The experience moved from generic product browsing to a fit-guided structure built around support, coverage, matching pieces, and expert help.
From catalog browsing
to fit-guided shopping
The goal was to translate what already worked in stores: fit advice, styling support, and confidence around coverage and support, into a clearer online shopping experience.
HAISABIKINI is the online store of an authorized U.S. franchise retailer of Agua Bendita, the Colombian swimwear brand. I first defined the fit-guided strategy through an e-commerce audit, then translated it into a WooCommerce-to-Shopify rebuild.
Outcome
The audit identified the need to make in-store fit expertise visible online. The rebuild turned that strategy into navigation, collections, PDP logic, product data, and trust cues.
The project created a cleaner Shopify foundation for fit-based browsing, product merchandising, PDP guidance, SEO, analytics setup, QA, and future optimization.
Context
HAISABIKINI sells Agua Bendita swimwear through physical stores and online.
In store, customers get help choosing styles based on fit, coverage, support, size, body confidence, and styling. Online, that value was harder to see. The previous store behaved more like a catalog: product grids, seasonal collections, and product pages that expected shoppers to decide on their own.
Customers could already find Agua Bendita elsewhere. HAISABIKINI needed to make its advantage visible online: authorized Agua Bendita access, 12 years of swimwear expertise, physical store credibility, and help choosing the right piece.
The strategy phase identified the core opportunity: move from product-first browsing to guidance-first shopping.
The challenge
How do you make an online swimwear store feel less like a generic catalog and more like a guided fit experience without delaying launch or building a custom fit tool?
Shoppers needed help answering practical questions: Which top gives more support? Which bottom gives more coverage? How do I find the matching piece? Do these patterns coordinate? Is there a real person who can help me choose?
The work had real constraints: messy product data, thousands of product images, Shopify theme limits, and an Odoo integration outside my scope before launch.
My role
I led the strategy and UX structure from the initial e-commerce audit through the Shopify rebuild.
I focused on:
- Defining the shift from product-first to guidance-first shopping.
- Restructuring navigation and collections.
- Creating product data logic for product type, gender, support, coverage, print, and matching relationships.
- Defining how support and coverage appear across PDPs and collections.
- Designing PDP guidance logic for size guidance, WhatsApp support, matching pieces, same-collection recommendations, and trust signals.
- Scoping phase one around a strong Shopify foundation instead of an overbuilt concept.
I did not own Odoo/ERP debugging, full custom development, Meta shop migration, or post-launch analytics.
Key decision 1
Make fit guidance the strategic direction
The audit showed that the old site listed products and collections, but did not clearly explain why a shopper should buy here instead of through Agua Bendita’s main site or another retailer.
I recommended positioning HAISABIKINI around swimwear guidance. The store still needed to sell Agua Bendita clearly, but the stronger digital role became helping customers choose the right fit with more confidence.
Key decision 2
Structure navigation around shopping intent
The previous browsing structure leaned on catalog and collection logic. I restructured navigation around the way shoppers choose swimwear: product type, support, coverage, and matching pieces.
“Find Your Fit” gave shoppers a guidance-oriented path without requiring a custom quiz, while Men and Kids stayed simpler because the fit logic was less complex there.
Key decision 3
Make product data do UX work
The fit-guided experience depended on cleaner product data. I structured the product logic around category, gender, product type, support, coverage, print, and matching relationships.
Tags supported collection and filtering logic. Metafields supported cleaner PDP display logic, especially for support and coverage. This became the bridge between the fit-guided strategy and the Shopify implementation.
Key decision 4
Turn the PDP into a decision page
The old PDP showed the product, but did not support the full swimwear decision: fit, size, matching pieces, and help if the shopper was unsure.
The new PDP introduced product-type-specific fit logic, clearer sizing, directional matching labels, WhatsApp stylist help, same-collection recommendations, and trust signals.
For tops, the matching section says “Bottoms designed to match.” For bottoms, it says “Tops designed to match.” This made the relationship clearer than a generic related-products module.
Key decision 5
Keep phase one focused
Lightweight feedback from three people sharpened the final execution. Matching labels became more directional, microcopy clarified “same collection” versus “designed to match,” and PDP labels became more explicit about bikini tops and bottoms. “Complete the set” and “Complete the look” were also treated as distinct concepts.
There were bigger opportunities too: a fit quiz, richer product education, advanced recommendations, image reordering, custom analytics, and a fuller mix-and-match guide. Rather than expanding the scope before launch, I kept phase one focused on the Shopify foundation and the core guided-shopping experience.
The rebuilt store launched in late August 2026, with the larger ideas left as post-launch opportunities to evaluate against real traffic and customer behavior.
What I would improve next
Once the site is live and the Odoo integration is stable, I would focus on:
- Measuring engagement with fit collections and support/coverage filters.
- Understanding where shoppers drop off when completing a bikini set.
- Improving product naming so silhouette and product type are easier to scan.
- Reordering images so the item being sold is clear earlier.
- Tracking WhatsApp clicks from PDPs.
- Clarifying collection-based matching and mix-and-match logic.
- Improving filters for ready-to-wear and accessories.
- Testing whether a more guided fit flow is worth building later.
The foundation was built so future improvements can be based on evidence, not guesses.