Responsible merchandising is responsive to change. The ability to roll with shifts in consumer behavior, supply chains, tax changes and tariffs is the key to keep converting when conditions are in flux.
When these factors are significant, they force ecommerce merchants to rethink everything from procurement and pricing to promotions and personalization. Accordingly, it’s important to ensure search and discovery experiences don’t suffer due to challenges like inventory shortages and adapt fast to rapidly shifting shopping behaviors.
The following is a playbook for tackling these uncertainties. Much of the heavy lifting is handled automatically within Constructor, thanks to AI, machine learning, and advanced search engineering. But there are also steps you can take to enrich the data and merchandising you provide Constructor to optimize for specific ways your business may be affected.
Challenge: Shifting Consumer Behavior
Times of economic uncertainty impact customer behavior in many ways — such as when consumers tighten their wallets, rollback spending, or even start “panic buying” in anticipation of price increases or product shortages.
The key is to detect behavior shifts early and ensure your search and discovery application is in lockstep. Let’s look at some “digital body language” clues that customers may be reacting to economic changes.
Browse & search behaviors
- Applying price filters (e.g., “Under $50”, “$50–$100”)
- Sorting by price (usually “low to high”)
- Searching using price-inclusive queries (e.g., “cheap running shoes,” “affordable moisturizer,” “under $25 gift”)
- Clicking into category pages that explicitly call out deals or budget-friendly options (e.g., “Sale”, “Budget buys,” “Value sets”)
- Ignoring premium products or skips over items with higher price tags when scrolling
- Searching for substitute products with lower price points
- Lingering longer on lower-priced product pages
- Avoiding clicking sponsored or featured items if they appear premium-priced
- Returning to price-comparison sites or Google Shopping to validate pricing
- Using voice or Chat UI to ask: “What’s the cheapest X?” or “Show me X under $20”
- Opening more product detail pages (PDPs) per session, indicating value comparison behavior
- Shorter browsing sessions, indicating shoppers are spending less time exploring and more time on intentional purchases
Purchase behaviors
No matter what you sell, if your customer file starts changing behaviors, you may notice declines in average order values (AOVs) and items per order, as well as a drop in SKU diversity within each order.
Within product categories, shoppers who are trading down for economic reasons may show these common behaviors:
Apparel
- Choosing fewer designer or premium brands, shifting toward house brands or lower-tier labels
- Opting for basics (e.g., t-shirts, leggings) over statement or trend-forward pieces
- Buying single seasonal items instead of full wardrobe refreshes
- Picking sale colorways or sizes (the discounted SKUs) instead of preferred ones
Health & beauty
- Purchasing fewer high-margin or non-essential items (e.g., setting spray, face mist, fragrance layering sets)
- Limiting spend to essentials or replenishments versus new drops
- Skipping limited-edition collections and seasonal launches
- Using Buy Now, Pay Later (BNPL) services for moderate-sized orders
- Building and saving carts to purchase during sitewide sale events only
Grocery and pet
- Switching to store-brand or private label instead of name-brand products
- Switching from premium SKUs to conventional alternatives (within a product category)
- Choosing frozen or shelf-stable items instead of fresh or perishable versions
- Purchasing smaller pack sizes or shifts from single units to value packs
- Swapping out specialty or niche products (e.g., oat milk, grass-fed beef, imported cheese) for more basic counterparts
- Moving from branded ready meals to ingredients for home-cooking (cheaper per meal)
How to respond
Many of the tools you need to respond to rapid changes in customer behavior and product attractiveness can be found in Constructor. But there are a few additional tunings you can manually apply in our dashboard to customize our platform to your business strategies.
What Constructor updates automatically
- Real-time behavioral data: Our Attractiveness Scores are recalculated constantly based on live clickstream data so search and browse results evolve as customer preferences shift
- Price sensitivity detection: Our algorithms adjust ranking in part based on price elasticity, meaning if shoppers begin valuing cost over brand, the on- and offsite experience can reflect that in real time if desired
- Intent signals: Price sorts, filter usage, and engagement signals feed back into the ranking algorithm, ensuring that the most contextually relevant products rise to the top
How you can enhance Constructor for your business
As you make strategic business decisions at the C-level in response to tariffs, you can build this context into Constructor to further optimize your merchandising logic. For example:
- Create new (hidden) attribute fields for conditions like “margin,” “country of origin,” “at risk,” “supplier reliability,” or “tariff impact” (these will NOT be visible to customers)
- Categorize inventory groups that have importance, such as high/low, distance/location, etc.
- Apply ‘boost and bury’ rules to these new attribute fields and inventory groups according to your business requirements (as well as allow/block lists based on margin data)
- Consider refreshing price bands for faceted navigation, boost/bury, and personalization rules (e.g. “high intent = filtering by $200+” becomes $250+)
- Consider boosting private label or white label SKUs
- Consider displaying “Made in [domestic country]” badges in product lists and on product pages, and promoting it as a filter or creating dedicated recommendation pods
Challenge: Inventory in Flux
A number of factors may impact your catalog assortment and product availability in 2025 and beyond. Increasing cost of goods and cancelled purchase orders across the supply chain put a squeeze on inventory. You may be faced with fewer new seasonal items, dwindling evergreen inventory, or uncertain replenishment of best sellers.
This means your search and discovery strategies need to accommodate inventory shortages and stockouts in real time. Returning visitors going directly to search to rebuy their faves need relevant and attractive substitutes, and they need them surfaced at the right touchpoints. Casual shoppers (in browse mode) still want choice, great deals, and a guided selling experience — even with fewer options.
Conversely, recessionary pressures, tariffs, or inflation may create conditions that result in overstock, which requires a different strategy. Aging inventory needs visibility to move – but not at the expense of showing what’s most appealing to each customer.
