The launch of Shopify’s Custom Data platform aimed to enable building and integrating content and custom data attributes with native Shopify tools, which previously required third-party partner apps or costly custom technology stacks.
However, Custom Data was initially born from explorations solely intent on building a Content Management System (CMS). Before the team had gone too far down that road, our user data showed that more advanced data and content modeling tools like Contentful or Sanity.io were swiftly becoming the go-to choice for large and enterprise merchants integrating with Shopify—effectively making Shopify a spoke in their e-commerce tech stack rather than a hub where merchant data and content could be centrally managed and leveraged throughout the rest of the platform with ease.
Unfortunately for merchants, these powerful tools come with a steep learning curve and additional expenses and complexities. So, rather than build a small, basic set of CMS features, we pivoted to build a foundation for managing bespoke data structures, which we knew would inevitably include customer-facing content.
With the launch of Custom Data, the next priority was streamlining the content creation process for merchants. I formed a team of UXers around this subset of user flows, and we got stuck into the Content Management roadmap. Shortly after, an opportune collaboration with OpenAI and the GPT-3 large language model (LLM) paved the way for Shopify Magic—a growing suite of features enabling merchants building storefront content to quickly move beyond the blank page.
Depending on their brand needs, customer segments, and the sheer amount of inventory they are listing, it can take merchants weeks or longer to prepare the content for their storefronts. The longer a merchant takes to launch their store, the larger their opportunity cost and the higher the likelihood that their endeavors will come to a screeching halt. Building and managing this content can quickly escalate into a full-time position for many small merchants. Merchants with staff for building content don’t want to waste time building or integrating the tools to manage it before they can start.
So, as a part of the Content Experiences group, the Custom Data product area was responsible for understanding, designing, and enabling many of the primary use cases of Shopify Magic. We commenced with a multi-pronged user research effort to identify and understand these use cases.
Our journey to define the core tasks where Shopify Magic could have the most impact began with an intensive user research phase. We conducted quantitative research, observational sessions, and segmented usability surveys to understand merchant behavior and their underlying needs better.
Quantitative Research: Working with our data partners, we identified a trend in a subset of merchants where they signaled relatively strong intent to build content within our CMS, but we hit a wall in creating content entries. Often, smaller merchants would opt-in to the onboarding for Custom Data, and some would even build their first content models. Many would create an initial entry only to delete it within a few days or weeks. Others would never create an entry to begin with. These trends gave us a great place to dig into.
Observational User Sessions: We observed and understood how merchants perform daily tasks. The process involved breaking down workflows into discrete tasks based on how users conceptualize their work. This exercise gave us an intimate view of the merchants’ writing process, modeling content, and categorizing products. We also spoke to a swath of merchants who had built models without entries to understand if barriers kept them from progressing with their content. We saw that while many merchants intended to round out their stores with content—some even with strategic ideas about the kind of content they wanted to build—they often didn’t understand tactically what a good content model looked like.
Segmented Usability Surveys: We wanted to understand the usability barriers merchants face in their everyday operations. We segmented our merchants based on their size, experience, and other factors to get a nuanced understanding of their specific challenges. Through these surveys, we identified the most time-consuming and complex tasks for each merchant segment that became the prime candidates for Shopify Magic.
As a result of our research, we identified the following user tasks as key areas where Shopify Magic could make a significant difference:
Shopify Magic offers an optional, streamlined set of features integrated within existing merchant workflows. The UX must be highly contextual, flexible, and lightweight.
Product descriptions are key to customer engagement and sales growth. Our team aimed to simplify this process while maintaining high-quality content. However, we understood the importance of a user-friendly experience for our merchants.
We recognized that not all merchants would want to use this feature as a primary tool for writing product descriptions. Some may only need to make minor adjustments to their product pages, while others might prefer to bypass this feature altogether. Therefore, we designed the feature to be visible but not intrusive, using a field flag UI component with the Shopify Magic icon.
Initial tests allowed Autowrite to generate content based solely on a product title, without any keywords in the description field. However, the results were often generic and inaccurate. We realized that product names often reflect brand positioning rather than item descriptions, and unique products may not be well represented by an "average" description.
We decided to decouple content generation from the Product Title field, requiring at least 2 keywords in the description field to generate a more accurate product description.
Another challenge was deciding where to place the generated content. We wanted to ensure that the feature would not overwrite any existing content in the product description field. We found a solution by displaying the generated content outside the description field until a merchant chose to keep it.
