A long list of features and functionalities won't tell you which search and product discovery solution is best for a department store, but three questions will: Do shoppers find what they mean? Can merchandisers and product teams control the results? Will your product data hold up as shopping moves into new interfaces?
Department store technology buyers often evaluate three distinct experiences as part of a single purchasing decision: search, product discovery, and agentic discovery (AI shopping agents). A unified platform can power all three, but sharing the same engine doesn't necessarily make it intelligent. The real question is whether that engine can reason across shopper intent, real-time behavior, and product data to deliver better results — not just serve the same catalog across different interfaces.
Department store catalogs complicate traditional keyword relevance because they contain hundreds of overlapping categories and dozens of attribute vocabularies, searched by shoppers who approach the same product from very different angles.
Take someone searching "dress for a wedding,” for example. They might want a formal gown, a cocktail dress, or a guest-appropriate midi. The right answer depends on context that keyword matching can't resolve.
Or take a search for "gifts for dad,” which can span tools, apparel, electronics, and grooming. Cross-category queries make it harder for menus and filters to help. Plus, long-tail queries can return zero results or irrelevant items, while head terms that work well in one category behave unpredictably when they match across hundreds of subcategories.
And on top of that, rules-based relevance worsens over time. Pinning products, writing synonym lists, and maintaining boost rules can be manageable at a few thousand SKUs. At department-store scale, with millions of SKUs, it becomes a backlog that never clears and caps what your team can improve.
To protect against overweighting platform features that look impressive on paper (but don’t fit your catalog) and underweighting capabilities like merchandising transparency, perform the capability-to-outcome test.
For every capability or development a vendor shows you, ask what it improves. Then, tie it to a business metric you already track:
This essential reframe helps you decide whether your team can use the system after launch.
| Belk's search team previously spent countless hours manually fine-tuning queries and creating redirects. After switching to Constructor's behavior-driven search, the department store reduced its reliance on manual interventions while achieving a compounded 7.4% lift in revenue per visitor. |
Search, product discovery, and agentic discovery (via AI shopping agents) have different goals, data inputs, and success metrics. Even so, they should all be driven by a single coherent strategy.
Here’s how to distinguish between them — and ultimately connect them — so you can more clearly evaluate vendor claims:
A shopper who types a query has a specific goal. The search system's job is to match that intent and surface the most relevant results, even when the query is conversational, misspelled, or navigational.
For a department store, that means handling popular head terms and a huge long tail of specific queries: "waterproof jacket for hiking in rain," "non-stick pan set under $100," "tall men's dress pants navy." Each one is a real purchase intent; if missed, the sale is lost.
Product discovery is exploratory. Shoppers browse category pages, click recommendation modules, and follow cross-sell prompts, often without a specific need or item in mind. The discovery system's job is to identify and show products worth considering and keep real users moving toward a purchase.
For department stores, this is where baskets grow across categories. A shopper who comes in for kitchenware and sees a well-matched dining chair in a "complete the look" module just grew their basket. That only happens if the recommendations system understands how those categories relate.
| With tens of thousands of SKUs spanning diverse product categories, Petco turned to Constructor to deliver more relevant, personalized search and browse experiences. By using real-time shopper behavior to inform product rankings across departments, the retailer achieved a 13% lift in sitewide conversions and a 4% increase in revenue. |
Agentic discovery is different from simple site search or product discovery. It’s also different from customer support chatbots. Think of agentic discovery, or agentic commerce, as AI agents acting as a "personal shopper" that can curate, compare, and sometimes buy on the shopper's behalf.
When it comes to agentic commerce, a shopper might use a conversational interface on the retailer's site, such as Constructor’s AI Shopping Agent, to find a gift, compare options, or work through a complex decision. To aid their decision-making, an agent on the product detail page (PDP), such as Constructor’s Product Insights Agent, can address last-minute product questions.
No matter the use case, AI agents built on a central reasoning engine and powered by real-time behavioral data can interpret the request, reason across products and information, and return guidance and personalized recommendations the shopper can act on.
At Belk, shoppers using Constructor's AI Shopping Agent convert at more than 2x the rate of shoppers using standard search, demonstrating the revenue potential of conversational product discovery.
