GenAI use cases for the digital shelf: What you need to know

    Stephanie Rubin Senior Director-Solutions Consulting(Customer Growth)
    December 4, 2023
    generativeA use cases for the digital shelf what you need to know

    Try CommerceIQ Insights, our free GenAI-powered Chrome Plugin that helps you optimize your content and analyze sentiment for any product on Amazon!

    It’s all anyone has been talking about.

    Ever since ChatGPT launched to the public, consumer brands everywhere have been excited about the possibility of what GenAI can do to business efficiency overall, and ecommerce specifically. 

    It’s early days, so ‘test and learn’ is the focus for most right now, but we are already starting to see those experiments coalesce into a few key use cases. 

    From an ecommerce perspective, three areas loom large: content generation and optimization, image optimization, and personalized marketing.

    The ‘findability’ of your product is critical to conversion. If shoppers can’t find your product, the switch to your competitor is just one click away.

    Download our free guide to learn

    • Why product content management is critical to your CPG ecommerce success
    • Fundamentals of product content excellence
    • How to combine Digital Shelf Analytics (DSA) with Product Information Management (PIM)
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    Here is how consumer brands can successfully leverage GenAI across these three areas and accelerate, amplify, and streamline their omnichannel activities.

    1. Generating and optimizing ecommerce content

    Content is the big near-term opportunity for generative AI in ecommerce.

    Today, creating and maintaining good ecommerce content is a highly labor-intensive process. 

    Every retailer has their own rules and standards for product titles and product descriptions, and their own algorithms that dictate how content informs visibility and organic search performance. It takes brands a huge amount of time and effort to generate content that meets the needs of every single retailer.

    What’s more, content constantly needs to be updated to reflect changing market needs and consumer trends. Ecommerce is highly dynamic – the keywords that worked at launch are unlikely to be effective two years down the line. Brands that adopt a ‘set it and forget it’ mentality end up with content that is woefully out of date, which in turn leads to poor search performance and reduced visibility.

    Generative AI has the potential to make the entire content creation process vastly more efficient. 

    GenAI tools can recommend optimized product titles and descriptions based on what’s trending right now, as well as taking into account retailer and brand-specific guidelines. 

    GenAI can also make sense of ratings and reviews, and suggest optimizations based on what consumers are saying about a product. Our new ChatGPT plugin for Amazon, which is currently in beta for CommerceIQ clients, is a case in point. It recommends optimized product titles and bullet points, and also produces sentiment analysis based on the 60 most recent reviews to suggest optimizations.

    Retailers are experimenting with similar tools. Amazon has announced plan to deploy GenAI to summarize product reviews, making it easier for shoppers to see common themes across reviews.

    For now, brands will want a human to check any AI-generated content for accuracy and compliance before implementing it. The lighthouse, however, is that GenAI will ultimately make those optimizations automatically, with no human input required, creating huge efficiencies and cost savings for brands

    2. Generating and optimizing images

    Whether it’s product pack shots or mobile-ready hero images, consumer brands spend staggering amounts of money on photography for optimized images.

    Generative AI can drive significant cost savings by allowing brands to use a single photograph and text-to-image prompts to generate as many optimized images as they need, instead of requiring each image to be photographed separately.

    Google, for example, recently launched a tool called Product Studio which enables brands to generate product imagery using generative AI. Targeted at small and medium-sized businesses, the tool can be used to create different stylings and seasonal variations of existing images. It can also remove distracting backgrounds from product shots and improve the product quality of low-res images.

    3. Delivering personalized marketing at scale

    Marketing is a key use case for generative AI in general. Bain describes it as a GenAI ‘hotspot’ because of its blend of creative and data-driven work, while McKinsey sees marketing as one of four areas that are set to generate the bulk of value from GenAI.

    Already, major brands are experimenting with GenAI-driven marketing, with Coca-Cola’s Create Real Magic campaign and Coke AI Studio among the key examples. 

    From an ecommerce and online retail context, GenAI’s ability to deliver personalized marketing is particularly enticing.

    Personalization is an important – and growing – trend for consumer brands.  However, creating truly personalized marketing campaigns takes a lot of time, effort, and money right now.

    Generative AI can help brands tailor their digital marketing copy and ad content based on consumers’ individual habits and needs, and do so in a way that is much more scalable and efficient than is currently possible.

    GenAI’s predictive abilities can also be used to provide personalized recommendations to shoppers during the shopping experience itself. Plus, they can help brands understand their shopper and better predict future purchasing behavior.

    Retailers are also starting to experiment with GenAI to deliver personalization. Instacart, for example, has launched Ask Instacart, an AI-powered search tool that answers shoppers’ questions and provides personalized product recommendations, while France’s Carrefour has launched a chatbot called Hopla that assists shoppers in finding grocery products that suit their lifestyles, dietary preferences, and budgets.

    4. What are the risks of using GenAI for ecommerce?

    As several high-profile lawsuits have shown, there are potential legal risks involved in using generative AI – not least because it isn’t always clear how and where AI tools get their information. 

    When using GenAI to create product content, the main risk is that the AI comes up with claims that don’t conform to brand standards or legal requirements. This is why human oversight remains essential, at least in the early days.

    Also, using GenAI is an art and a science. A computer model might not immediately grasp the full feel of a brand or what it’s trying to communicate to consumers, so content might initially feel wide of the mark. The model gets smarter as it is trained, so full automation is feasible in the long term, but it’s a case of crawl, walk, run to get there. 

    GenAI is a true game-changer for ecommerce and the possibilities are hugely exciting, but there’s also a lot of hype right now. It will take time for the technology to be refined and the most compelling use case to emerge. Brands should experiment, see what works for them, and resist pressure to launch head-first into costly GenAI projects just for the sake of it. 

    The ‘findability’ of your product is critical to conversion. If shoppers can’t find your product, the switch to your competitor is just one click away.

    Download our free guide to learn

    • Why product content management is critical to your CPG ecommerce success
    • Fundamentals of product content excellence
    • How to combine Digital Shelf Analytics (DSA) with Product Information Management (PIM)
    Your Image

    Stephanie Rubin Senior Director-Solutions Consulting(Customer Growth)


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