Where should you start improving the search experience, and what will bring the most value at different steps? We’ve created an ebook with a detailed roadmap to the advanced search experience.
Personalization is a broad topic in E-commerce that includes a variety of components. One of the most critical ones is the configuration of internal site search and tailoring it as per the needs of each unique customer based on their profile data, purchase history, or preferences.
Site search is sometimes underestimated, yet poor or no site search has a direct impact on brand loyalty. Up to 43% of buyers are using the site's search box. So customizing search results may be a key source for making information available throughout your site and, thus, closing sales more quickly.
According to Google's research, on-site search rejection costs businesses more than $300 billion each year. Another research shows that 80% of customers will exit a website after having a bad search experience. When done right, personalization of site search helps minimize bounce rate, boost conversion rate, promote brand loyalty, and much more.
However, there are lots of details to figure out before personalization. In this article, we will explain how the personalization of site search works and how your business can benefit from it. Besides, we will take a look at practical ways to personalize the customer experience through site search and mainly analyze one of the leading search engines – via Elasticsearch integration.
Before we cover best practices, let's define some terms and answer frequently asked questions about personalization.
Personalization entails adding user-specific signals on top of all other signals to increase the relevance of your product. It is a set of practices of showing different items to different customers based on demographics, intent, preferences, browsing history, past purchases, how they use their device, and other factors.
The purpose of personalization is to enhance the customer experience by anticipating and meeting customers' needs and preferences before they express them. Using data on each individual customer, you can make it simpler for customers to find what they want, based not just on the WHAT of previous purchases or browsing, but also by knowing the WHY behind their preferences.
Relevance is an important metric of personalization, which is determined by delivering personalization within the context of the digital experience. Good personalization is about using the right channel to deliver the right message to the right audience at the right time.
Customers love personalization. By providing an advanced customer experience tailored to your client's demands, you make them feel more compelled by your brand and this directly impacts conversions. As a result, personalization is a current trend in many domains, particularly in site search E-commerce, where it has long been a rallying point, and one of the major E-commerce site search best practices as of 2022.
Let's take a look at the statistics:
The search feature is frequently the first point of contact for visitors to your site. Doing it correctly might be the difference between someone becoming a lifelong customer and leaving your site and going to a competitor. Other than that, customers frequently don't know what they're looking for. They occasionally misspell search queries and require a little nudge in the proper direction.
Personalized search results are more accurate and relevant, allowing customers to quickly obtain the information they require. Search personalization is also known as query understanding. It entails leveraging information about the searcher to either personalize query probability estimation or supplement the question as a signal for query interpretation.
Personalizing site search won't magically improve your product, especially when user intent is clear. However, when user intent is broad, and you offer several options, personalizing site search helps connect customers with relevant information. Site search E-commerce allows organizations to look at each click, search, and every item added to a cart to better learn what each consumer appreciates and predict what they’re most likely to buy.
All customers were once unknown visitors to your site. Even if a customer is not logged in or is visiting the site for the first time, a search may be tailored by browser type, IP location, time of day or year, mobile versus desktop, and other criteria. You can advertise items based on their IP location even if you don't know their gender, age, or purchasing history. For example, in Miami, you may promote different winter clothes than in Minnesota.
Customers who log in to their accounts and have a purchasing history, such as pages viewed, items purchased, gender, age, and so on, are considered known visitors. You may tailor results depending on their profile and site behavior. In general, the more information you know about someone, the more customizable results the system can provide.
Once you have customers' data, you can evaluate which practical method is the best suited to leverage personalization for your website. There are various approaches you may take to implement intelligent site search and customize search queries for your customers.
Behavior-based ranking. According to this model, we build the search that is constantly learning from your customer user experience on your website. In other words, you analyze the behavior of your customers on the website and predict the possible actions they want to make. The user can affect the results with their own choices and browsing history. This model also refers to in-session personalization that seeks to offer relevant experiences from the moment a consumer enters your site. This approach helps us to take into account every action the user performs on the site to learn about their individual search experience. The idea is to determine the user's purpose fast and utilize that knowledge to direct them to the most appropriate product suggestions.
Rule-based ranking. This approach is built by the principle that we first define the audience based on their data such as demographic information, devices, browsers, and similar, and based on such specific parameters we set up rules according to which we show particular products to particular groups of customers that drive search personalization. As a result, customers get results offered based on their profile automatically. This method is also related to history-based personalization which is based on the knowledge of who the customers are and what actions they previously have done on the website. With every new interaction, this model collects information about user intent building its knowledge base. Often, users are added to engagement groups on the guidance or recommendation of the personalization algorithm.
