In an increasingly AI-dominated retail and e-commerce world, basic product copy and bare minimum product data simply no longer cut it.
Both because:
- High-quality product data is rocket fuel for both traditional and AI-powered search and discovery
- AI can enrich and optimize the product data itself, at the source
Retailers and brands must invest in a product content optimization strategy that can dynamically keep up with today’s consumers and is fluent in what we refer to at Lily as consumer, merchant, marketer, and machine “speak” (we’ll touch more on that later in the post).
If it all starts with product content, what is “Product Content” defined as, exactly?
– Product content includes the attributes and copy that consumers see, as well as invisible metadata.
– Product content optimization refers to the dynamic process of improving and expanding product information, such as adding more details, attributes, specifications, descriptions, highlights, and other labels or tags to a catalog, or more specifically, the product record, product listing, product ad, or product detail page (PDP).
Here are the three things every retailer, brand, and digital agency needs to know about product content optimization.
1) Product Content Is No Longer Static
Gone are the days when product content generation was a one-time exercise when a new product was brought to market. Consumer language is dynamic, so your product content should be too.
Consider that:
– Seasons change (a spring dress becomes a summer dress)
– New trends emerge from micro trends to macro trends
– Consumer vocabularies evolve and also vary by region
All of this contributes to how today’s consumer describes products differently than even yesterday, as context changes. After all, SEO was never static and thrives on fresh content!
Product content optimization today is a dynamic feedback loop that interprets, distills, optimizes, and distributes enriched product content to all destination systems.
By the Way, Do Product Descriptions Still Matter?
Yes! In fact, Lily AI’s 2025 consumer shopping survey showed that 52% of consumers report that short product descriptions are one of the most commonly used content to help with purchase decisions.
From the research, 44% also reported using longer product descriptions to inform their shopping decisions.
2) Product Content is Not Just What Consumers See
Consumer-facing product content includes both titles and descriptions but also product images, videos, and reviews, to name a few. All of these help consumers evaluate products. But which of these helps consumers find the products in the first place?
To be sold, products first need to be found.
That’s where product data comes in, and more specifically, metadata and product data feeds.
Product Feeds:
From Google to Amazon to Meta, you name it, it’s a product data feed world when it comes to online shopping. Product data feed platforms like Feedonomics, DataFeedWatch, and ProductsUp enable seamless distribution of product information across various channels like marketplaces, search engines, and social platforms, ensuring consistency and accuracy in how products are presented to consumers.
Yet, distributing data is the (relatively) easy part; enriching and optimizing the product data is another story.
In product feeds, there are basic field requirements, such as a title, price, and images, and then there are optional fields such as additional product attributes. These optional fields are often left blank or incomplete because the marketer, or their digital agency, simply does not have the data or a process to enrich it. And who can blame them? With Google alone, it’s hard enough keeping up with the product data specs as is!
– Pro-Tip: Lily AI’s GMC integration makes product content optimization a breeze!
Product Metadata:
Metadata encompasses detailed product information—such as attributes, categories, and tags—that enhances search engine visibility and ensures products appear in relevant customer searches. By enriching the product metadata and schema markups on the PDP, brands and retailers can add more structured depth to their product information to boost search engine, answer engine, and Agentic AI discoverability.
An added bonus? There are two, actually! The same metadata enrichment optimizes:
1) Site Search: Regardless of which site search platform you’re using. From site search leaders Algolia to Bloomreach, to built-in-house systems or off-the-shelf Google site search solutions, all search is optimized with enriched product data.
2) Search Ads: From traditional Shopping Ads to PMax, enriched PDP copy and metadata increase landing page relevance. Landing page relevance improves ad quality scores, which helps advertisers boost ad visibility and cost-effectively drive more impressions and clicks, without increasing media spend!
3) Product Content is Not (only) Merchant Speak
Product content enrichment refers to the process of improving and expanding product information to make it more complete, accurate, and compelling for consumers, as well as merchants, marketers, and machines. Let’s dive into what that really means here.
Consumer Speak: Connect with Intent
Consumer speak is a dynamic, ever-evolving natural language lexicon influenced by (but not limited to!) pop culture and social media. From catching vibes to vibe coding, the way we all speak changes almost by the day these days!
