How to Practically Implement Microservices in Retail

Also Read: Localizing UX is the Next Big Cart Abandonment Fix

No Autocomplete or Suggestions

A “dumb” search box that offers no assistance is a wasted opportunity. Modern users expect autocomplete suggestions as they type. If your DTC site search lacks this, you force users to type full queries and guess keywords, increasing the chance of typos or mismatches.

Autocomplete not only speeds up the search process but also guides users by suggesting popular products or categories. For example, if a user types “dress”, a good autocomplete will drop down suggestions like “summer dress”, “red cocktail dress”, or specific product names. Without autocomplete, you miss the chance to steer customers to what they might be looking for.

Lesson: Implement an autocomplete feature that suggests relevant search terms, products, or even images as the user types.

Poor Handling of Synonyms and Misspellings

A very common mistake is relying on exact-match keyword search. If your search algorithm can’t interpret synonyms, plurals, or slight misspellings, customers will see those dreaded “No results found” pages far too often. For instance, a shopper might search for “sofa” and get zero results because your products are labeled “couch,” or they type “tshirt” and your catalog only matches “t-shirt.” Shoppers often use different words (or make typos) for the same intent.

Lesson: Ensure your search can tolerate spelling errors and recognize equivalent terms.

Also Read: Why Is My Conversion Rate Dropping Despite Steady Traffic?

Irrelevant Results (Bad Ranking Logic)

Even when search returns results, are they the right results? A common issue is poor relevance ranking – for example, a query for “organic shampoo” might return a long list where the top items aren’t even shampoo, or they’re irrelevant products that happen to contain one of the words. This often happens with basic search engines that sort by trivial criteria or don’t understand context. If users get a page of products that don’t match their intent, they will bounce.

Lesson: Don’t let your search be “dumb.” It should prioritize relevancy – taking into account product titles, descriptions, popularity, and user intent – rather than just doing a raw text match. If your analytics show users often refine their search or exit after seeing results, it’s a sign your relevancy algorithm needs improvement.

No “Zero Results” Fallback Plan

One of the worst dead-ends in ecommerce is a blank search results page that says “0 results for your query.” Often, this happens because of the issues above (typos, synonyms, etc.) or when the catalog truly doesn’t have what was searched. But many DTC sites make the mistake of leaving users at this dead end with no guidance.

Lesson: Never leave a zero-results page blank. At minimum, provide helpful suggestions like “Check your spelling or try a simpler term,” or display alternative products and categories. Better yet, proactively avoid zero results by implementing fuzzy matching (showing the closest matching items). If a term isn’t found, show popular products or offer to contact support. A “no results” page can even be turned into a merchandising opportunity by showcasing best-sellers or new arrivals so the session isn’t wasted.

Also Read: How ‘Zero Click’ Product Pages Are Changing Conversion Strategy

Lack of Filtering and Sorting Options

Some DTC brands treat search results pages too simplistically – just a dump of items with no way to refine. If a user searches a broad term like “shoes” or “moisturizer,” they might get hundreds of results. Without the ability to narrow down (e.g. filter by size, color, price, category) or sort (by relevance, price, popularity), the shopper can get overwhelmed.

Lesson: Treat your search results page like a mini category page – include facet filters (brand, category, price range, attributes) and sorting controls. This allows shoppers to drill down to exactly what they want if the initial result set is too broad. It’s especially essential for DTC verticals like fashion (think filters for size, style, color) and electronics (filters for specs, features).

Slow Search Performance

If a shopper types a query and your site takes several seconds to load results or suggestions, you’re introducing friction. Slow search responses often result from unoptimized databases or servers. Mobile users in particular might abandon if results don’t appear almost instantly.

Lesson: Optimize your search for speed. This might mean upgrading your search technology, indexing data for quicker lookup, or caching frequent queries. The goal should be to deliver results in a blink (under a second) so that users feel the search is responsive and reliable.

