It’s no surprise that shoppers love Amazon or Google. They make finding things so easy and intuitive.

Similarly for any B2C and B2B business with an online store, the most engaging ecommerce site search features should deliver what your shoppers want as well as predict what they might need. Type what you’re looking for in the search box, and watch it appear instantly—like magic. No frustration with irrelevant or zero results. Just fast, accurate search results pointing you to exactly what you want, even if you spelled it wrong or didn’t call it by the exact right words. Unfortunately, many ecommerce sites aren’t quite there yet.

This blog provides the top 10 ecommerce site search features required for a modern online shopping experience. You’ll read about why these capabilities are core to any search application and how they work together to deliver a unified, seamless customer journey.

  1. Scalability is a crucial differentiator. With the always unpredictable currents of social media and viral marketing, Black Friday can come any day these days and systems have to scale up and scale down as traffic, data, and content volume swings from one extreme to the other – all without a huge devops team and unneeded hardware.
  2. Signal capture is the capture of user behavior data or “signals.” In most search applications, signals include events such as user queries, clicks, add-to-carts, purchases, and other similar clickstream data. However, in advanced uses, this may include user location, returns, referring page, or any number of event type data from both the website or any other touchpoint. Being able to capture, analyze, and use signals effectively can help you better understand shoppers’ intent, delivering an engaging online experience while increasing your conversion rates.
  3. Machine learning (ML) powered recommendations aggregate user signals to create a personalized shopping experience for each customer for each visit. Ideally ML models can be imported by your data scientists for the best optimization. With AI technology driving the future of ecommerce, machine learning is a critical component when choosing your ecommerce site search engine.
  4. Business rules management is still important for manually adding, removing, and editing business rules including boost, block, and bury functions using a simple dashboard and interface for making changes, previewing them, and committing them to production. Consider a drag-and-drop interface and dashboards via which your merchandisers can respond to changing tastes and language in real-time, without relying on IT.
  5. Natural language processing (NLP) enables named entity recognition (NER) which recognizes brand names, model numbers, prices, color, size, fabric, styles, and other nouns and adjectives in a product catalog data set. You can combine NLP and machine learning to surface products that align with shoppers’ intent, without them having to search for the exact product names or categories.
  6. Null results resolution fixes queries that don’t return results so shoppers don’t experience a dead end and leave your online store. This feature – a low-hanging fruit one – is crucial to improving your conversions, add-to-carts, and average order values while reducing your bounce rates and frustrating shoppers.
  7. Synonym detection and misspelling correction that go beyond simple word lists to apply machine learning and language analysis to suggest synonyms and misspellings on the fly. A smart system will suggest possible synonym combinations to admins so they can continuously update and understand what shoppers are looking for. Misspelling can also be resolved with head/tail analysis – a machine learning technique that identifies and rewrites underperforming queries to be more like similar, well-performing queries.
  8. Autocomplete/typeahead is among the essential site search features every online shopper expects every time they type in a search box. A rich autocomplete capability extends this to provide instant result sets (products or categories) that can include attributes such as links, prices, and thumbnail images. Further, auto-classification is a more advanced form of typeahead. For example, when a user types “speaker,” then “audio electronics” is automatically selected as a category.
  9. Faceted navigation and range filtering add checkboxes, sliders, and other UI elements for filtering and limiting a search by criteria important to the shopper (size, color, shipping options, price range). This is especially useful for limiting a search to a category or department.
  10. A/B testing and experiment management inform admins whether changes to search results rankings are working as expected. Especially with personalization, recommendations, or other AI techniques, it’s important to determine whether the changes actually improved click-through rates, purchases, or any specified measure of success.

As you move along the ecommerce maturity journey, your site search needs to evolve from simple catalog search to a hyper-personalized customer experience. The 10 site search features discussed above will be foundational to your implementation and improvement strategy. Check out our Ecommerce Search Buyer’s Guide ebook to take a deep dive into these features, maturity stages, and critical metrics to consider as you select your next ecommerce site search software or refine your current one.

In addition to digital touchpoints, we expect that omnichannel integration will be one of the underpinning drivers of the state-of-the-art ecommerce site search features in the coming years. This strategy connects and reflects data from retail websites, mobile apps, loyalty programs, and store visits, in order to give retailers greater visibility across all channels. This empowers brands to provide services like personalized offers and in-store pickup at the closest location, creating one unified shopping experience that your customers love. Want to see how all of these elements work together to deliver that delightful experience? Schedule a demo today.

About Garrett Schwegler

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