Search engines are constantly evolving with new features and capabilities released all the time. As compute power increases, features like AI and personalization can become faster, more feature-rich, and less expensive.
The future for site search engines is looking bright, and there are a lot of great new features around the corner! In this article, we take a look at seven features to watch over the next year, including:
- Voice search
- Visual search
- Product and shelf location
- Omnichannel personalization
- AR/VR search
- Hybrid search
Voice assistants are transforming the way people shop online. Consumers don't need to type or click to get what they want, they can simply ask. Voice search is being used more and more, and with it comes the opportunity for retailers to differentiate their site from their competitors.
Voice search can be a powerful tool for retailers to increase customer engagement, reduce customer service calls, and improve conversion rates. Additionally, voice search platforms are giving retailers the ability to collect more first-party data about customers and their online shopping habits.
Adding voice search to your site isn’t too different from adding text-based site search. The actual speech to voice recognition is handled by the user’s mobile device which converts the voice query into text. The search engine does the rest.
However, optimizing for voice search may require some changes on your site. Voice search is optimized a little differently because the kinds of queries are different.
As mentioned, voice searches tend to be worded differently than text searches. They’re longer, more specific, and more likely to be full questions than simple keywords. When we speak, we’re used to using full sentences with more detail than when we type. - via Hubspot
Bonus benefit: according to at least one article, if your site is optimized for voice search, Google may give it a boost too in organic search listings.
Visual search technology has been around for more than 20 years. Extremely visual sites like Unsplash and Pinterest have made great use of it, but it’s been relatively slow to be adopted for other use cases.
That’s about to change for a few reasons. One is that Google is aggressively marketing their own flavor of visual search that will allow anyone to take a photo and shop for the item right from their phones.
Another — and arguably something that scales more — is that the barrier to build visual search applications is dropping dramatically. Better technologies and APIs will allow anyone to design visual search for a specific product collection or website.
For example, we’re introducing our own flavor of visual search powered by Neuralsearch. The same technology behind Neuralsearch — neural hashes — can be applied to images. Image data is mapped by the search engine to help it find similar images.
However you ultimately design visual search, by harnessing its power you can increase the value of your content, differentiate your e-commerce buying experience, and help customers find what they're looking for faster.
Product and shelf location
Nowadays it's common for online sellers to show customers how much inventory remains. To go one step further, you can display which physical stores have inventory. Want something even more precise? Now, you can even tell searchers exactly which shelf location in the store to find the items they’re looking for.
Retailers like Home Depot have been doing this for a while now. Provided that each store is configured the same — same layout, same number of aisles, etc — identifying a shelf location gets much easier.
This is a smart and effective way for retailers to provide value to their customers for their BOPIS business (buy online, pick up in-store) and to differentiate their business from the competition.
In the new WBR Insights report, The Role of On-Site Search in a Changing E-commerce Industry, e-commerce executives said personalization (48%) and recommendations (47%) were a top priority for their business. There’s good reason for it. Companies using advanced personalization report a $20 return for every $1 spent. Consumers want personalized experiences, too: 91% of consumers say they are more likely to shop with brands that include relevant offers, information, and recommendations.
Personalization data comes from just about everywhere — in store, websites, and social media platforms, or in other words, anywhere that customers interact with retailers and brands.
Many companies are now building data warehouses to capture interactions across channels to use that data for better personalization and recommendations. If you know a customer’s search, purchase, and viewing history, for example, you can personalize on-site search results and product recommendations.
Merchandising is very important for any retail business, and that includes e-commerce. Searchandising is the process of merchandising through on-site search results and product recommendations. For example, if someone is searching for “backpack” during a back to school sale, you can prioritize results to include the merchandise in your campaign.
In brick and mortar stores, retailers can build window displays, assemble in-store product arrangements, or display signage to help sell more products — be it for a special sale, to reduce inventory, or show off new arrivals.
In the online world, a website's standard merchandising might include things such as:
- Designing catalog landing pages
- Promoting certain products in search results
- Adding sales banners on product thumbnails or pages
Connecting search results to your merchandising campaigns is now easier than ever because search providers (like Search.io!) frequently offer built-in merchandising capabilities of their own. The same rules you build-in for your campaign can be applied to both search results and throughout your site’s category page content. It offers merchants a vastly simpler and faster way to configure campaigns from a single dashboard.
In an age where digital experiences are taking over, it is not surprising to see more and more companies looking for innovative ways to enhance their customer experience.
One of the most recent developments in this area has been the introduction of augmented reality (AR) and virtual reality (VR) to e-commerce.
Companies are using these technologies to create interactive experiences for their customers. For example, in the case of AR, it may allow customers to see what a product would look like in their own home or office, or to try on virtual clothing or makeup. With VR, applications can range, but it might include the ability for a shopper to go through a rack of clothing or other virtual items to pick out the ones they want.
This technology also allows customers to interact with products before they buy them and so make sure that they are making the right purchase decision.
Big, innovative companies will be the first adopters. For example, Walmart announced the addition of AR tools to its website, allowing consumers to see selected furniture in their space while online shopping. Snapchat has featured AR capabilities for years to allow its users the ability to interact and customize with everyday items in photos.
As for product search, it will be embedded into AR and VR using voice and visual search technologies. Voice and visual search lend themselves to AR/VR where users can either speak their search aloud or point to the object, color, or style they’re interested in searching for. The biggest hurdle will be integrating voice search and voice applications with AR/VR technologies. However, those barriers are coming down as the technologies mature.
There are, broadly speaking, two kinds of search technologies:
- Keyword search: the granddaddy of search, keyword search is really fast and very good for certain types of queries
- AI search: the new kid on the block, AI search understands concepts and complex or subtle kinds of queries.
But the battle between keyword search and AI search isn't an either/or. It's both. Put both technologies together and what you get is hybrid search... a 1 + 1 = 3 feature that's greater than the sum of its parts.
Hybrid search results are more accurate than just one type of search engine. Whereas keyword search is good at single-word queries or exact matches, AI excels at longer queries, symptoms, and related ideas.
Relevance is a measure of how well search results match the query and user intent. Hybrid search delivers better relevance for all types of queries than either keyword-only or AI-only search.
Neuralsearch is an example of a hybrid search engine that runs both keyword and AI engines during query time. For example, in the screenshots above we can see results for two different searches — one with a brand name and one without. The relevance is a combination of neural AI and keyword scoring (and in one case, a dynamic boost has been applied to one result). The final score determines what position the product is displayed in results.
By adjusting the weight of neural or keyword relevance, customers can change the order of results and test what weights are best for driving conversions.
What’s clear from our testing is that hybrid search offers better relevance and smarter results than one or the other for most collections. Moreover, it virtually eliminates the need to write synonyms or relevance rules.
There are some great developments happening in search and discovery. Learn how your organization can benefit from site search today: signup for a free 14-day trial or contact us for a personalized demo.