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Oct 19, 2023 | 6 minute read

Using Composable Commerce as Generative AI’s Frontend

written by Bryan House

Generative AI applications like ChatGPT have captured the world’s imagination about what can be done with AI. But as a recent podcast and article from Benedict Evans point out, the “blank slate” nature of ChatGPT may leave many users with more questions than answers about its utility. Evans argues, “I don’t think a text prompt, a ‘go’ button and a black-box, general purpose text generation engine make up a product, and product takes time.”

In commerce, many have already been experimenting with generative AI applications like OpenAI’s ChatGPT and have seen its potential to make performance marketing easier. From engagement emails to product descriptions, they’re using generative AI to A/B test content and optimize campaigns. Elastic Path has our own OpenAI integration that will draft a new product description and add it as an extended field on your product. You can test dozens of different product descriptions with minimal effort, and optimize for the best conversions.

Which got me thinking, could composable architectures be an accelerant for ChatGPT as a commerce product?

Unbundling, composable commerce and generative AI

By creating an API, OpenAI has essentially left it up to the world to productize generative AI. It makes sense to do so, since the software world has been unbundling into API-connected building blocks for the past decade, with commerce included. The freedom of choice in software has led to an explosion of best-for-me architectures, where customers choose the components they value most for their business. Why? Commoditized, all-in-one software platforms won’t drive competitive differentiation. Commodity breeds sameness.

In commerce, there could be hundreds or thousands of ways to productize ChatGPT via composable commerce applications. The power, in this case, lies in the specificity of applications, rather than the general-purpose prompt box to “send a message” as the main user interface for ChatGPT. Performance marketing is perhaps the first use case that’s unlocked the utility of ChatGPT — marketers’ time can be better spent more strategically than developing iterations of content to A/B test.

Here are a few examples of areas for generative AI-as-product in commerce.

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Product descriptions

More than half of U.S. online customers will abandon their purchase if they can’t find an answer to a quick question, which can often be accomplished with an optimized product landing page and product description.

As noted above, generative AI takes the hard work out of developing alternative product descriptions, so you can quickly optimize your product landing pages based on what works best for your customers. For Elastic Path users, the power lies in configuring and integrating the Elastic Path OpenAI integration with our existing Commerce Manager, which is integrated directly into Product Experience Manager. With no code required, you have a purpose-built generative AI-powered application with genuine utility.

Curated shopping experiences

As a recent Fast Company article covers, some early innovators like Stitch Fix trained algorithms on shopper data to offer curated products based on similar shoppers’ preferences. This type of experience takes the burden off of the shopper to find what they need (escaping the “tyranny of the grid”) and brings relevant products to them. Some brands, like Hungryroot, anticipate that generative AI can serve as a sort of “shopping companion,” explaining why a certain recommendation was made. Overall, as the article notes, generative AI can make shopping research much easier by allowing you to compare two products side by side, or virtually try on apparel.

Engagement emails and ads

Similarly to product descriptions, generative AI applications can be trained on your brand’s voice to create compelling email marketing campaigns and ads — and integrated into an overall composable commerce ecosystem. Subject lines, headlines and CTAs can be optimized for the highest possible engagement and tested against other human-generated or AI-generated copy to see what works best. Over time, you can explore trends in what works and doesn’t work using AI-powered analytics tools, developing the right copy and creative that engages and converts your particular audience.

Dynamic landing pages for social commerce

It’s hard to underestimate the power of algorithms for trendsetting, especially when it comes to social platforms like TikTok. A growing number of Gen Z’ers use TikTok as their search engine for product discovery, and with TikTok Shop rolling out to 150 million U.S. users by October, you can expect its commerce strategy to become even more influential.

If you’re a brand experimenting with new commerce channels like TikTok Shop or whatever happens to be next, it’s important to understand that you’re competing against a social network’s own commerce strategy. Shoppable landing pages are one way to maintain control over your brand, your data, and the customer experience — while making a campaign relevant to a specific creator or goal.

That is where composable commerce applications like CX Studio come in as a potential generative AI frontend. In this use case, ChatGPT could help marketers scale content generation with their creator- or campaign-based shoppable landing pages.

Composable architectures accelerate ChatGPT-as-product

The examples above are just a few use cases for generative AI as a commerce software product today and in the near future. With the imagination and potential of composable commerce application developers, there could be infinite “frontends” that increase the utility of generative AI for specific purposes.

In a way, composable architectures can enable developers to find product-market fit for generative AI in commerce and beyond. The market will decide which applications have the most utility and accuracy for their business purposes. Merchandisers and marketers will dictate what works in a “best-for-me” architecture. The best thing about the API economy is that it lowers the barrier to experimentation with ChatGPT — the products that succeed will use ChatGPT’s pattern-matching to scale human capability, enhance conversions, and drive real business value.

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