Presented by Coveo
Stuck indoors, shoppers across the world went online in 2020 — and stayed, with sales growing by 28% worldwide. But despite this boom, too many companies failed in the face of growing customer expectations, and their own lack of relevance.
Relevance is when a customer gets what they want, when they want it — before they even knew they wanted it, and with no effort at all. Nowadays the seamless, frictionless, and personalized shopping experiences of brands like Walmart and Amazon have set the standard, and the kind of experience that customers simply expect. Any online retailer that falls short faces consequences.
“This new wave of consumers has higher expectations than ever before,” says Ciro Greco, vice president of artificial intelligence at Coveo. “While 9 out of 10 consumers say they expect online shopping to be equal to or better than in-store, half are experiencing crushing disappointment, and almost three quarters of customers say they will abandon a brand after three negative experiences.”
These are the findings of Coveo’s Relevance Report: 2021. It surfaced that consumers are frustrated by confusing website navigation and by inadequate search functions, sabotaging their efforts to find the information or the products that are relevant to them. And these shoppers are showing their dissatisfaction by abandoning their carts, sending marketing emails to the trash, and just not coming back to shop.
The big retailer difference — and why it doesn’t matter
The big retailers have built their experiences with a grocery list of hard-to-compete-with advantages like a treasure trove of data, billion-dollar budgets, dedicated data scientists, and the ability to translate the latest up-and-coming ecommerce research into real-world strategy.
“The main characteristics that all these companies have in common is that they all manage highly transactional websites capable of collecting big-data,” Greco says. “Many problems that are tackled with AI are driven towards extreme optimization, because a 0.2% improvement in recommendation accuracy can translate into serious monetary uplifts at that scale.”
The majority of ecommerce research is done by the big ecommerce companies like Rakuten, and it’s aimed at solutions tailored toward high-traffic websites, big data, and the sophisticated technology that only the big players can afford. Unsurprisingly, smaller retailers have felt simply unable to keep up, and have counted themselves out of the personalization and relevance game too soon.
But Coveo is working to change that. Its team of data scientists is continually researching and publishing in leading peer-reviewed journals how machine learning can personalize with cold-start/anonymous shoppers.
With applied artificial intelligence now cheaper, more accessible, and more user-friendly than ever before, it is leveling the playing field in a way that means that the available budget, resources, and data is usually more than enough to develop game-changing personalization that keeps shoppers shopping. Powerfully compelling personalization is within reach even when you’re not one of the mega retailers and lacking big data.
“What we call democratization of AI is the growing availability of methods and technologies that a few years ago were only accessible to tech giants. This will help every retailer to start addressing optimization problems and to put in place the pieces needed to deliver truly relevant experiences to their users,” Greco says.
Nailing personalization without big data
Big retailers can offer personalized customer experiences, like custom home pages, because they have a wealth of customer history data. But for most B2C sites, only 5% to 25% of visitors are recurring — so history-based personalization fails nearly 95% of the time. On top of that, the average bounce rate sits at about 45%.
But smaller retailers can still easily offer personalized experiences even for first-time visitors by offering shoppers the kind of intuitive, context-based experience they’d have in a bricks and mortar store, where a clerk who knows what they’re looking for can lead them directly to what they need based on these cues.
How? They know what their users do while shopping, what they click on, what they search for, and what they buy. This wealth of information can be used to feed a layer of intelligence that is focused on predicting where a certain shopping session is going.
Even when the identity of the shopper cannot be inferred, it is still possible to deliver 1:1 personalization based on what they do throughout a shopping session. Coveo Relevance Cloud guides shoppers to exactly what they’re looking for with fewer clicks, giving consumers a feeling that their experience is tailored to their needs, which makes them more likely to seal the deal.
The Relevance Cloud uses AI, machine learning, deep learning, and data to help make every customer interaction more relevant by collecting signals from all digital interactions around each customer, finds insights using AI, and then adds relevance to search results, recommendations, and personalization. This intelligence helps to improve key metrics like cart conversion rate (CVR), average order value (AOV), and ultimately, gross merchandising value (GMV).
Astonishing real-world results
“These methods can be used to improve relevance at many different touchpoints: search, suggestions, and recommendations,” Greco says. “It’s good to keep these three elements under the same umbrella, because the same underlying intelligence can be used to both help customers find exactly what they’re searching for and offer relevant suggestions to discover new products.”
Here’s a look at what intelligence applied to data can achieve.
Hearts on Fire
Hearts on Fire, a leading global diamond jewelry brand owned by Chow Tai Fook Jewelry, is the largest vertically integrated jewelry company in the world by revenue. The company sells its products through B2C and B2B ecommerce websites, a large network of independent retailers, and more than 2,000 points of sale in China and Hong Kong.
The company leveraged Coveo’s AI and machine learning models to enable its customers to rapidly find relevant products and create a personalized online shopping experience and saw its B2C ecommerce purchase conversion rate increase by 587%, average order value increase by 49%, and bounce rate decrease by 31%, which drove a meaningful increase in revenue. The company experienced similar results on its B2B website over the same period.
Caleres: Famous Footwear, Dr. Scholl’s Shoes, and Naturalizer
Caleres owns and operates such popular shoe brands as Famous Footwear, Dr. Scholl’s Shoes, and Naturalizer. To improve site search for its family of brands, Caleres turned to Coveo’s AI and machine learning platform. Using a relevance platform, it was able to offer better search results by leveraging data from past users’ experiences on the website. With each interaction, the platform gets better at quickly understanding what customers want and showing them relevant search results. Caleres saw a 20 – 25% lift in conversion rates for customers using search — in comparison to their legacy platform, which did not use machine learning.
The platform also helped them improve the facets on its search listings and category pages. Facets allow the shopper to refine their results based on facets like brand, color, size, and price. About 30-50% of site visitors engage with facets during a session on Caleres sites, and those visitors have a 50-60% lift in conversions compared with visitors who don’t engage with facets.
And finally, machine learning and AI helped the company use a data-driven approach to tune its buying pipelines when choosing which products to boost. The result is that machine learning is yielding an average 13% increase in click-throughs and, in their most recent test, a 23% increase in conversions.
“Personalization is no longer a nice-to-have, but a must-have for ecommerce brands and retailers,” Greco says. “Bottom line: all serious players spare no expenses for AI research. It’s important, it makes the difference, it is worth investing in it.”
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