Keep in mind that when it comes to agentic discovery, agents are only as useful as the data and various sources used to build and currently power them: product descriptions, availability, pricing, variants, fulfillment options, return policies, customer support documentation, etc. Incomplete or inaccurate data can lead to answers that mislead shoppers, and it can also negatively affect commerce AEO efforts.
| Surface | Primary goal | Data inputs | Typical user behavior or action | Primary KPI |
| Ecommerce search | Find a specific item | Keywords, behavioral signals, catalog data | Types a query | Search-to-cart rate, search click-through rate |
| Product discovery | Inspire and cross-sell | Behavioral patterns, merchandising rules | Browses, clicks recommendations | Revenue per session, lower abandoned cart rates, AOV |
| Agentic discovery | Conversational guidance, addressing product concerns | Natural language; product offer, policy data, support docs | Asks a question | Task completion rate, AOV, lower returns |
Department store product discovery solutions should be held to the highest standards because of the catalog complexity, long-tail query volume, and the range of shopper intent that come with operating in this vertical.
Rules-based relevance doesn't scale for department stores. The right solution learns continuously from real-time clickstream signals (what shoppers click, add to cart, skip, and buy) and uses that data to improve rankings without constant manual work.
In practice, the system responds almost instantly to how shoppers actually behave. A product that keeps converting in a given market during a given season appears more often. Shoppers made that call by buying it. Nobody wrote a seasonal rule. When behavior shifts with the season, a trend, or a promotion, the system adapts.
Word to the wise: Watch out for the vendors who say "AI-native." Ask them to show you which signals affect rankings and how the learning and user feedback loop works.
| Switching to Constructor from its legacy search platform to leverage AI-powered personalization grew Target Australia’s search revenue by 9%, search’s overall contribution to revenue by 12%, and reduced bounce rates by 93%. |
A strong solution performs well on head terms and on the varied long-tail of department-store shoppers. That long tail includes product configurations, cross-category gift queries, conversational requests with implicit intent, and browsing paths spanning multiple category trees.
Filters and attributes need to work at scale, including attributes that exist in one category but not another. If a search for "waterproof" returns outdoor gear but misses outerwear and footwear, the solution should help you fix it.
Also, product relationships matter just as much. Connecting dining chairs to a "kitchen furniture" search, or showing athletic socks next to running shoes, takes a system that knows how existing and new products relate. Shared keywords aren't enough.
A black-box model leaves merchandisers reacting. They can see outcomes in analytics but can't diagnose problems or act with confidence. For a department store running seasonal pushes all year, that means hours spent guessing at fixes.
The solution is to opt for glassbox merchandising controls, which allow merchandisers to see the factors behind results and use tested controls to change them.
A glassbox workflow might include:
Via Constructor’s Searchandizing dashboard, teams can add rules to modify their Search, Autosuggest, and Browse experiences. They can also see the impact of slotting, boosting, or burying at a glance.
For department stores, visibility into how manual rules are performing is especially important. Different teams manage different departments and are not always aware of how their own boost, bury, pin, and block rules impact broad-based search and browse experiences outside their own domain.
A Rule Performance dashboard with agentic explanations of how rules collide, where they trade off, and how they impact clicks, conversions, and revenue is critical for large merchandising orgs.
Beyond dashboards, agentic AI makes data analysis conversational and actionable. When rich behavioral data is paired with an always-on merchandising intelligence agent, merchandisers can talk to their discovery platform like a trusted teammate — asking why products rank the way they do, why a category’s revenue per visitor suddenly dropped, or which keywords would benefit from synonym expansion. They can even plan data-informed campaigns and workflows, then let the agent execute the work.
Constructor’s all-knowing AI merchant agent is easily accessible within the dashboard. It sits at the bottom right-hand corner of every page and can be activated at a moment’s notice.
When activated, the agent can explain why something happened and recommend and draft out the best next actions in a matter of minutes. This is thanks to having real-time knowledge of every catalog, rule, setting, and performance data (more on the agent later!).
Before you evaluate a solution or plan a migration, set baselines. (If you don’t have time, you can have product discovery specialists do it on your behalf in the form of a search audit or an agentic audit.)
Commonly tracked site-search KPIs include:
For product discovery, track engagement rate, add-to-cart from recommendations, cross-sell attach rate, and revenue per session. And for agentic discovery (via on and offsite agents), consider task completion rate, guidance accuracy, average order value (AOV), and return rate.
Keeping metrics separate by surface shows you where to invest and makes it easier to measure the impact of changes against key business goals.
