By Elizabeth Gallagher, Linate
When it comes to search results, precision matters. In fact, close to 80 percent of people will leave a site and never return after searching for something twice unsuccessfully. On the contrary, companies that can effectively implement accurate 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 these findings, several eCommerce players are still turning their backs on investing proper time and resources into improving their search functions.
The Underestimated Power Of Search
Ecommerce firms continue to overlook the importance of search on their results and customer metrics. Instead, they focus their attention and resources on things like the site layout and usability, organizing product categories, and keeping tabs on inventory issues. While these are critical variables, a poor search function can make all that work go to waste. Customers can’t buy what they can’t find.
Sharp layouts and thoughtfully designed product categories allow people to use websites intuitively, but once they use the search bar, there’s a chance they could be led astray. The detrimental impacts of poor search always have been understated. Marketing managers and data analysts are inundated with information. When sales are lagging, finding the root cause can be extremely 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.
Still, many eCommerce players have opted to use an unorthodox search tool with limited functionality. However, as a result of the pandemic, eCommerce competition is much higher. The modern mobile consumer expects every business interaction to happen efficiently and accurately—and search is no exception.
Stronger Engagement Through Revamped Search Function
The accuracy, relevancy, and speed of an eCommerce search platform are directly related to 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 traditional search solution, it’s possible to make small improvements over time. Each refinement to the search tool’s capabilities and accuracy will lead to higher customer satisfaction rates and purchasing results. Enhanced usage of the search bar also provides the company with important 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.
Analyzing The Back-End Tech
Wayfair is a great example of an eCommerce provider with an industry-leading search and sort tool. A customer can find or build their perfect product with search tools that help them filter by color, material, and style. The site provides the shopper with a variety of 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. Ideally, customers would be able to 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 also can greatly improve search function relevancy for users. For example, site search leader Lineate works with eCommerce firms to deliver the best results that are customized to their specific needs through machine learning. 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.
This dynamic tool can help 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.
Ecommerce firms that commit to improving their search functions through technology, are arming themselves with the tools to complete the customer journey. It solidifies a company’s trustworthiness and provides the satisfaction customers are looking for. With improved search, eCommerce players can look forward to completing more transactions, gaining more referrals, and ultimately increasing brand advocacy.
About The Author
Elizabeth Gallagher is CRO of Lineate.