Ultimately, we designed the product description card to expand contextually, housing the content and generative controls just below the description field. This allowed merchants to select a tone, rewrite, keep, and provide feedback to our model, while comparing the generated description to their existing content.
Enabling the feature for auto-generating image alt text was a clear "quick win" for merchants, even if they weren't explicitly asking for it. Our research indicated that many merchants overlook this task due to unawareness of its impact on SEO, accessibility, and discoverability. Moreover, image alt text works best when it's concise and descriptive.
Given these insights, we decided to auto-generate content for image assets lacking alt text, subtly prompting users to either keep or rewrite it. We aimed to integrate this process seamlessly into the users' workflow, requiring minimal effort—ideally, just a single click. This approach ensured that merchants who prefer to provide their own alt text faced no hindrance.
Building custom-fit content models accelerates content creation and guarantees the inclusion of key information. This enhances merchant efficiency and drives buyer conversion through standardized attributes.
Building custom-fit content models expedites content creation and ensures the consistent presence of crucial information, boosting merchant efficiency through standardized attributes that drive buyer conversion.
The most significant problems merchants have in building their content models is determining what constituent components are needed to effectively tell a story consistently across all the surfaces where merchants implement them and which of the available data types best fit each component. The best models are highly reusable and reflect the standards and nuances of the market each merchant is selling in. Building and then maintaining those standards for every possible market or industry requires a herculean effort, which is one reason no one in the CMS space has done it yet. With Shopify Magic, I saw an opportunity to skip ahead several years and construct these standards for each merchant—essentially in real time—and I tasked one of my product designers and one of my creative technologists to build a prototype of this quickly. The results were so gratifying that we had to get this out to merchants.
By including the available data types and their corresponding field validations in the prompt to GPT and the kind of content the merchant is trying to craft drawn from the model name field, we can build the model for them to a high standard in most cases. Even so, users must be able to pick and choose only those components they know will work for their storefront and discard the rest of the suggestions quickly.
Shopify Magic intelligently suggests product attributes based on the merchant’s storefront profile, inventory, and product description to classify items within a well-defined taxonomy automatically. This process enables customers to search and filter products easily, enhancing the overall shopping experience.
Creating a universal product taxonomy that aligns with industry models and merchant mental models is an ongoing process. The key is to make the experience so intuitive that manual methods can't compete.
My team and I collaborated across product areas to leverage existing merchant input, aiming to automate as much as possible. After a couple of months, we developed an MVP that met merchant needs and aligned with external channel taxonomies like Google and Facebook.
We utilized product name, description, and images, along with the storefront's category, to suggest the most relevant categorization scheme with pre-filled attribute values. Merchants simply needed to confirm these, but could also freely edit them, acknowledging the variability in terminologies across companies.
The team's success in this project, soon to be rolled out globally, was due to understanding merchant tasks and challenges, and achieving broad alignment. Auto-categorization and attribution were features that could only have been realized with UX leading the organization. I'm extremely proud of the work we accomplished together.
Beta testing for the other features, such as auto-generating image alt text, content models, and product categorization, has also shown strong demand. Feedback from beta users and early adopters indicates that these features address critical pain points for merchants, simplify their workflow, and reduce the time and effort required to create and manage high-quality content. The team plans to launch these features globally within the next few months.
The rollout of Shopify Magic has generated significant interest and enthusiasm within the merchant community. The adoption rate has been remarkable in just a few short months since its launch. Currently, tens of thousands of product descriptions generated by the tool are being used on public storefronts, showcasing its widespread appeal and effectiveness. Many merchants are still testing the feature, highlighting its potential for continued growth.
Shopify Magic has demonstrated its capacity to significantly decrease the time from merchant sign-up to the first sale. Merchants have reported increased efficiency, improved content quality, and enhanced customer user experiences, leading to higher conversion rates and sales growth.
Moreover, the successful implementation of Shopify Magic has reinforced Shopify’s commitment to providing innovative and user-friendly tools for merchants and showcased the potential for further collaboration with AI and ML technologies. Moving forward, the team will continue to iterate on the existing features, gather valuable user feedback, and explore new possibilities to empower merchants with even more efficient and effective tools for their e-commerce businesses.
In summary, Shopify Magic’s strong initial adoption and positive results underscore its value in helping merchants launch their online storefronts quickly and effectively. As the platform continues to evolve and expand, it is poised to become an indispensable tool for e-commerce merchants looking to build high-conversion, quality storefronts with minimal time and effort.