Also Read: Why Headless Commerce Matters

Ignoring Mobile Search UX

DTC brands that design a decent desktop search often drop the ball on mobile. If the search icon is hard to tap, or the results overlay is difficult to navigate on a phone screen, you’ll lose mobile shoppers (who are a huge portion of traffic). Mobile search needs to account for smaller screens and touch input. Additionally, because typing on mobile is prone to errors, error tolerance becomes even more critical.

Lesson: Make sure your internal search is fully mobile-optimized. This includes a prominent search icon, a fullscreen search interface that’s easy to read, big tap targets for suggestions, and robust autocorrect for fat-finger typos. You might also consider voice search input for mobile users as a convenience feature.

Not Analyzing Search Data

Finally, a strategic mistake many DTC teams make is failing to monitor and learn from site search analytics. Your internal search logs are a goldmine of customer intent data – they show exactly what users want, in their own words. If no one is looking at this data, you’re missing opportunities. For example, you might discover that dozens of people search for “gift card” on your site and you have none, or that a specific product is constantly searched (indicating high interest).

Lesson: Regularly review what users are searching for, which queries return zero results, and which searches lead to purchases. This will inform everything from adding new products (if there’s demand) to adjusting your search synonyms. It will also help you continuously improve the search experience –for instance, if you see many people search for “FAQ” or “shipping”, you might integrate content results or quick answers into the search.

Also Read: Why Smart Retailers Are Simplifying the Homepage

Knowing the problems is half the battle. Now let’s focus on practical solutions. Here are concrete tactics DTC brands can implement to turn their internal site search into a conversion powerhouse:

Make the Search Bar Prominent and User-Friendly

Ensure the search field is impossible to miss on your site’s layout. Use a clear icon and consider a placeholder text like “Search products…” to invite usage. For mobile sites, put the search icon in the header on every page. The easier it is to start a search, the more people will use it.

This is a quick win: simply emphasizing the search bar on your homepage or landing pages can drive more engagement. Make sure the search box is also functional: allow submitting by pressing enter or tapping a search icon, and keep the text in the box after search (so users can refine their query without retyping it).

Implement Autocomplete and Query Suggestions

Add an autocomplete dropdown that appears as the user types in the search box. This should dynamically suggest likely search terms, product names, categories, or even display thumbnail previews of products. A rich autocomplete speeds up the process and guides users toward actual items you carry.

For example, if a user types “moisturizer”, the suggestions might include “moisturizer for oily skin”, specific product lines, or relevant content like “How to choose a moisturizer (Blog)”. This way, even if they don’t know the exact product name, the system helps them along. Autocomplete can also correct spelling mistakes on the fly (“Did you mean…”) before the user even hits enter. Given how many shoppers expect this feature, implementing it can significantly improve search success rates and reduce exits.

Also Read: How Gen Z is Forcing Retailers to Rethink Digital Strategy

Build a Synonym Dictionary (and Use Fuzzy Matching)

To avoid zero-results situations, configure your search to handle synonyms, variations, and misspellings. Start by analyzing your search logs for queries that returned no results or poor results – often you’ll find common patterns (e.g., “tee” vs “t-shirt”, “laptop case” vs “notebook sleeve”).

Create a synonym dictionary mapping these terms to the ones your catalog uses. Many modern search platforms let you define synonyms (so that “couch” searches also match “sofa”, etc.). Additionally, enable fuzzy search or auto-correction to catch typographical errors. A user shouldn’t have to type a query perfectly to find what they need – if they type “iphon charger”, your search should reasonably assume they meant “iPhone charger” even if the spelling differs.

Optimize Relevance Ranking (Boost the Best Matches)

Improving the ranking logic of your search results is one of the most impactful changes you can make. Instead of a basic text match, use a ranking algorithm that factors in relevancy signals. These signals can include: keyword match (product title, description, tags), but also product popularity, conversion rate, in-stock status, and even personalization (more on that shortly).

For example, if someone searches for “running shoes”, your top results might be your best-selling running shoe styles (because popularity often correlates with what most shoppers want), rather than an obscure product that just has “running” in its description. Many DTC brands also choose to manually “pin” or boost certain results for key search terms – this is sometimes called search merchandising. For instance, you might always want your flagship product to appear first for a category search, or you might boost items that have higher margins.

