Automated product tagging uses AI technology to create descriptive product labels for retailers to list on their websites and sell on their ecommerce sites. It does so by scanning photographs, images, or media content, and recommending various attributes to highlight the items.
In retail, tagging new products for sales can be a major pain point. Tagging your images and products correctly with good descriptions is extremely important to get good search results. It may not seem like such a big deal to attribute labels to images of your products when you are only updating five or ten items. But think about a major retailer with a product catalog of thousands of items. Think of any fashion retailer that must manually tag thousands of new fashion items and accessories each season as the trends change.
From a retailer's perspective, this tagging process is part of good inventory or catalog management. It helps with online product discovery. And, until now, product images were scanned by people looking for visual attributes about the items to call out, or label. To get accurate search results, people would scan the items to identify notables, colors, and other attributes for which consumers run online searches. During high inventory turnover periods like the end of a season with a full new fashion lineup, that meant dramatically scaling up staff practices at product tagging, with upswings in operational costs, and massively busy periods with considerable chaos and confusion.
Automated product tagging uses AI technology (neural networks, deep learning, etc), to improve productivity for retailers by providing the descriptive labeling needed as your merchandise product data. Automatic product tagging significantly reduces the amount of time and effort it takes to tag an entire catalog of goods. Not only does it reduce time and effort, but it can also dramatically improve the consistency and thoroughness of your retail catalog labels and descriptions. That's key to your search engine performance and findability.
Think of it this way, if an auto product tagging system contends with the bulk of the attribute labeling for your new product lineup, then your people can focus on just reviewing the choices made by the AI system. In doing so, they aren't tired-out due to the sheer volume of items. It is also far easier to review the suggestions and create important new ones, than it is to create all-new descriptions from scratch for several thousand items.
With the help of powerful artificial intelligence algorithms, automated tagging organizes and labels photographs in the product catalog, based on various properties. Creating tags for images allows for more efficient search and retrieval. Used effectively, it lets your staff focus on improving the descriptions of the images tagged rather than drowning in the bulk of the activities.
Imagine being able to save hours on creating accurately labeled text for products in a catalog, improving the visibility of your business online, and being able to accurately describe products without the need for human intervention. Ultimately, this saves a retailer money by reducing mass staffing needs to accomplish this task. Reviewing the AI-generated suggestions is both easier and faster than creating all-new labels.
The challenge for automated product tagging is that not all solutions available in the martech space are perfect. Some AI solutions require extensive training before and after implementation, which can produce results that are not necessarily perfect. With OSF’s TagSonic however, this training is unnecessary, as the AI has already been trained over millions of images. The need to have a person do a quick scan of the recommendations before going live is still a good practice, though. Although modern systems are now quite exceptional at spotting the intended subject, your human employees can do a quick scan to double-check the work and descriptive results.
Producing high-quality images of your products and services is key to your marketing success on online platforms like Instagram, Pinterest, and Facebook. To have an effective social media campaign, you need good photos of your products because that’s what draws attention. When looking through the products on retail websites, people usually click on the first few items they see, so having an attractive photo can increase your conversion rates. Automated product tagging is not a new concept, but it does work well for retailers who have a large inventory to tag or even for companies that sell multiple types of products.
The benefits of automated product tagging are clear: better information for customers, improved findability by search engines, and speed to market your products online— all while providing a tremendous opportunity for retailers to save money in staffing as well as reduce the number of returns. All told, an automatic product tagging solution dramatically improves the efficiency of a retailer's traditional labeling process. The drawbacks? Finding a system that can do so with minimal supervision is small in comparison to seasonal staffing and training costs.
Luckily, automated product tagging is not an all-or-nothing commitment. You can start small, using it to tag the products on your site that are the most popular or that you have the most of, to see if it works for you. If it does, you can start tagging more items, by category, size, color, or seasonal lineup. The key is to build the processes and check that it works for you and your business.
Automated product tagging is definitely one of the technologies that no retailer should consider going without. This can seriously improve the industry, quietly. One product attribute at a time.
Simion began his career at OSF in 2007 when he entered the internship program as a software developer and quickly ascended within the company holding roles as a team leader and project manager. From 2013 to 2018, Simion established and optimized the company's commerce division, growing the group to over 400 people who delivered over 200 projects, making OSF Digital one of Salesforce’s top ecommerce partners. He has created new local structures designed to support the company's rapid growth in APAC and LATAM while at the same time, worked on innovation of the company's processes, best practices, and products including the creation of the OSF Product Labs program.
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