Why Intent-Enabled Search is the Key to Converting High-Intent Shoppers
Let’s say you go to your favorite fashion and apparel website and type your usual search term for whichever specific item you’re looking for in the search bar. What do you typically find? Are the results relevant? Or are they a swing and a miss?
More often than not, shoppers are having trouble finding exactly what they’re looking for. But why is this? Why are shoppers not easily able to search and be delivered relevant results from retailers? The answer is simple: thin and inconsistent product data.
So how can retailers ensure a consistent, seamless, and intent-enabled search experience? By addressing thin and inconsistent product details — and ensuring that high-intent shoppers find what they’re looking for.
Why Product Data is Important
Of each of the millions of product listings available online lives data that describes the item being sold. That information, whether outwardly present or not, is helpful in providing relevant search results for shoppers. For my e-commerce enthusiasts, Amazon advertisers, search engine marketing (SEM) specialists, and marketers out there, think keywords or hidden keywords.
This relevant product data is essentially descriptive text used to differentiate or better personalize the search for items and highlight product attributes. It’s also single-handedly the most important requirement for capturing the attention of intent-driven online shoppers that leads to increased conversions.
Visitors who are using site search are without a doubt high-intent shoppers. This is why one of the most important places that product data lives is within the search bar. That data can make all the difference, especially when you consider the stats:
- Search bar shoppers are 2.4x more likely to buy and spend 2.6x more money. (Salesforce Research)
- Up to 30 percent of e-commerce visitors use a site search box when one is offered. (UX Booth)
- Nearly 84 percent of companies don’t actively optimize or measure their on-site search. (AddSearch)
- Site search is 1.8x more effective at producing conversions. (Econsultancy)
- A visitor utilizing internal site search is known to convert up to 5x to 6x higher than the average nonsite search visitor. (CXL)
- Sixty-eight percent of shoppers would not return to a site that provided a poor search experience. (Forrester Research)
Surpass the Basics
Typical retailer merchant-driven product attributes tend to stop at the basics, and can even possess some inconsistencies or inaccuracies due to traditional manual labeling processes. For example, when a merchant describes a blue blazer jacket as merely a blue blazer jacket (maybe it’s also black or is referred to as a sport coat).
With this basic level of product detail, shoppers who are only ever looking for those basic attributes will be the only people presented with this item. If a shopper searches for anything more detailed, their search results are likely to be disappointing if all they’re met with are vanilla search results that don’t match their intent.
With artificial intelligence, customers can enjoy 10x more attributes for each of the products in a retailer’s assortment. These attributes allow said customers to be presented with better, more tailored search results for all of the more subjective and thematic searches they’re making on a retailer’s site.
How Retailers Can Ensure a Consistent, Seamless, and Intent-Enabled Search Experience
With the help of a product attribution management platform, retailers can ensure they’re meeting the unique needs of each of their shoppers. This means providing a relevant shopping experience whether the consumer is in the research phase or if they’re visiting a website with a specific purchase in mind. To provide exactly that experience, retailers can leverage highly trained machine learning pipelines — built and trained by a diverse team of industry and domain experts. With over a billion training data points collected, and 2 million-plus new ones added daily, product attribution technology provides the richest, deepest and most accurate data available on the market today.
Byron Jones is the vice president of product at Lily AI, the product attribution management platform built to power the present and future of e-commerce.
Byron Jones is the VP of Product at Lily AI, the product attribution management platform built to power the present and future of e-commerce. He previously worked at Optimizely, a software solution that helps organizations to unlock digital potential. He also worked with the National Aeronautics and Space Administration (NASA) as a Flight Systems Engineer for the NuSTAR Project, among a variety of other roles in developing the surface mission and executing the science operations within NASA’s Jet Propulsion Laboratory.