Search optimization (sometimes termed “searchandising”) allows you to blend relevance with business goals. Audit some of your common searches – are the top results truly what a customer likely wanted? If not, adjust your search algorithm settings (or rules in your search tool) to promote more relevant or profitable items. Continually test and tweak relevance by watching search click-through rates and conversion rates. When relevance improves, conversions go up – shoppers find the right product faster and buy with less friction.

Also Read: Break Purchase Hesitation With Micro-Moments in the Funnel

Enable Faceted Search and Filters

As mentioned in the mistakes, giving users the power to refine search results is very important, especially if you have a broad catalog. Implement a sidebar or top menu of filters on the search results page. Typical filters include things like category, brand, price range, size, color, ratings, etc., depending on your product vertical.

Also consider context-specific filters; for example, if someone searches “laptops,” show filters for screen size, RAM, etc., but if they search “running shoes,” show filters for shoe size, gender, and so on. In addition to filters, a sort-by dropdown (e.g. sort by price low-to-high, newest arrivals, customer rating) can help users reorganize results to their liking.

Providing these refinements significantly improves the chance that a shopper will find a product that fits their specific needs. Work with your development team or search provider to implement faceted navigation on the search results. Start with the most important attributes (the ones customers frequently care about) and add more as needed. Make sure the filters are relevant to the query context if possible (dynamic filtering).

Design for Zero Results (Always Offer Alternatives)

Despite your best efforts, there will be times when a search query doesn’t match any product exactly – maybe the item is out of stock or outside your offerings. Plan for this scenario. Instead of a blank “no results” page, design a helpful zero-results page that keeps the visitors engaged. Tactics for this include:

• Show a message like “Sorry, we couldn’t find that. Here are some suggestions:”
• Provide links to top-level categories or a site map (“Try these departments…”).
• Display top-selling products or new arrivals (which might interest any shopper).
• If you have a live chat or chatbot, prompt the user “Need help finding something?” on the no-results page.
• Encourage a broader search: “Try searching for a more general term.” Perhaps even keep their query in the search box and allow them to edit it easily (so they don’t have to retype from scratch).

The goal is to turn a dead end into a detour that can still lead to a sale. Create a custom no-results component on your site. Populate it with a few product recommendations (popular items or those related to the query if possible), a search tips section, and a call-to-action like contacting support.

Also Read: Do Shoppers Love or Fear Hyper-Personalization?

Leverage Search Analytics and Tune Continuously

Once you start making these improvements, it’s important to treat site search as an ongoing, data-driven project. Set up tracking for search queries in your analytics (if you haven’t already via Google Analytics or similar) to capture metrics like: top search queries, frequency of searches, zero-result rates, search exit rates (people who leave after searching), etc. Use these insights to continuously tune your search.

For example, if “promo code” is a frequent search on your DTC site, that’s a hint to make your discount code or sale information easier to find (maybe create a dedicated page or FAQ that your search can surface). If a specific product is trending in searches, ensure it’s prominently featured. Also, identify queries with poor performance – if a high-volume search term has a low click-through or conversion rate, investigate why (maybe the results are irrelevant or there’s no satisfying result).

By iterating in this way, you create a feedback loop: better search → more conversions → more data to improve search further. Schedule a monthly (or bi-weekly) review of site search reports. Adjust your synonym list, add redirects or merchandising rules for oddball queries, and consider A/B testing changes (some advanced search platforms allow A/B testing different search algorithms or configurations).

