Unleashing the Power of Ecommerce Autocomplete Search

In the highly competitive world of ecommerce, delivering an exceptional customer experience is crucial for driving sales and retaining customers. Central to this experience is search discovery, which plays a critical role in helping users navigate through a vast array of products and information. Autocomplete or predictive search (or search-as-you-type) has revolutionized the way we discover information online, becoming an indispensable tool for search engines and ecommerce websites alike.

In fact, a staggering 96% of major ecommerce sites now offer autocomplete to meet the expectations of their shoppers. By offering real-time suggestions based on users' search terms, autocomplete elevates the search discovery process, saving time and effort for consumers while guiding them towards relevant results. 

Let’s dive into the underlying technologies and processes powering autocomplete, and how businesses can harness this technology to deliver an intelligent search and product discovery experience to users.

How autocomplete works:

As users initiate their search queries, autocomplete (also known as predictive search) comes into play, offering suggestions to complete the query. This advanced feature is powered by sophisticated algorithms and machine learning techniques that examine the user's search history and popular search terms to generate relevant and personalized recommendations.

The suggestions produced by autocomplete are ranked based on a variety of factors, such as:

1. User's search history

Machine learning algorithms analyze an individual's past search behavior, enabling the system to tailor suggestions to the user's preferences and interests, ensuring a more customized search experience. For instance, if a user previously searched for "wireless earbuds" on an ecommerce site, the next time they begin typing "wi" in the search bar, the algorithm might prioritize "wireless earbuds" as a suggestion.

2. Commonly searched terms

Autocomplete leverages large datasets of frequently searched terms across its user base, helping to surface popular and relevant suggestions that cater to a wider audience. For example, during the holiday season, an ecommerce site may notice a surge in searches for "Christmas sweaters." Consequently, autocomplete would prioritize this term for users typing "Chr" in the search bar.

3. Geolocation

Location-based data is incorporated into the autocomplete algorithm, enabling the system to provide region-specific suggestions that are more likely to resonate with users. For example, a user in New York searching for "pizza" on a food delivery platform might receive suggestions for nearby pizzerias, while a user in Chicago would receive suggestions for local deep-dish pizza establishments.

4. Popularity and relevance

The ranking of autocomplete suggestions is influenced by the popularity and relevance of search terms, ensuring that users are presented with the most fitting and sought-after results. Machine learning techniques play a crucial role in optimizing the ranking process.

5. Contextual information

Natural language processing (NLP) techniques are employed by autocomplete algorithms to understand the context and semantics of search queries, which helps generate more accurate and contextually relevant suggestions. For example, a search for "iPhone" on a tech news website might return suggestions like "iPhone 13 review" or "iPhone 13 vs Samsung Galaxy S21," taking into account the context of the website and its content.

Understanding the underlying technology powering autocomplete functionality is essential for businesses aiming to deliver a seamless and engaging search experience in today's competitive digital landscape.

Do you really need autocomplete on your ecommerce site search?

A study by the Baymard Institute found that 70% of ecommerce sites could benefit from improvements in their search functionality. Enhanced search capabilities can lead to a 35% increase in conversions, emphasizing the importance of incorporating features like autocomplete in your ecommerce site search. By offering real-time suggestions based on users' search terms, autocomplete allows customers to find relevant products quickly and easily, reducing friction in the buying process.

Moreover, autocomplete can enhance the user experience on mobile devices, where typing can be cumbersome and screen real estate is limited. In 2022, 54.8% of global ecommerce sales were made through mobile devices which means that brands need to prioritize delivering impressive mobile search experience if they want to compete in the digital commerce landscape. Autocomplete simplifies and accelerates the search process on smaller screens, allowing users to find the information they seek without excessive typing - a major win for smartphone users

Major brands have already embraced autocomplete to provide a more streamlined and enjoyable shopping experience. Let's explore why you need predictive search for your online store's success, along with real-life examples of brands that have benefited from implementing autocomplete.

1. Quick, relevant and accurate search results

Autocomplete saves users time by providing instant suggestions based on their search terms, allowing them to find what they're looking for with fewer keystrokes. For instance, Amazon's autocomplete feature helps users navigate through millions of products by offering tailored suggestions as soon as they start typing.

2. Handling typos and spelling errors

Autocomplete algorithms can correct typos and spelling errors in search queries, ensuring that users receive relevant results even when their input is imperfect. For example, clothing retailer ASOS implemented an autocomplete feature that can handle common misspellings, enabling users to find the desired items despite input errors.

3. Mobile-first search experience

With the growing prevalence of mobile devices, providing a user-friendly search experience is more important than ever. Autocomplete simplifies search on smaller screens by minimizing typing and offering relevant suggestions. Fashion retailer H&M utilizes autocomplete to create a seamless mobile search experience, helping users quickly locate products on their mobile devices.

4. Boosting conversions

Autocomplete can improve the customer experience by quickly guiding users to relevant products, increasing the likelihood of a purchase. Walmart, for example, uses autocomplete to offer relevant suggestions to shoppers, making it easier for them to find the products they need, which in turn boosts conversion rates.

5. Knowing your users better

Analyzing autocomplete data can provide valuable insights into customer behavior and preferences, enabling businesses to refine their marketing strategies and product offerings. Home Depot, a leading home improvement retailer, leverages autocomplete data to identify trending products and categories, allowing them to optimize their inventory and marketing efforts accordingly.

In essence, incorporating autocomplete into your ecommerce site search is essential for providing a seamless user experience, increasing conversions, and gaining valuable customer insights. 

Autocomplete and product discovery: a match made in ecommerce heaven

Autocomplete has transformed the search experience for users, making it faster and more intuitive. Implementing predictive search in your ecommerce site search is vital for improving customer satisfaction, increasing conversions, and ultimately driving business growth. Don't miss out on the opportunity to harness the power of autocomplete – explore Zevi's advanced search solutions and elevate your ecommerce experience today.