Up to 43% of visitors to retail websites immediately go to the search bar, and those consumers are 2-3x more likely to make a purchase than site visitors who don't use the search bar. That's why any initiative to improve the digital experience must keep search front and center. Improvements to the search experience can make the most impact because they directly affect these high-intent shoppers. If you can serve up relevant and comprehensive results for your most promising site visitors, you will convert more orders and build brand loyalists. And the stakes couldn't be higher. PwC reports that 32% of customers will leave a brand they love after just one bad experience.
But not all searches are created equal, and different types of searches require different strategies.
Search terms signal behavior
That first shopper looking for a "shirt" knows they need a shirt. But what kind of shirt? They haven't explicitly provided any meaningful information, so there's more work to be done to help them find their perfect shirt. Strategies such as faceted search (to help guide them down a path to the most relevant results) or personalization (to predict which shirts they are most likely to be interested in) can be effective measures to improve the search experience for these browsers. Ultimately, this shopper likely has a higher intent to buy than a casual browser, but they still require inspiration, and may not be quite ready to make a purchase just yet.
But that second shopper looking for a "sequin party shirt" has a very specific vision of the product they're looking for, and has signaled that they are a very high-intent shopper. If you can accurately return matching results, you have a high likelihood of converting this visitor. Best of all, you don't need to rearchitect your search experience or implement any sophisticated personalization strategies to create a great digital experience for this shopper. All you need is to be able to return relevant and comprehensive results. Sounds simple, right?
Getting descriptive search results right
While searches for specific brands or product categories typically perform well, the harsh reality is that 46% of e-commerce websites can't support thematic searches. That means brands and retailers are delivering a lackluster experience to their highest intent shoppers. Unfortunately, something that sounds so simple is actually quite difficult. E-commerce businesses face several challenges in creating an intuitive, delightful experience for shoppers who use descriptive searches:
Long-tail search terms require automated solutions
Not many customers come to your site to search for "sequin party shirts", "boho chic dresses", or "feminine business casual skirts", but when you combine all of these long-tail searches together, you have a critical mass of high-intent shoppers all desperately wanting to purchase these hyper-specific items. Hard coding the endless array of possible search terms into your database is incredibly challenging, and not something you can brute force your way to through manual efforts. Yet most retailers rely on a team of merchandisers who manually assign product attributes, tagging each product with its category, brand, color, and perhaps 2-4 other attributes such as material or pattern.
Consistency is difficult at scale
Even companies who capture a bit more detail for each product still struggle to deliver highly relevant search results because enforcing consistency across large teams of merchandisers processing products from hundreds of brands is incredibly challenging. Yes, companies may have strict taxonomies that define specific product attributes, but mapping pictures of products and brand-specific vernacular to those product attributes is an inherently subjective process. One individual merchandiser's opinion of what constitutes "feminine" should not influence the search experience.
Let data and automation lead the way
Delivering the most relevant and comprehensive results to high-intent searchers requires a solution rooted in data and automation. At Lily AI, our product attribute management system has been trained on over one billion data points to recognize granular product details and apply sophisticated relational intelligence to enrich product data with consumer-centric attributes. Product attributes can be delivered in real time at scale, enabling brands and retailers to effectively support the wide variety of descriptive searches from their highest-intent shoppers.
Learn more about the role of product attribute data in driving search conversions and much more in our webinar featuring Bloomingdale's. Bloomingdale's leveraged Lily AI's granular and consumer-centric product attributes to enhance site search across apparel, shoes, handbags, and jewelry, improving the accuracy and relevance of its site search experience.
Watch the webinar