Use Merchandising Rules (Searchandizing)

“Searchandizing” is the practice of merchandising within search results – essentially, customizing the outcome for business objectives. We touched on this in ranking optimization, but let’s emphasize specific tactics:

• Boosting Promotion or Seasonal Items: If you have a holiday sale or a seasonal collection, you can set rules so that a search for generic terms will sprinkle in some of those promotional items. E.g., a search for “gifts” in December might show your holiday gift bundle as the first result.
• Redirecting Certain Searches: For some queries, you might not want the standard product grid at all. For example, searching “store locator” or “shipping policy” might be better served by taking the user to an informational page. You can set up rules to handle these cases.
• Banner or Content Integration: Some brands integrate marketing banners at the top of search results for certain keywords (e.g., searching “membership” could show a banner about your loyalty program). Just be careful that this augments rather than distracts from showing products.
• Upselling and Cross-Selling: Like the Target DSLR example, consider showing higher-end alternatives or related items in results. Another case is showing accessories: a search for a phone could also display a popular phone case. This can increase the average order value.
• Demoting Unprofitable or Low-Stock Items: Conversely, you might push products that are nearly out of stock lower down, or hide items with low margins in favor of ones with better margins (as long as relevance to the query is still maintained).

These kinds of rules can be implemented via your search engine’s admin (if supported) or with some custom logic. The idea is to merge merchandising strategy with search intent. When done right, it increases conversion and often boosts the basket size, because you’re showing customers items in the search results that you want them to see (and that they are likely interested in).

Identify your top 10–20 search queries (especially category-level terms like “shoes”, “skincare”, etc.) and decide if any special merchandising would make sense for each. Then use your search solution’s features to create those boosts or redirects. Monitor the impact on click-through and sales for those queries and adjust as needed.

Also Read: How to Handle SKUs with No Historical Data (e.g., New Drops, Collabs)

Personalize Search Results (if possible)

One advanced tactic is incorporating personalization into search. This means the results take into account the individual user’s behavior or profile. For example, if a returning customer typically buys men’s clothing, a search for “jackets” might show men’s jackets first, whereas a different user might see women’s jackets.

Personalization can also use browsing history (e.g., if you viewed electronics, a search for “apple” might prioritize electronics over fruit). If you can implement even basic personalization, it can set your UX apart. Some AI-driven search engines will do this out of the box, or you can segment customers (like by gender preference if known, or by past purchases) and tweak results accordingly.

Evaluate if your current platform supports personalized search ranking. If not, consider whether the investment in a more advanced solution is justified by your scale – for many mid-sized DTC brands, it can be. Start small, perhaps by personalizing results for logged-in users vs. new visitors, and see if it lifts conversion.

Invest in AI-Powered Search Technology

Many of the above tactics – from autocomplete and typo tolerance to personalized, intent-based results – are made much easier by modern AI-powered site search solutions. While we won’t endorse a specific tool here, it’s worth noting that the field of site search has evolved rapidly. AI and machine learning can now interpret natural language queries, learn from user behavior to improve relevance, and even handle things like voice or visual search.

If your current search platform is outdated or your CMS’s built-in search is too basic, upgrading to a dedicated tech partner can be a game-changer. These solutions come with features like NLP (natural language processing) that understands queries in plain English (e.g., a query like “best camera under $500” could actually be understood and filtered accordingly), vector search for semantic matching (so “running sneakers” matches “jogging shoes” even if the exact word isn’t present). Crucially, they save your team the effort of reinventing the wheel – you get enterprise-grade search without having to build it all in-house.

Many DTC brands find that outsourcing search to a specialized provider yields a quick ROI. In short, better search = more sales, and AI is the enabler for “better” in many cases. Assess your site search’s current capabilities vs. what’s available in the market. If features like typo correction, synonyms, and dynamic ranking are lacking, it may be time to consider an upgrade. Even if budget is a concern, remember that the revenue uplift from improved search can be substantial (often in double-digit percentage increases), easily justifying the investment.

Optimizing your internal site search is one of the most practical, high-ROI moves a DTC brand can make to recover lost ecommerce sales. It requires some focus and possibly new tools, but the payoff is clear: more of your hard-won site visitors will convert into buyers. The impact on revenue can be significant. You’ll reduce bounce rates from search pages, increase add-to-cart and conversion rates, and likely see overall sales lift as more visitors find what they need. Importantly, you’ll also be delivering a better customer experience, which improves your brand reputation in the long run.