Have you ever wondered why it’s so easy for your shopaholic best friend to find the perfect pair of shoes online while your search for a luxurious red cashmere sweater always comes up short?
For many, the online shopping experience can be frustrating, confusing and downright disheartening. The root of the issue stems from the significant disconnect between how consumers search for products online and how retailers label, describe and present them on their sites and in their ads. In many cases, though, it’s not that the product doesn’t exist; it’s that the language they are using to describe the product isn’t in consumers’ everyday vocabulary.
Many consumers also are taking to AI-powered search engines like Google Gemini, ChatGPT and Perplexity for their searches. This puts the onus on retailers to have product descriptions that are not only clear for consumers but also are able to be found and understood by the various types of algorithms and technologies powering traditional searches, on-site searches, and now, AI searches.
Retailers need to be both agile and savvy during this period of unprecedented transformation, and those that prioritize making their language more consumer and machine-friendly will be the ones that have an advantage.
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The Criticality of Clear Product Language
Personalization is a key element to any online shopping experience. In fact, over 70% of brands say that AI adoption will fundamentally change personalization and marketing strategies. Additionally, with this new generation of AI, 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn’t happen. Further, while most retailers and brands claim to offer personalized experiences, 66% of consumers have reported a negative experience when it comes to attempts at personalization. There’s lots of upside, and AI is well-positioned to help retailers and brands finally deliver on their “personalization” brand promises.
However, in order for retailers to truly understand how to leverage AI to improve their online shopping experience strategies, they must first acknowledge the roadblocks that today’s shoppers face. According to a new report, 80% of consumers have given up on an online search because they couldn’t find what they were looking for, and 66% said that retailers use descriptions that make it challenging for them to find what they want.
When they are able to find what they are looking for online, 89% said they’ve still resorted to buying the item in-store because they had questions regarding quality, fit, color or size, among other details. And to be clear, those consumers didn’t actually want to go to a store; they did so only out of inconvenient necessity.
This search issue is a problem, and without fixing it, retailers are at a disadvantage. According to the same survey, of the people who said that the search experience impacts their spending, nearly three-quarters (74%) estimate spending $25 or more per visit on sites where they have a positive experience. However, 85% said they’d purchase a similar item from another brand or retailer if they aren’t able to find what they are looking for from their initial brand or retailer choice. In short, the impact of poor product descriptions and details can mean missed revenue targets and lost customer loyalty – a deadly combination in today’s hyper-competitive retail environment.
If it Can’t be Found, it Can’t be Sold
89% of business leaders believe personalization is valuable to their business’ success in the next three years. When the first step of personalized online shopping is making it easy for shoppers to find what they are looking for, retailers need to ensure that their assortments are discoverable. One answer to this call is product content optimization – the process of enriching product data with product information that is merchant-, marketer-, consumer- and machine-friendly to enhance product discoverability no matter where that discovery process happens – from Google and social media to on-site and beyond.
Whether a consumer is browsing Instagram or TikTok, conducting a quick Google Search, or getting into the nitty-gritty of a retailer’s ecommerce site to find a specific item, how products are described on each of these channels is critical. For example, an “ochre godet skirt” from Brand A should come up in a Gen Zer’s search for a “short fall skirt” on TikTok, as well as a Gen Xer’s search for a “neutral-colored corduroy skirt” on Google.
However, this goes beyond basic attributes, synonyms and trends in consumer-facing contexts. Having discoverable products means that retailers also are consistently auditing and updating their data in their backend contexts to match shopper queries.
There also is an increasing interdependence on product description data between ecommerce and advertising, so it is imperative that retailers maximize the effectiveness of both to ensure their inventory is always in front of the right eyes. To do so, they should:
- Understand consumer queries: Retailers need to understand consumer search queries. Retailers with product data that encapsulates consumer-centric language that is customized and optimized for each platform (Google, social media, ecommerce, AI-powered search and answer engines, etc.) will see results – products that can’t be found, can’t be sold!
- Ensure product data is organized: AI outputs are only as powerful as the quality of the data powering the models, so product data must be complete, accurate, consumer-friendly, relevant and consistent to garner the best results. Retailers should not only ensure they have a diverse and well-distributed dataset across their product categories, but also conduct regular audits to refine taxonomy and label processes to align with both industry standards and consumer expectations.
- Align digital marketing and ecommerce efforts: Roles and responsibilities differ across the marketing and ecommerce functions, and while overlaps and dependencies exist across SEO, paid media and site optimization, they’re rarely aligned. Retailers and brands must connect the dots between the efforts of digital marketing and digital commerce teams to ensure product details and descriptions are optimized across all channels.
- Embrace AI: Retailers and brands should adopt AI early, experimenting with the technology to stay competitive and relevant, especially during this new era of generative engine optimization (GEO) and answer engine optimization (AEO). With 40% of shoppers having used new and emerging AI-powered search engines to assist them in online shopping, it’s critical that retailers make their product content AI-friendly so that it can be found on these new search engines.
Retailers and brands have an opportunity to leverage AI to change the trajectory of their performance – whether it be in ads, search results or in product content descriptions. Those that ensure consumers and machines are at the heart of their strategies will triumph.
Purva Gupta is Co-founder and CEO of Lily AI, a retail technology company empowering retailers and brands by bridging the gap between consumers, merchants, marketers and machines. Leveraging a suite of advanced AI technologies fueled by high-quality, human-verified proprietary data, Lily optimizes product content, enabling retailers to understand complex consumer search behaviors and improve product attributes, titles and descriptions. Recently, Gupta made Inc.’s 2024 Female Founders 250 list and was an Ernst & Young Entrepreneur of the Year finalist. She also is a Tory Burch Fellow and holds an MBA from the Indian School of Business and a Bachelor’s in Economics from Shri Ram College of Commerce (India).
