Accurate search results matter. Nearly 80 percent of people will leave a site and never return after searching for an item two times unsuccessfully. On the flip side, firms using effective search solutions will retain their customers at higher rates. According to Salesforce data, searchers pull in more revenue, spending 2.6 times more on mobile and desktop retailer’s sites compared to customers that do not use search.
Despite the data, several ecommerce players are still turning a blind eye towards investing adequate time and resources into improving their search functions.
The Underrated Power of Search
Ecommerce firms continue to underestimate the impact of search on their results and customer metrics. Instead, they pull their attention and resources towards the site layout and navigation, managing product categories, and handling inventory issues. While these are very important variables, all that work is for nothing when paired with poor search. Customers can’t buy what they can’t find.
A sharp layout and smartly designed product categories allow people to use websites intuitively, but should they use the search bar, they might be led astray. The detrimental impacts of poor search might be understated. Marketing managers and data analysts are inundated with information. When sales are behind, executing root cause analyses is difficult. Looking at the number of searches made tied to the bounce rate is one key metric that can uncover problems within the search function if it’s not producing quality results.
Many companies insist on using an “out of the box” search tool with limited functionality. However, ecommerce competition is much higher as a result of the pandemic. The modern mobile consumer also expects every business interaction to happen quickly and accurately—and search is no exception.
Better Engagement Through Improved Search Function
The accuracy, relevancy, and speed of an ecommerce search platform go hand in hand with customer engagement. When people find items quickly, can sort easily, and receive relevant recommendations, then search is doing its job.
For ecommerce companies transitioning from a simple search solution, it’s possible to make small improvements over time. Each refinement to the search tool’s capabilities and accuracy will elevate customer satisfaction metrics and purchasing results. Enhanced usage of the search bar also provides the company with rich data it can use to analyze for revenue generation.
Consider the revenue Amazon derives from its product recommendations, which is driven by its search and buying pattern data. How an ecommerce firm sets up their search taxonomy relates to the quality of their “product recommendations” engine, so it provides long-tail benefits beyond improving individual search results.
The Back-End Tech
Wayfair is an excellent example of an ecommerce provider with a quality search and sort tool. A buyer can find or build their perfect product with tools that help them filter by color, material, and style. The site provides the shopper with multiple options and routes that all work in tandem to narrow down searches.
It’s a quality tool, but the next level for this type of search would be to enable this granularity through the search bar with regular syntax. So instead of the customer selecting various dropdowns, they just type what they want, and all that intelligent filtering happens on the back end, without manual inputs.
Top search solutions providers will offer a range of improvements and features for search, including:
- Recognizing synonyms and colloquialisms to avoid customers reaching dead ends
- Search platforms should feed into custom ranking processes for optimal ranking of search results for better retention and more sales
- Type-ahead suggestions to speed searching and offer best-match keywords for customers who might be unsure of what they need
- Highlighting functions that bring the important parts of a site to a visitor’s attention based on their searches
Machine learning technology can also greatly improve search function relevancy for a variety of users. For example, site search leader Lineate works with ecommerce firms, and multiple large hospitals and education organizations, among other clients. Search is unique for these types of implementations where different groups of people search for the similar content using different language.
For example, a doctor would use clinical terms to describe a patient condition in a different way than the actual patient using the same search tool. A robust search can use machine learning to refine itself over time to ensure the same information is presented regardless of who is doing the searching, and specifically how they search.
The same dynamic can benefit ecommerce sites by improving results for different groups of people who think about products and perform searches in varied ways. Mapping these searches with a database is needed to complement the machine learning, to ensure when a search is made that previously unrelated syntax is paired together.
When ecommerce firms improve their search functions through technology, they are arming themselves with the tools to complete customer journeys. It leaves an impression of satisfaction and trust, which encourages completing transactions, leading to referrals and brand advocacy.