How AI Cataloging can supercharge eCommerce strategy
Fashion lovers are turning to online shopping more than ever before. So much so, that Forrester Research predicts the number of global online fashion buyers will reach close to 911 million by 2022, making fashion the largest category of online buyers overall.
As online retail matures, it’s fundamental for retailers to embrace tools that help them innovate quick, easy and at scale. AI cataloging is one of those tools.
What is AI cataloging?
AI cataloging automates the process of tagging items on a retailer’s website. When done manually, cataloging can take up much more time to complete, while AI saves you and your team up to 90% in tagging time. For example, AI cataloging can tag 5-6000 items in two to three days, as opposed to two to three weeks when done manually. In addition to saving time, it also eliminates lost sales due to delayed time to market.
Starting with AutoScribe, Streamoid’s AI cataloging tool is easy. Simply upload your products (via an image or CSV file with an image), and we’ll get to work. While our system has over 95% accuracy, it is also aware that some tags will need to be manually checked for quality control. It automatically selects tags that need to be reviewed (organised by category and attributes) to ensure you can get 100% accurate data while saving upto 80% of quality control time.
Cataloging uses a process called active learning, to learn from its mistakes. So, the more feedback it is given, the more accurate it becomes.
SEO Relevance with AI cataloging
Another benefit of AI cataloging is improving the SEO relevance of your products. With Streamoid’s AutoScribe system products get between 10 - 12 tags, compared to 4-5 tags when done manually. But of course, the number of tags doesn’t make much difference if they aren’t relevant.
Streamoid’s AI cataloging tool uses its own fashion taxonomy to ensure including the most relevant tags for each product.
A great example of this is our work with Target. Understanding that customers need better alignment when shopping on its website, Target turned to Streamoid’s AutoScribe. This resulted in more product attributes being identified and associated for each item. For example, instead of tagging a dress as “casual”, we identified products as “evening casual”, enriching the “occasion” element even more. Our AI cataloging tool also enriched attributes including dress type, activities / occasion, shoulder type, sleeve length, shoulder length, hemline length and neck type.
How AutoScribe benefits your eCommerce strategy
Adding rich and contextual tags increases in-site discoverability and serves shoppers with much more relevant products which are likely to convert. By using our AI cataloging tool Target increased its click through rate by 50%, add to cart by 100% and ROI - by 24x.
Beyond the storefront, AI cataloging can be extremely helpful when it comes to your content marketing efforts. Accurate product tags help your editorial team find what they’re looking for quick and easy.
Perhaps one of the most important benefits of our AI cataloging tool is its use in predictive analytics. Our rich, contextual tags let retailers learn a lot more about their customers’ behaviours and preferences. This allows brands to create better personalised offers, defect and analyse trends and plan smartly for designs, buying and pricing for the season ahead.
Setting up Streamoid’s AutoScribe doesn’t require technical knowledge, so you can start straight away. The only thing you have to do is login with your account within the browser you’re using - no app installation means that you can operate it from anywhere. Once you’re all set and your catalog has been tagged, you can simply download the data in a CSV format (compatible with almost all platforms) and incorporate it on your site.
Automated cataloging is a must-have for retailers that want to streamline their processes, improve their teams’ efficiency and increase the average basket size on their websites. It’s the “invisible” ally brands must look to integrate in their operations.
Authored by Team Streamoid