Online Shoppers Have High Expectations

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Sergio Iacobucci, Senior Director, Ecommerce Marketing at Coveo, sets the stage for the importance of AI-powered search by reviewing current consumer expectations regarding ecommerce experiences. Iacobucci cites data from a Coveo survey of 4,000 consumers which found that:

• 93% respondents expect their online shopping experience to be at least as good as in-store, if not better.
• 91% say they encounter problems when trying to shop online.
• Gen Z consumers report frustrations at an even higher rate of 96%.

 

Shoppers' expectations of ecommerce are shaped by the highly personalized digital experiences they encounter across platforms like Spotify, Netflix, and Pinterest. Consumers expect a similar degree of relevance and tailoring from their online shopping experiences as they get elsewhere. Failing to meet these high standards can lead to abandoned carts and lost sales, particularly in an online environment with competitors just a tab away.

 

It's a tall order for retailers to continuously deliver experiences on par with the best digital platforms and properties, which is why AI is such an important ingredient here. AI-powered search capabilities allow retail brands to understand intent, provide accurate product matches, and connect shoppers to complementary content that guides them through the buying journey.

 

Retailers Face Challenges When Meeting Customer Expectations

Meeting customer expectations can be particularly challenging for mass market retailers that serve multiple verticals. Vibhu Arora, Associate Director of Product Management at Walmart, notes that shoppers' needs vary greatly across verticals like automotive, electronics, and apparel. Providing tailored, vertical-specific user experiences to match those nuanced expectations is challenging, particularly since retailers must maintain a cohesive cross-brand feel as customers navigate between categories.

 

Adding to this challenge are rapidly shifting market conditions which require that all retailers pivot quickly in response to macro events like Covid, rising inflation, or supply chain issues. When consumer sentiment sours, discretionary purchases get put on hold, forcing retailers to rethink strategies for promoting non-essential goods and services. Organizations who can't react swiftly to these disruptions risk losing ground to more agile competitors.

 

Consumer Pain Points Around Product Discovery

Online versus in-store shopping comes with some unique challenges for consumers as well. Both speakers touched on some common frustrations shoppers face during product discovery which include:

 

Failing to Understand Intent

Shoppers want to be known. A major point of frustration is, therefore, when a customer doesn't feel understood by a retailer. Arora explains that it's not just about interpreting the exact keywords a customer types, but grasping the underlying intent behind those words. For example, a search for "stand" could mean a TV stand, a nightstand, or a music stand. The true search intent depends on the full context of the shopping journey.

 

Lack of Product Education

Another pain point emerges when customers want to be educated about a product or product category, but can't find the information they need. This is particularly true of high consideration purchases like appliances, electronics, and exercise equipment. For example, someone looking at mountain bikes may know nothing about the key features and factors to consider when buying a bike. Without helpful guidance and content to guide them, the process of evaluating options becomes arduous and overwhelming.

 

The Paradox of Choice

Arora noted that an "endless aisle" problem plagues ecommerce. Too much choice can grind the shopping journey to a halt. A search for something as simple as inexpensive speakers or earbuds can return hundreds or even thousands of products, turning digital shopping into a slog of overwhelming results. According to Coveo research, the #1 reason consumers abandon a store is the inability to find what they want. Making this process easier (such as with search facets) is critical for keeping people invested in completing the sale.

 

How Poor Site Search Hinders The Digital Shopping Journey

 

Fundamental flaws in site search capabilities can further derail the digital shopping experience. Iacobucci reviews some of the basic issues that retail search engines struggle with which include:

• An inability to handle misspellings and synonyms – Searches remain too literal, with search tools that fail to grasp the actual query intent when words are misspelled or phrased differently. This leads to irrelevant results and frustrated customers.
• Poor data quality and catalog representation – Many product catalogs are still manually curated, inevitably leading to errors, missing data, and inaccurate representations of the available products. If the catalog itself is unreliable, search results will never meet expectations.
• Lack of personalization and context – Generic, one-size-fits-all search results ignore the critical signals about each shopper's individualized journey and interests. Failing to tailor experiences based on unique contexts means missing opportunities to be truly relevant. 

 

AI-Powered Search: The Solution for Personalized Product Discovery

AI-powered search can understand customer signals and automate personalization, allowing retailers to showcase the right products at the right moments throughout the shopping journey. This goes beyond just matching keywords. AI can generate smarter filters and facets tailored to each visitor's real-time context.

 

It can also curate highly-relevant product listings on homepages and category pages based on learned preferences. And with the ability to integrate complementary content and buying guides, AI empowers retailers to facilitate seamless, friction-free paths to purchase.

 

Designing Profitable Search Strategies with AI

Implementing AI-powered search capabilities allows retailers to design digital strategies that overcome consumer pain points and drive sales. Arora outlined several pillars that act as best practices when using AI for effective – and profitable – ecommerce search experiences. They include:

• Removing friction – Solutions like conversational AI remove friction by allowing customers to express themselves naturally using normal language, rather than being constrained to exact keywords.
• Understanding intent – You need an AI model sophisticated enough to discern the true intent to grasp the full context and meaning of a shopper’s search.
• Improving Ranking - AI can rapidly match the customer’s query to the most relevant products in your catalog. This "intelligent ranking" process surfaces the items customers are most likely to want based on their real-time shopping context.
• Refining filters and facets – Use AI to further refine and contextualize search results with dynamic filters and facets tailored to each unique shopping journey. For example, facets for a TV could focus on resolution, display type, smart platform. Filters for fashion could highlight size, color, material, and brand.
• Personalizing recommendations – AI-driven product recommendations use customer data, session behavior, and other signals to provide shoppers with personalized information, relevant products, and the support they need to confidently make a purchase.

 

Retailers can use AI-powered search to drive conversions and profit by removing friction and connecting shoppers with the information they need and the products they’ll love. The bottom line is that great ecommerce site search helps customers make confident purchasing decisions – and that’s good for everyone.

 

Don't miss this webinar showcasing how to leverage AI-powered search capabilities to transform ecommerce experiences. Learn strategies for understanding intent, personalizing product discovery, and maximizing conversions through intelligent search.