Did you know that over 68% of customers leave a website because of poor search experiences? This is one hidden problem that is most often overlooked - search abandonment.
Search abandonment happens when customers leave your online store because they cannot find what they are looking for. As they are not able to find what they need, they do not stick around on your website for buying other items or exploring your offerings. No wonder the eCommerce industry has admitted to losing about $2 trillion every year due to search abandonment.
In this post, we will explore search abandonment in detail and learn how to tackle this challenge with AI-powered solutions.
What Is Search Abandonment?
Search abandonment occurs when customers leave your website because they are not able to find what they are looking for often within the first few seconds of landing. They leave your online store without taking any action like exploring the product, buying the product, or simply interacting with your website's content. This results in the loss of sales opportunities and delivers a bad customer experience.
Let us understand this with an example. Imagine a potential customer lands on your website with the intent of purchasing a lavender-scented candle. The customer enters the query 'lavender scented candle' in the search box because this is the only product they wish to buy from your store. Now, if the customer just enters the search query, does not click on any of the products displayed, and exits your website, it is called abandoning the search.
Search abandonment is a serious issue that most eCommerce businesses are facing today. A study by Google Cloud revealed that 92% of consumers around the globe received search results that were irrelevant to what they were looking for. Further, 82% of these consumers prefer avoiding websites where they have experienced search difficulties altogether.
5 Common Reasons for Search Abandonment
Understanding search abandonment meaning is just the first step. Let us understand why customers abandon their searches.
The main reason for search abandonment is that the search function failed to meet the customer's expectations.
It could be due to -
Irrelevant or poor search results - In such situations, the search algorithm does not deliver results that match your customers' queries. This usually happens because of ineffective keyword/query matching, improper product descriptions, inadequate product tagging, and lack of support for conversational queries.
Poor query understanding - The search function is unable to understand customer queries because of spelling mistakes, singular/plural forms, abbreviations, or regional terms.
No search results found - Stumbling upon a no results found message may give your customers an impression that the product is not available or their search query was not understood. This could happen because your product catalog was not updated or the product synonyms were not configured.
Overwhelming search results - Showcasing too many irrelevant search results may overwhelm your customers. This can be further worsened by not providing an easy means to navigate the search results, leading to search abandonment.
Slow search performance - Slow-loading search results or laggy search functionality may cause customers to abandon your store.
How Does Search Abandonment Impact Your Ecommerce Business?
Search abandonment has a direct impact on your eCommerce sales. From lost revenue to unsatisfied customers, the issues are many. Let us understand how these abandoned searches can influence your eCommerce business.
Dissatisfied Customers
When search functionality does not deliver the right results, your customers are not able to find what they are looking for and are left empty-handed. This leaves a lasting negative impression on your brand. It reduces the chances of repeat purchases and might also compel them to write negative reviews about your eCommerce store.
Increased Bounce Rates
As your customers abandon their search and exit your website, they tend to spend less time on your store. This contributes to higher bounce rates which is a red flag for search engines. It signals the search algorithms that your store offers a poor user experience, making it harder for your site to rank higher and attract organic traffic.
Lower Conversion Rates
Every abandoned search is a missed opportunity for a sale. When customers do not find desired products, they are less likely to complete the purchases. This leads to fewer conversions and hinders your ability to generate revenue from your existing and new customers.
Poor Search Insights and Analytics
Abandoned searches leave gaps in your data. It becomes tedious to gather accurate insights into what customers are searching for and why they are leaving your website. This absence of actionable data can cost you opportunities to fine-tune your inventory, marketing strategies, and search functionality.
Increased Costs for Customer Support
Customers often reach out to your support team for help when they are unable to find products. This increases the workload on your customer support teams as they have to offer required assistance to the customers. This will certainly divert their attention from other important tasks, making them feel overwhelmed with additional work.
Revenue Loss
Every abandoned search is a potential purchase that never happened. It is a lost sale and revenue that could have contributed to your bottom line. Over time, these lost sales add up causing a significant revenue loss.
6 Ways to Reduce Search Abandonment
Search abandonment is indeed a challenge for most eCommerce businesses. However, it is not an unsolvable one. You can significantly reduce your search abandonment rates by enhancing the search experience you offer to customers.