Did you mean & You might also like. This approach allows automated search assistance for each unique customer. First, we analyze user profiles, then make suggestions on what’s better to recommend them, based on the current trends and their profiles. Besides, we can also take into account what other similar user personas are browsing, or purchasing while interacting with the website.
Personalized query probabilities. When the visitors are known, you can use this approach to predict what they will search for. Searchers often repeat queries, and sometimes even within the same session, which then is saved in historical query logs. Using this information, we can analyze a searcher’s interests and personalize query probabilities.
Personalization as an intent signal. Personalization might be used for query rewriting or result ranking. Searchers on a clothing website, for example, are most likely seeking things that they can wear. If we know, or can guess, the gender and measurements of the searcher, we may alter the query to filter or enhance results that fit those criteria.
Custom E-commerce development includes a variety of options for implementing site search personalization. Elasticsearch, a well-known and powerful technology for personalizing site search, allows you to add scalable, relevant search experiences to all of your apps and websites. It offers a wide range of search result customizing possibilities right out of the box.
Technical details behind Elasticsearch
It is a RESTful search and analytics engine. Released in 2010, Elasticsearch is a Java-based API built on Apache Lucene. Elasticsearch personalization indexes your data using keywords. This makes search queries go quickly because Elasticsearch searches through the keywords instead of the full text (this is called an inverted index).
What Elasticsearch offers for site search personalization
What Elasticsearch offers for site search personalization
Here are some of the best examples of site search personalization to learn from.
This website reflects the duality of the classic and contemporary, showcasing a collection of 17th-century art alongside modern pieces and colorful design.
The website offers plenty of tickets to reserve and buy for exhibition programs in the art museum. The goal is to make it easy for customers to use the website and give them useful recommendations.
The Discover section provides some interesting and unexpected methods to view the museum's collection. Visitors can explore artworks by mood, color, medium, or artist, or they can choose Random to be provided with a group of items that are produced casually. A navigation bar on the site helps visitors to find key information, such as the museum's location, opening hours, and daily events, without having to go through the Visit us pages.
It is not necessary to register on the site because the Frans Hals Museum uses an in-session personalization model. The site actively analyzes all requests made by each specific user and outputs not only all relevant results but also those that are similar in style to artists, exhibitions, locations, and more. By opening each of these results, the visitor sees another portion of recommendations that match their interests.
Zenni, as a pioneer in the eyewear market, provides a one-of-a-kind experience to its clients. Augmented reality (AR) is used in this technology, which lets people try on the glasses on their own devices before buying them.
Zenni wanted to create a more engaging customer experience by providing personalized product recommendations through its digital store.
Customers can register and enter their prescription information, such as pupillary distance (single or dual PD) and preferred lens type (single vision, bifocal, progressive, or readers). All search results will be personalized according to the provided parameters.
Other than that, the website offers autocomplete recommendations with visuals by making the search box visible. For example, when users type kids, product photos from the Kids category are displayed.
Ryanair is based in Dublin, and its flights link 37 countries and 225 locations. Ryanair has positioned itself as Europe's greenest airline in recent years.
Ryanair intends to show customized search results to customers to preserve its competitive edge long-term and to continue providing higher value for customers.
The great feature on the website is pre-flll search options based on past searches, purchases, and locations. When customers use the search function again, Ryanair includes a dropdown of suggestions of appropriate terms and categories. This practice works well both for customers that are browsing and those that have arrived on the site with a specific repeat purchase in mind. Browsing visitors will be able to select from a range of relevant categories while returned customers can quickly select the options they require.
Patagonia is a manufacturer of outdoor apparel and equipment for the silent sports of climbing, surfing, skiing and snowboarding, fly fishing, and trail running.
The aim of Patagonia was to offer query predictions for customers that are tailored to the site’s specific content, so users can be confident they’re finding their desired content.
Patagonia provides a great example of personalized autocomplete on E-commerce site search. When a user starts typing, autocomplete displays options in a menu beneath the search. Users can click the recommendation or use the arrow keys to go up and down the suggestion list and choose an alternative.
The following are the important conclusions from the article:
If you are looking for additional guidance on the implementation of site search functionality, the Wise team can provide you with Elasticsearch consulting or launch an audit of your existing search engine. We can also assemble a dedicated team to ensure end-to-end Elasticsearch integration and handle all tech challenges of your specific case.
At Wise Engineering, we implement advanced personalization that helps deliver real-time recommendations to users based on multiple factors, including purchase history, card additions, and more. Contact us to discuss details.