As-you-type search means that search results show up with every keystroke, including the very first one. By removing the need to hit “enter”, your users get the information they’re looking for faster and with no hassle, which makes them more likely to return and use your site more. For today’s users “spoiled” by the likes of Google and Amazon, as-you-type search is a given.

Every user is different, and so is every search they perform. Displaying results from all your product and content catalogs (blogs, marketing pages…) is the best way to make sure your users find the best fit for their query. For some users, a direct path to a product is best. Others prefer to read a blog post on the relevant topic before considering the purchase, or to get a quick answer from your FAQ page..

Providing a seamless search experience on any device the user might find themselves on is critical for a modern user experience. In addition to providing the right experience for each device, experiences build up as users engage with a brand on different channels. The actions they take on mobile will make the experience better when they interact with the brand later on desktop or their voice assistant.

Personalization is about taking signals from your user: the conscious signals they are sending you by interacting with your user interface, and the more subconscious, latent signals coming from their past preferences indications. You should optimize search result ranking strategy based on both, and do it subtly, giving users a sense that they are understood but not stalked.

You should leverage your own business metrics to optimize search results, ensuring that the user sees content or product you want them to act on. You could, for example, be tracking conversion rates on your products, and waning to display first the result with the highest conversion rate. Doing this should be easy for anybody on your team.

Being able to respond to the user with the most relevant search result, even when they make a typo, or enter “trousers” when they mean “pants”, is critical to making sure they don’t bounce in frustration. This is where NLP features come in: from typo-tolerance, synonyms, and ignoring plurals, to more complex language-specific features like decompounding.

In the world of modern search, agility is critical. Business teams – e. commerce merchandisers, media content curators, digital marketers and tech product managers – should not be left to log IT tickets and wait for enhancements that may grow the business. They should be able to optimize, test and fine-tune search relevance in real time, with user-friendly tools.

Consumers today want Google- and Amazon-like experiences looking for information, media content and products. Making updates to your search relevance strategies should be pushed live in a matter of seconds, whether you are promoting a breaking piece of news or updating search results when a certain product is out of stock.

Business should be optimizing and testing search relevance in the dark. Instead, they should have search performance data and post-query metrics to inform optimization strategies. A/B testing search should be fast, continuous and iterative – and available to both technical and business team members.

Through search, you can get invaluable feedback on your user’s intent: feedback that accelerates their digital journey, and therefore your revenue. But that’s only true if you’re actively using the signals they are giving you: if you collect, analyze and make decisions based on data like clicks, conversion events, best-and worst-performing queries, filters usage, etc.,

Modern search UX needs to be on par with what users expect from Google or Amazon. Building such experiences from scratch is hard and requires specialized search knowledge and immense amounts of coding. Consider instead prepackaged search widgets and component libraries that make it easy for developers who are not search experts to build first-class UX.

Building cross-platform search is complex because of different physical constraints and specific modes of interaction of different devices. Size and type of search bar, how many results you should show, displaying list, or images – there are complex decisions to be made, and technical requirements along the way. Your search should enable excellent UI and impeccable cross-platform performance.

Modern search should support most popular languages and alphabets, including symbol-based languages such as Chinese, Japanese, and Korean. Additionally, it should handle those languages on the same website/app, meaning that some users could search in French, some in English, and some in Korean. This multilingual “ability” of your search tool should be quick to scale, if your business decides to expand a different country or region.

Hosting, managing and maintaining a search backend is a lot of work: if you do it yourself, it means adding significantly to your infrastructure. You’ll need to provision for servers and engineers to operate those servers, as well as take into account the cost of hosting and maintenance on your own. ‘Search as a Service’ removes this burden.

Your search is as good as the security of your data. Does your search provider host data on a dedicated infrastructure physically separated from the data belonging to the other customers? Are they SOC 2 or SOC 3 compliant? Do they worry about restricted API keys, rate limiting, IP filtering and encryption at rest? Those are just some of the considerations for first-class search security. If you’re looking to build, host and run search yourself, these are all critical considerations for your data.

The question of scaling search often doesn’t come to mind until it’s too late. Even with predictable spikes in search data, such as during the holiday season for e-commerce providers, search infrastructure can easily fail. It is important that your search tool can handle the demands to your business, be it expansion to a new country, a hot piece of news, or a suddenly trending product line.

Machine learning builds libraries of key terms and weights them the entire time it is implemented.  By adding this component you move your search from a basic programming into a truly predictive and more accurate data set.  Starting with a good product understanding and search terms brings you the first level of capability, adding a learning engine that can follow:

• Technical trends and terms typed by your customer
• Learn the relevant results and adjust them as items are typed
• Add new terms or associate terms to existing searches

These take an average fuzzy search to a predictive search methodology.  This is the best practice today.  For a B2B Manufacturer or Distributor, it is critical.  These businesses have more SKU’s, attributes, specifications and materials than B2C businesses. We highly recommend adding an AI; software however, for a b2b company, don’t look at a “closed box” system, it should be open for programmers.  It must be adjustable and technically programmable to actually get the best results.  No AI is fully out of the box ready for your terms and technical needs in a high SKU environment.  It takes expertise.