So your team is on the same page, link surface KPIs to business metrics before you commit to a change, and remember that KPIs are most useful when they connect to what each business stakeholder cares about. A merchandising leader wants to know how search performance affects category revenue. A VP of ecommerce wants to compare discovery against other digital levers in terms of revenue per session.
That gives you a way to create and judge impact that survives reorgs and supports a credible business case for continued investment.
Be skeptical of performance claims without a clear methodology, including your own. Lift estimates, revenue attribution, and performance comparisons are only as reliable as the test design behind them.
When you read a vendor case study, ask: What was the baseline? Was lift measured with an A/B test or a before-and-after comparison? Over what time period, and on which metric? A reputable vendor can answer all of these. If one can't, don't treat the number as proof.
Agentic commerce is about AI agents becoming their own channel for discovery and purchase. While the technology is still early, retailers who prepare now will be best positioned to handle the influx of traffic from LLMs like ChatGPT and Gemini, as well as personal agents like Muse and Instinct.
As mentioned, an AI shopping agent is only as good as the data it’s built upon and can access. For a department store, that means ensuring consolidated data across supplier feeds, complete product descriptions, correctly modeled variants, real-time availability, clear fulfillment options, structured return policies, and consistent pricing.
Many retailers only discover data gaps when an agent gives an incorrect answer at scale. Auditing your agentic experiences for completeness and accuracy will not only improve your present-day discovery — and overall commerce AEO efforts — but also lower the risk of agent failures later.
Start with work that pays off today: improve search and product discovery, clean up product data, and start investigating how you can extend agentic experiences to new surfaces. The most important key to success in all this will be picking vendors who can not only clearly lay out their own agentic product roadmap but also help you strategically build yours.
Be wary of vendors who pitch you on all their agentic solutions without helping you identify the best use case for your specific business needs. Also, be wary of those who promise autonomous revenue growth from AI agents without explaining how those agents make informed decisions or what guardrails keep them on track.
Constructor is the AI-driven ecommerce product search and discovery platform that scales, learns, and adapts to how shoppers actually buy. Our central Commerce Reasoning Engine uses reinforcement learning to improve with every shopper interaction, serving the right product to the right person at the right time — everywhere.
By learning directly from real shopper behavior on your ecommerce site (clicks, scrolling, purchases, etc.), Constructor continuously drives stronger performance across the metrics that matter most to your business and improves conversion across every channel onsite and offsite, including agents (like our AI Shopping Agent and Product Insights Agent), answer engines like OpenAI’s ChatGPT, personal agents like Meta’s Muse, search, recommendations, retail media, and more.
Let’s break down how else Constructor is keenly positioned to address the specific needs of department stores.
Constructor is purpose-built for large, enterprise-level multi-category catalogs. Our engine handles cross-category discovery, long-tail and conversational queries, and complex attribute relationships at scale.
Instead of a rules library that becomes harder to manage as the catalog grows, we use behavioral data and reinforcement learning to return relevant results for both head terms and the long tail.
And because the data is in a continual feedback loop that powers all search, product discovery, and agentic commerce solutions, shoppers receive 1:1 personalized experiences no matter where they tune in — website, app, ChatGPT, or elsewhere.
Constructor's Merchant Intelligence Controls and Intelligence suite allows merchandisers to see what's influencing rankings and gives them user-friendly controls to intervene when needed. It combines advanced predictive analytics with A/B testing capabilities, enabling them to save time and strategically optimize for better business results.
And when they need an always-on analyst right in the dashboard to further guide product strategy and research answers to pressing questions, they can access the Merchant Intelligence Agent (MIA). This agent has real-time knowledge of every catalog, rule, setting, and performance data, and can provide explicit feedback, explain why something happened, and then recommend and draft the best next actions.
The same behavioral signals, product data, and transparency that underpin Constructor's search and product discovery experiences also provide the foundation for Constructor’s agentic experiences and AI agents.
Our agentic commerce suite — which embraces emerging technologies like Google’s Universal Commerce Protocol (UCP) — is built to help department stores better tailor their on- and offsite shopping experiences to fast-changing customer needs and market trends, while providing merchandisers with the tools they need to stay top of mind and digitally omnipresent.
To receive valuable insights and relevant information on building a connected agentic commerce experience, sign up for our product roadmap webinar.
Use these questions to evaluate any department-store search and product discovery solution, or to benchmark the one you have.
If your current stack can't answer these questions, talk to experts who can show you where your search and product discovery process, as well as agentic experiences, stand today.