To complicate this further, even how we search varies by platform. On Amazon, you might simply search for “Accent white chair” yet on ChatGPT your prompt is much longer and more complex.
As new terms emerge and gain traction through social platforms and cultural phenomena, they influence how people express identity and engage with brands.
For marketers and merchants, staying attuned to this shifting linguistic landscape is crucial to crafting messages that resonate authentically with their audience. And while both micro and macro trends certainly inspire shopping decisions and motivations, so too does the need to shop by the occasion or need, to help achieve whatever desired aesthetic or functional purpose.
This is more than the behavioral language insights gleaned from clickstream or on-site search data. To capture the most holistic view of consumer language today, retailers and brands need to harness both e-commerce data as well as SEO and social media data.
Merchant Speak: Ensure Accuracy and Consistency
Product content optimization starts with merchandising teams since they are critical stakeholders in any product data strategy.
Before any product data optimization exercise, the core details, such as item type, features, and color, must first be verified. Every time data changes hands between the manufacturer to wholesaler, brand, retailer, marketplace, and resaler, it’s an opportunity for data loss or degradation.
Once product data accuracy is confirmed (an excellent use case for Computer Vision and Natural Language Processing, in particular!), then begins the data enrichment process.
This includes:
– Filling Gaps: Such as embellishment details or more nuanced fit elements (sleeve fit), that might have been skipped or overlooked.
– New Subjective Details: Adding additional details that can be harder to objectively define, such as trends, styles, occasions, or micro-occasions.
– Adding Synonyms: Ensuring relevant synonyms are captured for all of these attributes, since back to the previous point above around consumer speak, everyone talks, searches, and shops differently. Think trainers vs tennis shoes vs sneakers, as an example.
Marketer Speak: Optimize for Discoverability
Product content requires meticulous enrichment and dynamic refreshes for optimized visibility across organic search (SEO), paid search (SEM), and e-commerce on-site search.
– Natural Language Keywords: Use insights from consumer speak to naturally incorporate relevant keywords (long-tail, question-based) into consumer-facing product copy as well as invisible metadata.
– Brand Voice Alignment: While this almost goes without saying, consistency and agreement with brand guidelines are always worth mentioning, especially in the context of consumer-facing content!
– Optimized Google Merchant Center (GMC): Accurate, detailed product feeds are vital, especially for platforms like Google Merchant Center (GMC). Whether paid Product Listing Ads or organic Product Listings, high-quality product data is rocket fuel for optimized organic and paid performance. Fortunately, enriching and managing product content and attributes at scale in the Google Merchant Center is easier than ever with Lily AI!
Machine Speak: Be Found by Google and AI
How can brands and retailers communicate more effectively and efficiently with the search engine giant Google, as well as AI-powered search and AI Agents? By harnessing highly detailed, structured, consumer-friendly product content in every digital channel.
– Schema Markup for Context: Implement structured data (Schema.org) like Product, Offer, Review, and FAQPage schema. This helps search engines and AI understand context, enabling rich snippets (price, ratings in search) and powering accurate AI-driven answers.
– Metadata Unlocks Relevance: Rich metadata—data about your product data—provides essential context for AI. It improves data quality, enables personalization, enhances discoverability, and makes AI outputs more interpretable. AI itself can help extract and manage this metadata.
– The Feedless Future: As part of the 2024 launch of Google Merchant Center Next, GMC can now automatically source new products from an e-commerce site through a feedless process called “Found By Google”, in addition to the feed-driven “Provided By You” data source. This means that enriched and optimized PDP product data will only continue to grow in strategic importance!
Enrich Product Content with Lily AI
Effective product content optimization demands fluency in all four languages: consumer, merchant, marketer, and machine. Mastering this multi-faceted approach—keeping content dynamic and valuing both consumer-visible copy and invisible metadata—is crucial for enhancing discoverability, driving greater productivity and new efficiencies, as well as boosting web traffic and sales.
And we would know, Lily AI is the leader in AI-powered product content optimization, and we see the impact for our clients every day!
Lily AI is transforming eCommerce and advertising by using AI to decode consumers’ true shopping intent and bridge the language and metadata gap between how retailers describe their products and how consumers express what they want. 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, improve product attributes, titles, and descriptions, and personalize shopping experiences, resulting in operational efficiency and increased sales.
Want to learn more or schedule a demo? Let’s talk!