Search matters. Nearly 80% of people that search for an item two times without finding what they want will leave the site and never come back. And on the other side, firms using a quality search solution will hold onto their customers through strong retention rates. According to Salesforce data, searchers pull in more revenue, spending 2.6X more on mobile and desktop retailers’ sites compared to customers that do not use search.
Despite the numbers, many ecommerce players are not investing enough time and resources into improving their search functions.
Underestimating the Power of Search
Ecommerce firms continue to underestimate the impact of good or poor search on their business results and customer metrics. Instead, they put their attention and resources toward the site layout and navigation, managing product categories and handling inventory issues. All these warrant dedicated time and effort, but that is wasted when paired with poor search. Inventory will not fly off the shelves if people cannot find and buy what they want.
A crisp layout and smartly designed product categories allow people to use the site intuitively, but if they first use the search bar, they might be led astray. The detrimental impacts of poor search might be hidden. Ecommerce marketing managers and data analysts are inundated with information. When sales are lagging, performing root cause analyses is difficult. Looking at the number of searches made tied to the bounce rate is one key metric that can point to problems with the search function not producing quality results.
Many companies continue using an “out of the box” search tool with limited functionality. However, the ecommerce competition is much higher, especially after the pandemic. The modern mobile consumer also expects every business interaction to happen quickly and accurately — and search is no exception.
Improving Search Functions for Better Engagement
The accuracy, relevancy, and speed of an ecommerce search platform is tied to customer engagement. When people find items quickly, can sort easily and receive relevant recommendations, then search is working properly.
For ecommerce companies moving from a simple search solution, it’s possible to work iteratively and make small improvements over time. Each refinement to the search tool’s capabilities and accuracy will improve customer satisfaction metrics and purchasing results. Enhanced usage of the search bar also provides the company with rich data it can use for analysis and revenue-generating projects. 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 its search taxonomy relates to the quality of its “product recommendations” engine, so it provides long-tail benefits beyond improving individual search results.
The Back-End Tech
Consider home furnishings provider Wayfair as an example of an ecommerce provider with a quality search and sort tool. A buyer can find or build their perfect sofa with the tool by filtering by color and fabric, and sorting by style. The site provides the shopper with multiple options and routes that all work in tandem to narrow down searches.
It is 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 solution 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 improve search function relevancy for multiple types of users. For example, site search leader Lineate works with ecommerce firms and multiple large hospitals and education organizations, among many other clients. Search is interesting for these types of implementations, where different groups of people search for the same 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 search 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 that when a search is made, previously unrelated syntax is paired together.
When ecommerce firms improve their search functions through technology, they are better able to complete customer journeys. It leaves an impression of satisfaction and trust, which encourages completing transactions, and then referrals and brand advocacy.
Elizabeth Gallagher is the Chief Revenue Officer at Lineate — a New York-based custom software development company — where she oversees marketing, sales and product development. Previously, Gallagher was Co-founder and CEO of the award-winning ed tech company, Pixeldream, where she brought dozens of high revenue technology products to market for leading organizations including McGraw-Hill and Pearson.