How to respond
Now more than ever, it’s critical that your search application go beyond relevance and be able to unpack customer intent to rank products in search and browse pages by attractiveness, or the ability to connect customers with products they're likely to purchase.
Constructor provides several tools to optimize shopping experiences while inventory is uncertain:
Leave “no product behind” with Attribute Enrichment
Many search engines simply match products to search queries (or populate dynamic category pages) based on product data feeds, including product attributes (such as color, size, material, or style) captured in relevant fields. But product attribute data is often missing, inaccurate, or contains keywords that could be expanded upon to include synonyms.
To solve this problem, we’ve built Attribute Enrichment to automatically generate and correct product data using a mix of AI, machine vision, and text classification. We also use machine learning to detect high-demand attributes from search query history and prioritize them during enrichment.
This solution finally allows companies to predict attractiveness when personalizing product rankings to shopper segments and individual users. It also allows them to match products to the semantic meaning of every search query and ensure no relevant or attractive options are missing from search, browse, and filtered results.
This leads to better user experiences, higher conversions, and improved merchandiser productivity.
👉Learn more about how we’ve built our extensive domain-aware attribute taxonomy and trained our Attribute Enrichment AI.
Reduce “no results found” with Cognitive Embeddings Search
No shopper wants to encounter zero results, and no merchant wants to serve an empty page! In fact, it’s common for ecommerce search engines to simply show something rather than nothing and pull partial matches (for example, using an “or” operator to return matches for every keyword in the query, so a search for “white gold tennis bracelet” returns white products, gold products, tennis items, and bracelets).
This tactic attempts to gloss over the fact the engine couldn’t find relevant and attractive semantic matches that might appeal to the customer, regardless of the actual keywords input to the search box.
In Constructor, we use our proprietary Cognitive Embeddings Search to both reduce the number of “no results found” conditions and improve the quality of close-match results. This enhanced vector-based algorithm involves layers of deep learning that connect the dots between keywords and products: keyword-to-keyword affinities, product-to-product affinities, and keyword-to-product affinities.
This means Constructor can understand the relationship between a term like "disco outfit” and “sequin tassel shift dress,” “flare sleeved tie front blouse,” and “paisley boot cut jumpsuit” to build a smart collection or match customer queries. It also means Constructor understands synonym-based intent without your team setting up an exhaustive list of synonyms in the back end.
Cognitive Embeddings also leverages “token skipping” to effectively produce results for long-tail search queries by removing bits of words that are less important to user intent. One problem conventional search applications have with long-tail search terms is every single keyword is queried against the product database. For very long terms, this can lead to slow response times, increasing the risk of user abandonment. Rather than leave the user in limbo for too long, these engines will trigger a “kill switch” to return zero results — which ironically increases the risk of abandonment.
Token skipping enables Constructor to return richer results faster without losing context. This is especially important for close-to-converting shoppers looking for a specific product to repurchase. They’ll often use the exact, full-length product name (or close to it). Constructor can both find that product listing faster and map it to close-substitutes based on enriched attributes and vector search.
Remember, in times of economic uncertainty, a shopper’s “holy grail” products may be out of stock or have increased in price enough to inspire your shopper to consider alternatives.
👉Learn more about the ins-and-outs of Cognitive Embeddings Search.
Help customers find substitutes with guided selling
Besides search and browse, Constructor supports two types of guided selling experiences: product quizzes and AI Shopping Agent.
With quizzes, you can create any number of product finder flows that leverage both your merchandising expertise (you set up the quiz questions) and Constructor’s AI machine learning. The product quiz filters and matches results as the user responds to questions for highly personalized results (and captures zero-party data in the process).
Quizzes can help save out-of-stock sales and build trust and confidence in suggested substitute items, especially when your quiz asks the right attribute-related questions that matter to the purchase decision.
We recommend promoting your quizzes with grid slot banners right in the product list for relevant search and browse experiences using Constructor’s searchandising rules, like this beauty retailer:
Constructor’s AI Shopping Agent provides a more conversational option for shoppers who can ask anything of the LLM chatbot, just like ChatGPT.
Not only can the agent answer natural language input like “I’m looking for a foundation with a similar ingredient profile to Estee Lauder Double Wear that matches shade 5C1 and does not contain SPF or mineral oil,” it can also address customer questions about tariffs, price adjustments, and inventory replenishment if you provide this information to Constructor.
Use visual or related search for out-of-stock products
With the Constructor API, you can build custom experiences that address specific use cases. One opportunity is to serve visually similar (or closely related by attributes) options when products are shown to be out of stock at the product list level, the product page, wishlist, or saved shopping cart.
For example, this beauty retailer has built this into both its wishlist and product list pages with Constructor:
View similar products from a Wish List
View similar products from a Product List
Algorithmic Resilience and Continuous Optimization
The Constructor team is always running continuous optimization tests in partnership with our customers. These experiments allow us to respond quickly to market shifts without overhauling your entire configuration.
If your business is being affected by any of these economic conditions, let your Customer Success Manager know. We can prioritize these tests focused on:
- Boosting US-based inventory (e.g. in the case of tariffs)
- Adjusting for margin sensitivity
- Localizing experiences for international markets (which may be less affected by tariffs, inventory availability, or other costs)
Your Profit-Protecting Playbook, Powered by Constructor
Economic uncertainties are a challenge, but they’re also a litmus test for operational agility. With the right data strategy and a flexible search and discovery engine, your storefront can respond faster than your competitors.
Constructor gives you the ability to adapt across all fronts: shopper experience, product visibility, algorithmic ranking, and merchandising control. In a climate where so much is uncertain, that flexibility is not just nice to have, it’s essential.
Ready to future-proof your product discovery experience? Let’s talk.