Here are a few ways how you can optimize your search functionality using AI-powered technologies and reduce search abandonment.
1. NLP-Driven, Personalized Search Experience
Natural language processing algorithms can understand what exactly your customers mean in their search queries and not just what they type. Instead of relying on exact keyword matches, it tries to understand the intent behind a search query to deliver the right results. For example, when a customer searches for 'comfortable winter boots for walking in the snow', NLP identifies key terms like comfort, purpose, and product category to showcase the best products. It can interpret conversational and even incomplete phrases in search queries.
Personalized search further complements these NLP-driven results by considering the customers' browsing history, purchasing behavior, preferences, and more to display products that align with their shopping needs. This approach helps you deliver spot-on search results for every search query. Your customers can find relevant products quickly and they are less likely to leave your website frustrated. This reduces search abandonment and cart abandonment rates and boosts conversions.
For example, Samawa Perfumes leverages an AI-powered search engine that utilizes NLP to provide search suggestions and personalized search results to customers. This search engine can analyze customer behavior, browsing patterns, and intent to deliver tailored results, ensuring customers can find the items they want quickly.
2. AI-Powered Search Recommendations
AI-powered search recommendations use machine learning algorithms to analyze customer behavior and deliver highly relevant product suggestions to customers. It studies their past searches, browsing history, and even real-time interactions to showcase items that align with customers' preferences. For example, when a customer searches for 'winter jackets', AI can recommend related products like winter shoes, gloves, scarves, and other products your customers did not even think they needed.
Here, AI anticipates your customers' future needs and guides them toward the products they are most likely to purchase. Instead of leaving them to search further, it proactively presents relevant options to them. This encourages them to stay on your website and explore additional products to add to their purchase.
For example, Zalando uses AI-powered search recommendations to drive sales. Their AI system takes into account the customer's size, preferred brands, and purchase history to recommend clothing that fits both their style and size. This reduces search abandonment as customers feel that Zalando understands their individual needs and keeps them away from irrelevant product listings.
3. Intelligent Query Autocorrection
Correcting typos instantly and suggesting relevant search keywords ensures customers stay engaged with the search process and reduces search abandonment. Intelligent query autocorrection uses advanced algorithms to understand customer queries, identify spelling errors, and automatically fix them. Instead of showing no results, the search solution suggests the closest matching or alternative search terms to customers, thus guiding them to relevant products. Your customers do not waste time rephrasing or refining their queries, making the entire search experience feel seamless.
For example, if a customer searches for 'winter shos for women', the search engine will automatically recognize the error and suggest the correct term i.e. 'winter shoes for women'.
For example, ASOS uses AI-powered query autocorrection to improve search accuracy. It ensures customers receive relevant results even if they make minor mistakes by automatically correcting to more accurate search queries. This accuracy of search results improves customer satisfaction, making them less likely to abandon their searches.
4. Hyper-Relevant Product Recommendations
Suggesting highly personalized products during and after a search is what hyper-relevant product suggestions are all about. You can leverage customer data, such as their behavior on your website, their product preferences, search history, past purchases, and more to understand what products they are most likely to be interested in. This will help you deliver the most relevant and attractive product options. It also makes it easier for customers to find desired products without any additional effort.
As the suggested products are specifically tailored to individual customer needs, there are fewer chances of search abandonment. They are most likely to stay on your website and explore your products, leading to higher conversions and sales.
For example, Sephora uses hyper-relevant product recommendations to personalize the shopping experience for its customers. A customer searching for products like foundation, concealer, and more is presented with items based on skin tone, color preferences, and even previous purchases. The search results are further complemented with matching blushes, highlighters, and bronzers. This curated product selection not only meets the customers' preferences but also lowers the chances of search abandonment.
5. Product Bundling for Upselling and Cross-Selling
As the name suggests, product bundling means grouping complementary or related products together to offer them as a package deal. This is done to encourage customers to enhance their primary product choice and purchase multiple items at a time. These sales tactics - upselling and cross-selling increase the order value while boosting customer satisfaction.
