There are a variety of factors shoppers look at when searching for clothes and accessories. Some of the most common factors include style, colour, pattern, length, and occasion. Traditionally e-commerce store owners and brand owners manually add these product specifications to images.
But with automatic product tagging, this is entirely unnecessary. E-commerce automated product tagging uses deep learning to classify and add tags to product images based on the various data points in the image. This is made possible through advanced image recognition algorithms.
E-commerce automated product tagging
Automated e-commerce product tagging is a rule-based process of analysing and labelling a product image in a catalogue by identifying visual attributes. For example, if an e-commerce product catalogue has an image of a violet dress, the image recognition, and Machine Learning technology can ‘tag’ it in a variety of ways such as ‘Knee-length, V-neckline, 3/4th sleeve, violet sheath dress”. This cuts down a significant amount of effort from your end and you only need to approve these automatically-generated tags with one single click.
Benefits of automated product tagging
E-commerce automated product tagging essentially eliminates the need for manual tagging, thereby saving store owners and brand owners ton of their time. Here are some of the internal use cases for automatic product tagging.
Product Catalog Management
Accurate tagging of products to the catalogue images paves the way for optimised and well-organised product feed from the backend. Adding correct tags to product catalogue image helps in tracking offerings, monitor bestsellers, under-performers, and keep a tab on stock levels. You can use automated product tagging based on styles, sleeve lengths, hemlines, colour, pattern, necklines, and occasion. You can add more tags from time to time to stay in line with the latest trends. These tags offer a great way to influence future purchasing decisions to understand sales statistics at the attribute level.
SEO and Searchability
Google’s AI – RankBrain is getting better at understanding the search context and intent. Therefore, the more relevant tags an image has, the better it has the chance to appear in the relevant search result. Automated product tagging automatically annotate images with correct category and attribute labels.
Auto-tagging for Content Creation
E-commerce automated product tagging is not just helpful for a storefront but is also useful during the content creation process. This is an excellent resource if you have a company blog that regularly produces blog content. Accurate product tags will allow content creators to find items they are looking for in real-time quickly.
Increase average order size
Every e-commerce store owner wants its customers to spend more each time they use their eCommerce store. Automated product tagging comes as a great help as it helps store owners to associate items based on product tags automatically and suggest them to customers during their checkout process.
In a real-life scenario, if as a shopper you are searching for a perfect projector and the store owner has tagged that item with ‘Projector’ and ‘Home Theater’ will find suggestions to purchase projector mounts and HDMI cables. These are some of the items that projector buyers also need but may not realise immediately. However, effective product tagging will help buyers realise immediately. However, effective product tagging will help buyers realise that they also need these items and thereby pushing the sales figures up.
The decrease in shopping cart abandonment
One of the biggest reasons shoppers abandon the eCommerce shopping cart is related to price, or the unavailability of the item they want, or they may simply realise that they don’t want the product that they are looking at. Product tags are a common strategy that helps mitigate losses due to these issues by suggesting the shoppers from a great variety’ similar other products, cheaper products, or different product brands or related products to the customer. This gives the shoppers enough opportunity to choose from a great variety.
Predictive Analytics: Informed Business Decisions
Utilise the power of machine learning to identify and understand past performance per product and per attribute.
Understand customer behaviour, style and preferences at individual, customer-segment and regional/geographical layers. Capture and analyse top trends and shifting trends at micro and macro levels.
By spotting all these real-time patterns in your data, you can inform future business decisions at design, buying-merchandising and pricing levels with a lowered risk of bias.
This means you will significantly lower price deductions, overproduction and product waste, benefiting your company financially and helping you create a more sustainable business strategy.
Group and manage your entire inventory for different customer segments, customer tiers and geographical locations by using regional and segment-specific product tags. These tags are flexible, adaptable and responsive to real-time changes in your customer base.
Use micro and macro trend attributes to sort your catalogue. Always stay relevant by addressing the seasonal shifts that affect your customer’s top search words.
Use your product tags to identify and understand each customer’s individual taste and style. Then personalise search results, product pages and similarity recommendations through their favourite attributes.
Easily create and layer attribute ‘filters’ with your product tags. Use unique ‘filters’ to give personalised ‘Complete The Look’ outfit recommendations for every product on your catalogue at a quality that can outperform your top human stylist.
Personalisation can be integrated into multiple customer touchpoints:
- Online recommendations to increase AOV (Average Order Value) and customer engagement.
- In-store clienteling solutions to supercharge your store teams. With the right tools, they can make personalised styling recommendations and up-sell while delivering value to your customers.
- Re-targeting and promotional campaigns that only show personalised, relevant content. Addressing each customer’s unique preferences will increase the ROI and efficiency of your campaigns.
The growing importance of auto-tagging
There is no doubt that auto-tagging products increase operational efficiency and leads to a reduction in manual tagging processes. Auto-tagging technology has become the backbone for various AI-driven products in the retail arena. Automated product tagging technology can identify objects in moving images, videos or to say, any sort of visual content, along with its visual attributes such as clothing, footwear, etc. The technology truly understanding shopper behaviour and preferences. Not only that, the technology constantly gets smarter and smarter through data-driven continuous learning systems. The visually intelligent system can extract multidimensional attributes with auto-tagging engines. Therefore the auto-tagging system engages with the customer in a way that enhances customer experience and leads to higher sales and helps create a holistic persona for each shopper. Automated eCommerce product tagging is key for on-site personalisation. With every click on a product, the system becomes aware of specific attribute-wise preferences, which is then combined with data-driven historical behavioural patterns. This enables the personalisation engine to become much more powerful and accurate in interpreting the shoppers intent. The product tagging system quickly learns with every event click and draws customised fashion choices for each user in real-time.
Besides, personalised recommendations is the core of the auto-tagging feature. It is also suitable for building other types of on-site recommendations such as – Visually Similar, Complete the look or the most popular Inspired by your Browsing History and many more.
All forms of product images can be tagged with automated product tagging. This is an amazing feature that makes it possible for all your videos and ad banners become instantly shoppable by displaying exact matches of products that buyers see on the screen. If they are not satisfied with what they are looking for, the AI will suggest similar looking ones if something goes out of stock.
Tagging is something that is not very apparent, yet is indispensable. An accurately tagged catalog is helpful along the retail value chain. Store owners enjoy decreasing costs owing to automated workflows. It also helps in product discovery by helping shoppers to index their products better. This leads to more accurate searches on the site. Other benefits include a significant reduction in bounces on search result pages. Users also enjoy hyper-personalisation relevant to their needs.