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.
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.
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:
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.
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:
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.
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:
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.
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.
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.
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
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:
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.