Elastic Path • 2024 • 0→1 blueprint
Defining the foundations of customer-facing search so merchandisers control what shoppers find.
[ hero image / prototype video ]
TIMEFRAME
2024
ROLE
UX Researcher • UX Designer
DOMAIN
Enterprise E-commerce • B2B SaaS
TEAM
1 Product Manager • 1 Tech Lead
SKILLS
Generative Research • Competitor Research • Object Modelling • 0→1 Research and Design
What's Elastic Path?
Elastic Path powers enterprise e-commerce through API-first tools that allow brands to customize and scale storefronts.
What was the gap?
Storefronts had no customer-facing search and no workflows for merchandisers to configure it. Clients paid for additional services to fill the gap, and we were losing customers to competitors with native search.
What was my impact?
Search Profiles — the 0→1 blueprint for search configuration at Elastic Path. Validated demand for AI-supported, data-driven tools. Future-ready foundation for AI features and search analytics.
The Problem
Customers wanted search. Elastic Path had no infrastructure, and merchandisers had no way to set it up.
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No native search — storefronts had no infrastructure and no merchandiser workflows.
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Clients paid for additional third-party services just to get product discovery.
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Deals were lost to competitors with native search — leadership made this a differentiator.
how might we
How might we give merchandisers control over what shoppers find — flexible enough for complex catalogs, simple enough for people with no search expertise?
Solution
Research pointed to Search Profiles — flexible enough for complex catalogs, simple enough for daily workflows. The MVP centered on four capabilities.
01
Search Profiles — named, reusable configurations
A reusable, named configuration that defines which attributes customers can search. One object model that works for a furniture catalog with 10 upholstery options or a rental designer bag business.
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02
Attribute selection — control what's searchable
Choose which product attributes — like SKU, product name, size, and color — are searchable. Apparel can make size and color searchable while Electronics prioritizes brand and model number.
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03
Boosting rules — search as a sales tool
Boost results by weighting product attributes and setting business-driven rules — like promoting seasonal attributes during campaigns or making attributes more searchable to move overstock inventory.
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04
Profile management — apply across catalogs
Create, edit, delete, and apply profiles across catalogs — so different configurations can serve different catalogs and categories without rebuilding from scratch.
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05
Beyond the MVP — groundwork for the AI features that will set Elastic Path apart
Every object was designed so future differentiators can plug in without rework: relevance-based autocomplete with partial synonym matching, AI attribute suggestions from historical search logs and customer behavior, and search performance analytics that predict how profile changes affect discoverability, clickthrough, and conversion.
Research
None of us had search expertise, so I led foundational research around three goals — the ideal shopper experience, the ideal merchandiser experience, and what clients want from search.
I first mapped how search works in commerce — from indexing to attribute configuration — then interviewed 8 merchandisers with very different catalogs, from furniture with 10 upholstery options to a rental designer bag business to teams at Ugg and Breville.
primary users
Merchandisers were the primary users — and they saw search as a sales tool, not a utility.
"We'd love to promote seasonal attributes like colour during certain campaigns — search should help us sell."
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Finding 1 → Search as a sales tool
Merchandisers wanted precise control over which attributes were searchable
SKU, product name, size, color — control meant driving sales, like promoting seasonal attributes during campaigns.
Finding 2 → Flexible configurations
Different catalogs needed different search setups
Apparel needed size and color searchable, while Electronics prioritized brand and model number.
Finding 3 → Business needs first
Configurations had to serve business priorities, not technical defaults
Like making attributes more searchable to move overstock inventory. They were unsure which attributes mattered most to customers.
This informed a competitor teardown of Algolia, Salesforce and Shopify, and working sessions with PMs, engineers, and architects to scope the MVP before designing any screens.
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Competitor teardown — manual search rules, attribute configuration, and AI tuning across Algolia, Salesforce Commerce Cloud, and Shopify.
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MVP scoping sessions with PMs, engineers, and architects — what ships now, what gets phased, what gets outsourced.
design principles
Merchandisers, not developers, would live in this tool every day. Three principles guided everything we designed.
Flexible for complex catalogs
From 10 upholstery options to designer bag rentals — one model that fits any catalog.
Simple for daily workflows
Business language, not technical defaults — merchandisers set up search without search expertise.
Ready for the AI ahead
Every object structured so AI suggestions and search analytics can plug in later without rework.
scoping the mvp
Research surfaced far more than we could build. The hard part was deciding what not to.
I shared findings through multiple working sessions with PMs, engineers, and architects. Together we drew the line between MVP, phased, and outsourced — decisions mostly constrained by technical resources and budget. Object modelling kept the MVP honest: if a capability didn't fit the Search Profile object, it moved to the roadmap.
[ object model / scoping artifact ]
Reflection
My biggest takeaway: 0→1 work is less about designing screens and more about defining the right problem.
With no search expertise on the team, foundational research became the product decision itself — mapping how search works, listening to 8 very different merchandisers, and studying competitors gave us the confidence to define Search Profiles as a blueprint that's flexible for complex catalogs, simple for daily workflows, and ready for the AI features that will set it apart.