Is your product search engine holding your business back? Stats suggest it is, with 80% of users exiting a site due to poor search.
While you may be unable to answer this question entirely, you’re not alone. Many online retailers underestimate the role an AI-powered product search engine plays in driving sales and conversions. And selecting a proper search solution is a worthy investment that can boost revenue and conversions, inform strategic ecommerce decisions, and delight customers.
Let’s dive into what some of the top product search engines are doing wrong, and what they should be doing instead.
Error #1: They Interpret Queries Based on Keyword Matching
Most search engines used on ecommerce sites today aren’t built for ecommerce or product discovery. They’re built on keyword engines — such as Solr or Elasticsearch — and prioritize relevance above all else. (And as ecommerce teams know, returning the most relevant products doesn’t always guarantee a sale.)
This approach of keyword matching works decently in content, like blogs and support documentation. But it’s less than ideal for driving specific ecommerce KPIs like revenue, inventory, and conversions. Not to mention it falls very short of offering a seamless shopping experience.
It also brings up the notion of search intent –– how do search engines relying on keyword matching algorithms decipher the intent of a user? Short answer: they can’t.
Intent isn’t always explicit in keywords. For example, a user searching for “spreadable butter” wants butter they can spread, not the cooking spray butter or Nutella spread that’s often returned for their query. And keyword matching certainly won’t return complementary items alongside spreadable butter, like the perfect bread for making toast.
What to look for instead: Choose a product search engine that can be optimized for ecommerce KPIs, thanks to transformers and large language models (LLMs) that perform a variety of Natural Language Processing (NLP) tasks. NLP is a type of artificial intelligence (AI) that helps software better understand human language. It uses advanced algorithms to interpret query data, effectively bridging the gap between what a customer actually wants and what the search engine returns.
Error #2: They’re Not Out-of-the-Box Solutions
“One-size-fits-all” keyword-based search engines need to be rigged to perform in line with those natively powered by an AI and ML core.
Layering newer technology on top of older search engines may seem like the logical path to saving time, budget, and resources. But that’s like expecting your flip phone to take iPhone Pro-level photos and save them in the cloud. It’s clunky, and it’s going to frustrate everyone involved because it just doesn’t work.
Understandably, many ecommerce businesses find themselves stuck in a mire of legacy tech that evolved from an unfortunate series of bandaging. This sort of technical debt can be so burdensome, it actually drives businesses toward adopting uber-customized, cobbled-together solutions that are difficult for a non-developer to navigate.
The end result is that search becomes slower for customers and requires a lot of internal manual maintenance and workarounds. Ultimately, it’s a time-suck that serves no one.
What to look for instead: Ecommerce teams aren’t developers, and they shouldn’t be asked to play the part. Since most good merchandising teams want to invest their time strategizing and supporting business goals rather than dealing with clunky UIs or over-customized integrations, we recommend setting your sights on an intuitive ecommerce product search engine that actually empowers your team and reduces their workload by 20%.
Error #3: They’re Not Built for Ecommerce
It shouldn’t be a surprise that since older search engines are based on keyword matching, they’re not built to support the use cases that affect ecommerce business metrics. Ecommerce search performs best when it’s powered by technology designed specifically for ecommerce conversions, not a one-size-fits-all solution.
Yes, ecommerce KPIs are always in flux, depending on the time of year, inventory levels, economic conditions, etc. But one universal conversion holds true despite ever-changing factors: did the user buy something? If they bought something, your search produced good results.
And does the word “personalization” ring a bell? It isn’t just a buzzword — it’s the best way to meet your customers where they’re at and drive your ecommerce goals.
Over 75% of consumers feel frustrated when a personalized experience does not occur, making them less likely to purchase.
What to look for instead: A good ecommerce search engine will show and rank products according to your customers’ historical data and business goals, rather than the vendor’s standard algorithm. Seek out product search engines that make personalized search recommendations for every individual user and are specifically designed to optimize ecommerce business metrics like revenue, profit, inventory management, and conversion rate, among others.
Error #4: They’re Not Embracing Composable Technology
The majority of keyword search engines are not based on composable technology, and while not necessarily a requirement for ecommerce solutions, keep in mind that investing in future-fit technology is critical for customer experience, agility, and performance.
Don’t be fooled by the long-standing technology that is so “reliable,” it hasn’t shifted its architecture in the past decades. Focus on the value afforded by a more agile technology that prioritizes speed, reliability, and customer experience.
What to look for instead: Consider ecommerce vendors that use Microservices, API-First, Cloud-Native SaaS, and Headless (MACH) principles when it comes to their product search engines.
Informed Decisions Start with the Right Platform
If the previous three years of a pandemic have taught ecommerce businesses anything, it’s that keeping up with KPIs requires adaptability. Responding to market changes is nothing new, and with economic uncertainty still looming, most businesses are seeking ecommerce technology that offers faster and measurable return on investment and value, all while keeping customers happy.
The majority of product search engines used by ecommerce sites aren’t helping those businesses reach their full potential. Site search engines that are built on machine learning (ML) can be trained to personalize the customer experience and hit ecommerce goals, all the keys to driving revenue, profit, and sales.
But not all search engines are created equal, and you shouldn’t have to sacrifice your customer’s happiness or your merchandising team’s sanity to drive your most important business goals.