Upselling means suggesting a higher-end version of a product. While cross-selling refers to suggesting additional products that complement the main item. This keeps customers engaged as they are readily presented with interesting product suggestions. It eliminates the frustration of searching for complementary items individually. Thus, reducing the likelihood of search abandonment and increasing the chances of completing a purchase.
For example, Amazon provides product bundles called 'Frequently Bought Together' and 'Bundle Deals' on product pages. This enables customers to see all relevant products in one place, reducing their chances of abandoning the search and shopping from other online stores.
6. Conversational Chatbots
Conversational chatbots are AI-powered virtual assistants that simulate human-like interactions with customers. They assist them in navigating your eCommerce store by answering queries, offering product suggestions, and resolving concerns in real time. These chatbots leverage natural language processing (NLP) to understand user intent and provide meaningful, contextual responses, creating an interactive shopping experience that mimics human support.
Conversational AI chatbot software like Bodt helps you offer instant support to customers, eliminating the challenges they could possibly face during their search journey. It acts as a proactive guide for struggling customers to ensure they find what they are looking for without leaving your online store.
For instance, H&M provides an intuitive chatbot on its mobile app to help customers discover products that suit their preferences quickly. The intelligent chatbot is designed in a way that it asks follow-up questions making the shopping experience as interactive as possible, eliminating the chances of an unsuccessful search process.
Sparq.ai - An All-in-One Search Abandonment Solution
Search abandonment is no doubt a significant challenge for most eCommerce businesses. However, with the help of the right tools, you can certainly tackle this challenge and transform your website's search experience.
Sparq.ai offers an excellent search abandonment solution providing intelligent features to help customers find what exactly they are looking for and keep them engaged.
Effortless Product Discovery - Our intuitive search engine anticipates customers' needs with the help of NLP to deliver precise and relevant search results.
Smart Search Recommendations - By analyzing real-time data and customer behavior, our search offers intelligent product recommendations. These suggestions are tailored to the individual customer’s preferences, making their journey smoother and more enjoyable while reducing search abandonment rates.
Error-Free Searches - No more “No Results Found” pages! Our advanced query autocorrection ensures that typos, alternate spellings, and incomplete searches do not impact product discovery. It keeps customers engaged and encourages them to explore more.
Powerful Faceted Navigation - Customers can refine their searches with dynamic filters like price, brand, or style. This streamlined navigation keeps customers on your site longer and guides them toward making a purchase.
Enhanced Customer Personalization - Our search solution leverages customer data to provide hyper-personalized recommendations to customers. It delivers a shopping experience that feels tailor-made for each individual.
Real-Time Mobile Optimization - With mobile shopping on the rise, our search offers a fully optimized search experience for smartphones and tablets. Your customers can enjoy seamless navigation and lightning-fast results, no matter the device they are using.
Actionable Analytics - Our search solution provides detailed analytics to help you understand your customers better. It helps you learn what they are searching for, where they are dropping off, and how you can improve your search experience to meet their needs.
Wrapping Up
Search abandonment is not just a missed sales opportunity. It can negatively impact your eCommerce store's growth. With intelligent search solutions like Sparq, you can convert these abandoned searches into powerful conversions. Its features like AI-powered product recommendations, autocorrected queries, actionable insights, and so on can engage your customers and retain them.
Schedule a demo today to see Sparq in live action and understand how it can help you build seamless shopping experiences.
FAQs
What is the difference between search abandonment and cart abandonment?
Search abandonment is when a customer performs a search query on your online store but does not click on any of the search results or take any further actions. While, cart abandonment is when a customer browses through your products, adds desired ones to their cart, and leaves your online store without purchasing them.
Why do customers abandon searches on eCommerce websites?
Some of the main reasons for search abandonment on eCommerce websites are -
- Irrelevant search results.
- Difficulty in navigating search functionality or filters.
- No search results pages.
- Slow loading times and other technical issues.
Can a no-search results page cause search abandonment?
Yes, when customers try to search for a product and get no results, they are more likely to abandon their search. Hence, you must design excellent no-search results pages to keep them engaged on your website.
Can AI reduce search abandonment?
Yes, AI can help you reduce search abandonment. You can use AI-powered search solutions like Sparq to refine your search results and deliver tailored search experience to customers.
