Generative AI and Retail: Beyond Skateboarding Pandas

Many executives in the retail industry have had the opportunity to experiment with generative artificial intelligence tools. The experiment will have a surreal quality, like the one above, created by requesting a photo of a panda wearing sneakers and riding a skateboard in Times Square from his DALL-E tool in OpenAI. sometimes. But emerging applications of generative AI in retail are certainly specific and will have far-reaching consequences.

First, remember where you were in AI and what changed. Traditional AI solves specific problems or makes specific predictions. You can rely on algorithms to learn patterns and structures in your data and apply that learning to new data. Generative AI also analyzes huge data sets for patterns, but the big difference is that it uses that analysis to generate original content that is new to the world, such as text, images, music (video not too far must).

Agile companies across industries are starting to innovate with this new technology. Coca-Cola, for example, quickly launched an AI platform. This allows digital creatives to create original art using iconic Coke brand assets, such as the signature contoured bottle. Jointly built with OpenAI and Bain & Company, the platform utilizes DALL-E and its text input sister, GPT-4. For Coca-Cola, this is just the beginning of a broader push for generative AI.

As it stands, emerging use cases for generative AI in retail can be broadly classified into four categories: personalized marketing, customer engagement and service transformation, operations and productivity, and customer and industry insights.

personalization marketing

Generative AI enables personalized marketing on a scale that was previously impractical. Consider his email campaign targeting thousands of consumers. Technology can adapt core-her messages to each recipient, using resonant language and incentives based on past preferences. For grocery shoppers who care most about deals, you can create emails with coupons that emphasize value. For those who consider themselves foodies, you can celebrate the origin of the food and include recipes.

Marketing professionals can be more productive with generative AI tools. For example, you can use them to generate first drafts of blog posts or creative images (any style, photorealistic, cartoon, abstract, etc.) that you can review and review. refine. Once the content is complete, generative AI can create derivative assets (such as photos resized for social media) much faster, freeing up time for higher-level tasks.

Generative AI can be woven into the fabric of a retailer’s website or app through individually tailored landing pages, product descriptions, and illustrations. Apparel retailers have the opportunity to better visualize how their customers wear their clothing by using generative AI to alter their photos.

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There are also disruptive possibilities that do not easily fit into current marketing categories, such as a “closet concierge” that suggests recommended recipes based on photos of the customer’s refrigerator or coordinates based on photos of the customer’s existing wardrobe. There is also nature of the opportunity.

Customer engagement and service transformation

Compared to more basic chatbots with templated responses, virtual assistants powered by generative AI offer a much better experience when shoppers need help or inspiration. increase. This is especially true as modern technology allows us to better contextualize our interactions with previously collected information.

Consider a customer looking for new running shoes while sprinting between meetings. Speaking naturally to her own device rather than typing, she asks her AI assistant at the retailer for the best pair for runners who log up to 15km a week. The assistant highlights the option and puts the customer’s previous iteration of shoes at the top of the list. She buys her familiar pair after accurately answering her verbal follow-up questions about whether her size is in stock and how long her delivery will take.

Tools such as these can lead to poor search on some retail websites, given that plugging in generative AI expertise via APIs is easier than upgrading an in-house search infrastructure. Offers a compelling fix to your experience. AI also enables multimodal search. This allows shoppers to no longer be limited to searching by text or keywords, but to take advantage of other starting points such as photos, audio, and video.

For customer service agents, generative AI can recommend scripts to follow in calls, suggest targeted offers that might entice customers, and create summaries of each conversation. Generative AI is already accelerating social and media interactions by suggesting possible responses to customer queries and comments. These prompts help social media specialists engage with more people. However, customers will notice companies that seem personally involved but do not implement the points they have raised.

Operations and productivity

Retail executives may already be harnessing the potential of generative AI in their personal abilities, for example, using OpenAI’s speech recognition system, Whisper, to automatically take meeting notes. there is. This is just the beginning of the operational benefits across organizations, as generative AI provides staff with tools that can help accelerate commoditized tasks.

Cover knowledge management at the forefront. Retail employees are bombarded with training documents when they join the company. It’s difficult to retain and use that information when needed, such as when grocery store employees are stationed at the meat counter and must adhere to strict hygiene rules. Generative AI chatbots can prompt and direct employees when they need them, boosting confidence and productivity with conversational tone. The technology can also codify best practices from unstructured data from top-performing stores and integrate that information into chatbots.

Vendor management is another operational area ripe for AI assistance. Many retailers today handle thousands of vendor relationships through large teams of experts. Generative AI can ease that load. You can automate (or semi-automate) interactions with some vendors by performing tasks such as creating a request for proposal (RFP), summarizing meetings, and writing follow-up emails.

Other back-office uses include generating text for job descriptions. The technology also reveals enhancements that retailers are not yet aware of. For example, by identifying scalable best practices from large amounts of internally generated information that has not yet been fully analyzed or collected. This “Capture and Organize” playbook should help retailers. And once you know best practices, you can use generative AI to automatically create or augment training and onboarding materials.

Customer and Industry Insights

New ways to analyze customer sentiment and loyalty are emerging. Generative AI can monitor the content and tone of customer interactions (phone calls, online chats) and assess in real time whether shoppers are satisfied or dissatisfied with the information they receive and how it is delivered. Measuring customer advocacy through metrics such as the Net Promoter Score℠ becomes a predictive rather than a reactive process.

Generative AI better understands structured feedback such as surveys, generating sharper insights at the level of individual stores, as well as providing a clearer view of the big picture. Its ability to handle vast amounts of unstructured feedback, such as free-form comments on social media, will be transformative. The ability to integrate this feedback with key industry, market, and category-level data enables real-time adjustments to products and assortments to changing sentiments, while generating new product and service ideas.

Online marketplace operators can also use generative AI to standardize how third-party sellers list their products online. This is thanks to uniform keywords and product descriptions that are automatically generated from product photos.

The present of AI is catching up with the future we imagine

Executive teams face serious strategic planning challenges when analyzing potential applications for generative AI. In many cases, the seemingly futuristic applications that emerge from their brainstorming sessions aren’t all that futuristic. Recent advances in technology are really big.

The immediate applicability of these AI advances is a far cry from the slow burn associated with other futuristic technologies such as blockchain and augmented reality, making a wait-and-see approach particularly risky for retailers. means high. Even in the face of uncertainty, there are good reasons to apply boldly. Today’s testing and learning approaches can develop repeatable processes that can be widely deployed tomorrow. AI will be at the core of the retail industry. Companies that only stop to check their inventory may never recover from the head start given to their rivals.