What retailers need to know about site performance, product findability and personalised recommendations

Online and in-store sales are heavily influenced by search performance and product discoverability, survey reveals what retailers are focusing on and where there’s room to improve. 

Lucidworks, a leader in AI-powered search, commissioned Retail TouchPoints for its inaugural ecommerce best practices survey to measure the key elements powering site performance.

The survey establishes benchmark KPIs such as add-to-cart (ATC) percentage and click-through-rate (CTR), and outlines top concerns such as performance, product findability, and customer experience. It also demonstrates that retailers are adopting AI-powered tools at a relatively high rate, but still have room to improve on creating hyper-personalised experiences and understanding customer intent.

Site performance, product findability, personalised recommendations, and more, all impact whether a customer returns later, purchases, or abandons their search completely. These factors are especially critical during high volume shopping periods, including Black Friday and the holidays. Almost three-quarters (73%) of retailer respondents say downtime, degraded site performance, and poor customer experience, collectively, is their top worry during peak demand periods.

“Retailers put endless pressure on themselves to create the perfect shopping experience for customers,” explains Diane Burley, Lucidworks VP of Content. “They know that they have a minute amount of time to prove to customers that they have the products they want. This has retailers constantly measuring to seek ways to improve. But what is aspirational and what is realistic? We created this survey so retailers could measure themselves against other retailers. This survey is groundbreaking because it shows the collective worries of brands doing more than $100 million in sales.”

“Customers who search are ready to buy, but research shows that 80 per cent of searches fail because sites rely on simple keyword search. Providing greater relevancy requires brands to analyse what people are searching for, then optimise synonyms lists, business rules, ontologies, field weights, and countless other aspects of their search configuration. Many of these steps are still being done manually. So we wanted to ask what types of data are you incorporating to refine your search — and how are you doing it,” explains Burley.

The survey found that loyalty programs are by far the most common data source brands utilise as part of their stack at 76%, with point-of-sale (POS) data at a distant second (59%). 60% of shoppers visit a site up to four times before making a purchase, with 40% making five visits or more before they buy. For the 67% of retailers that are collecting customer feedback signals, each of these visits is an opportunity to better understand a customer’s intent and boost the chance of an upsell or cross-sell.

“Retailers should be evolving beyond simple keyword matches when a customer clicks into the search bar,” says Adam Blair, Editor, Retail TouchPoints.

“If you’re not part of the 56 per cent of retailers using AI and ML to power product recommendations, it’s likely that you are missing an opportunity to better serve your customers. When a customer visits a site, they want to feel like the brand knows what they would like — even before they themselves know exactly what they’re looking for. That includes not just preferences such as price point, style and brand, but also hidden factors such as the number of visits they’re likely to make before purchase. AI-powered search engines can augment the brand’s expertise of their own product to provide a more personalised shopping experience right on the customer’s screen.”

Retailers aren’t usually technology experts. However, with new developments in AI and ML, it’s gotten easier to quickly improve site performance without being a search expert. Larger retailers, those doing more than $400 million in revenue, are leading the way in adopting AI-based systems. AI and ML are most commonly used (59%) for documentation classification, assigning an item to a specific category to make it more easily discoverable. Less than half of all respondents (49%) use artificial intelligence for query intent detection, and only 46% use it to power anomaly detection, a feature merchandisers and marketers rely on to track outliers and anticipate trends.

The Lucidworks survey was conducted in August of this year and limited to companies self-reporting $100M or more in sales, and 123 retailers participated. Next year, they hope to widen the survey to companies of all sizes and include B2B, D2C, digital goods, and possibly services (like tickets). Download the Ecommerce Best Practices Survey today.

About Lucidworks

Lucidworks builds AI-powered search solutions for many of the world’s largest brands. Fusion, Lucidworks’ advanced development platform, provides the enterprise-grade capabilities needed to design, develop, and deploy intelligent search applications at any scale. Companies across all industries, from consumer retail and healthcare to insurance and financial services, rely on Lucidworks every day to power their consumer-facing and enterprise search apps. Lucidworks’ investors include Francisco Partners, TPG Sixth Street Partners, Top Tier Capital Partners, Shasta Ventures, Granite Ventures, and Walden International. Learn more at Lucidworks.com.

About Retail TouchPoints

Retail TouchPoints is an online publishing network for retail executives, with content focused on optimizing the customer experience across all channels. The Retail TouchPoints network is comprised of two weekly e-newsletters, special reports, web seminars, exclusive benchmark research, an insightful editorial blog, and a content-rich website featuring daily news updates and multimedia interviews at www.retailtouchpoints.com. The Retail TouchPoints team also interacts with social media communities via Facebook, Twitter and LinkedIn.



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