisions and the technical Shopify build.

What I did
  • Ran discovery through stakeholder interviews, customer conversations, competitor analysis, and session replay behavior mapping.
  • Replaced the flat category list with a three-dimensional taxonomy, occasion, weave, and material, matching how gift, style, and material buyers actually shop.
  • Designed and developed the full Shopify experience, including authenticity signals like weaving technique stories and craftsmanship badges.
Outcome

Increased product engagement and scroll depth, more confident add-to-cart actions, and reduced browsing fatigue through clearer categorization.

Proof

Research showed customers frequently zoom images and read weaving techniques carefully before comparing multiple sarees, emotion and trust, not visual appeal alone, drive luxury textile purchases.

3D taxonomyOccasion · weave · material
Engagement & scroll depth ↑Post-launch
Browsing fatigue ↓Clearer categorization
Live on Shopifyhouseofishaani.com
House of Ishaani homepage, live at houseofishaani.com
The live homepage, houseofishaani.com
01·Context

No prior structure, a complex catalogue, and a trust gap

Banarasi sarees are a heritage, high-value product where authenticity claims matter enormously. The brand had no prior UX structure to build from, a catalogue that looked repetitive to an untrained eye, and no digital trust signals to reassure buyers that handwoven claims were genuine.

Evidence: stakeholder interviews, customer conversations, competitor analysis, and session replay behavior mapping

Session replays were the most useful input, they showed people behaving nothing like typical fashion-site browsing. Visitors zoomed into weave close-ups repeatedly, opened and reread the same technique descriptions, and toggled back and forth between two or three sarees before either buying or leaving without adding anything to cart. Customer conversations confirmed why: on a flat category list, sarees that were structurally different, gifting-occasion pieces, style-led pieces, fabric-led pieces, all looked interchangeable, so buyers fell back on comparing images pixel by pixel to feel sure they weren't missing something.

Insight

The catalogue wasn't failing on taste, it was failing on trust. Customers weren't undecided about which saree looked best, they were unable to confirm they understood what made each one different, and in a category where a wrong purchase is expensive and hard to return, that uncertainty was enough to stall the decision entirely.

02·Decisions

Structuring the catalogue around three different shoppers, not one

The original flat category list treated every visitor as a generic browser. But the interviews surfaced three distinct shopper types, gift buyers starting from an occasion, style buyers starting from an aesthetic, material buyers starting from a fabric, each asking a different first question the old structure couldn't answer.

Keep a single category list, add more product photography Rejected

More images would have fed the exact zoom-and-compare loop the session replays showed, without addressing why buyers needed to verify so carefully in the first place.

Three-dimensional taxonomy: occasion, weave, and material Selected

Lets each of the three shopper types start from the question they actually arrived with, and pairs that structure with visible authenticity signals so trust doesn't rely on image-zooming alone.

01

Three distinct shopper types. Gift buyers, style buyers, and material buyers each start from a different question, occasion, aesthetic, or fabric, and a single flat category list served none of them well.

02

Customers verify before they trust. Session behavior showed frequent image zooming and careful reading of weaving technique descriptions before comparing across multiple sarees, so weaving-technique stories and craftsmanship badges were added directly where that verification was already happening.

03

Guided navigation reduces fatigue. When products look visually similar, structure, not just imagery, is what lets a buyer feel confident they've compared the right options.

03·Design System

Component patterns, live on houseofishaani.com

A three-dimensional taxonomy only reduces fatigue if the filters and authenticity signals are visible at a glance, not buried in a product description. The visual system leans into festive maroon and gold, a marquee announcement bar, gold CTAs, and taxonomy chips that let a buyer filter by occasion, weave, or material simultaneously. Hover any pattern below.

Primary ButtonHover to lift
Occasion Weave
Taxonomy ChipHover / active state
Katan Silk
Handwoven · Zari Kadwa technique
Fabric Story CardAuthenticity signal
04·Outcome

What shipped

A live Shopify storefront with a three-dimensional taxonomy that supports catalogue growth without structural rework, built-in authenticity signaling, and a navigation model matching how gift, style, and material buyers actually search. The zoom-and-compare loop the session replays flagged didn't disappear, but it now happens alongside visible craftsmanship signals instead of as the only way to feel sure about a purchase.

Flat category list → 3D taxonomyOccasion, weave, and material
No trust signals → visible craftsmanship badgesPlaced where verification was already happening
Engagement & scroll depth ↑Client-reported, post-launch
Browsing fatigue ↓Client-reported, post-launch

What I can and cannot claim. The engagement and fatigue directions reflect post-launch patterns the client reported to me, not numbers from an instrumented pre-launch baseline I ran myself, so I've kept them directional rather than quoting specific percentages. What I can point to with more confidence is structural: a taxonomy built from three named shopper types instead of one generic list, and authenticity signals placed exactly where the session replays showed customers already looking for reassurance.

Nutriahaar

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