Transforming eCommerce Catalog Management through AI adoption
Leveraging computer vision and conversational search coupled with human-in-the-loop solutions offer a blueprint for efficient catalog management.
The current state of Catalog Management
The ability of enterprises to provide immersive eCommerce omnichannel shopping experiences hinges on the quality of catalog curation. This entails creating precise product taxonomies and generating accurate product attributes so that customer searches match products.
Addressing the choice overload effect
At the heart of effective product catalog management is aligning product information with shopper intent, language, and behavior. Inaccurate product catalog management can result in unstructured and inaccurate data getting attached to products. This can then lead to misalignment in demand and supply as maintaining optimal stock is a function of catalog management.
The only solution aimed at addressing the challenges of the choice overload effect and decision fatigue is through curating high-quality product catalogs. AI adoption will ensure consistency and relevance by enabling efficient product catalog management based on categories and attributes extracted from the image, further supported by human-in-the-loop solutions to enable intuitive search.
Catalog Management: fundamental but overlooked
Efficient product catalog management can drive seamless eCommerce omnichannel customer experiences both online and offline by integrating shopping across brick-and-mortar stores and eCommerce platforms. The accuracy of product tagging and standardizing determine how well a customer can identify and distinguish one brand from another based on how well managed the search results are. Catalog management is a sure-fire way of creating brand loyalty by ensuring consistency in product catalogs, thereby setting an enterprise apart from its competitors.
Building brand loyalty through catalog management
Customer intent can be discerned using smart analytics that leverages descriptive tags, which can guide customers to the products they are looking for faster. Catalog data can also be used to identify trend gaps and gain insights into customer behavior.
AI-driven change
AI is now making its presence felt at every step of a customer’s buying journey, from expressing intent to the point of conversion, and eCommerce catalog management is no exception to this trend. According to the JDA PWC Annual Retail CEO survey, retailers are spending 18 cents out of every revenue dollar generated online to meet customer satisfaction, and it is predicted that this number will grow. A cost-effective method of ensuring customer satisfaction is by providing greater value to customers by optimizing catalog management.
The key objectives of AI adoption in product catalog management are to continually optimize product catalogs using demand analysis, speed-up time to market, and harness the power of NLP to generate accurate product descriptions. Advantages of having AI in product tagging can deliver personalized shopping experiences for customers with improved search results and intelligent recommendations.
Generate accurate product descriptions through NLP
Through image feature extraction, AI can create accurate, granular, and standardized product tags. AI-driven product tagging can also extract relevant attributes by processing the pixel content in images leading to a 90% increase in catalog processing time, tagging them accurately and in context. New products can thus be added to the catalog faster and at scale.
AI algorithms can leverage metadata for cataloging, resulting in improved product visibility. AI-powered product tagging can drive SEO rankings of particular products and ensure more direct browsing paths and entry points. Customers can find products through intuitive search that hinge on quality, rich and deep metadata.
Intuitive search to drive product SEO rankings
AI automated product tagging, however, requires high-quality images to function effectively. While product imagery is a key element of automated product tagging, a detailed taxonomy should take into account product tags, metadata, and precise attributes to enable personalization.
Computer Vision in eCommerce
Computer vision plays a crucial role in enabling efficient image categorization, thereby driving precise information extraction. Using computer vision to auto-tag products can segment a product or attribute under multiple nomenclatures, which will allow for the product to be found easily. Each product has unique tagging requirements which need to be context-specific.
Context-specific product tagging
Deep Learning paradigms such as Convolutional Neural Networks (CNN) have led to significant breakthroughs in the labeling efficiency of Computer vision. A CNN mimics the human brain by building a hierarchy of layers where each layer adds one level of abstraction over the previous one. A layer-wise approach allows for classification using higher-level features such as clothing patterns and styles, something that is pivotal to fashion tagging.
Enabling Conversational Search
Increased product exploration across channels is only possible through structured product catalogs. AI adoption opens up new possibilities for conversational search and curating recommendations that drive eCommerce omnichannel retail experiences.
Conversational search, which entails the use of complete sentences and natural-sounding phrases in search queries, hinges on advances in NLP and makes use of chatbots and virtual assistants to provide the perfect guide to customers seeking products. Advances in conversational search are aimed at pivoting chatbots from their traditional role of offering support to actively performing guided sales.
Identifying intent through conversational search
Using NLP and NLU, conversational search automation can be used to understand context and intent. This is the leading reason for enterprises to employ conversational solutions, according to a survey by Opus Research, all of which stress the need for accurate product curation. Smart analytics that leverages descriptive tags can discern customer intent and guide customers to the products they are looking for faster.
Cataloging with Taskmonk
The Taskmonk platform is optimized for comprehensive eCommerce catalog management, which ensures consistent taxonomy and the precise labeling of attributes. Using Taskmonk’s purpose-built drag and drop solutions allows users to customize taxonomies and improve labeling efficiency.
Within cataloging, product tagging is key to performing optimized conversational search. Attribute labels provide deep, specific insights that can aid categorization and search. Catalog management is only as good as the labeling solution used to perform the annotation and the domain knowledge of the human-in-the-loop experts. The effectiveness of Taskmonk’s geographically distributed labeling teams and automated workflows ensure smooth digitization and faster time to market.
Curate catalogs with customized taxonomies
With greater awareness of the role of Catalog Management in the success of eCommerce enterprises and the centrality of platforms like Taskmonk in performing precise and accurate cataloging, the advancements brought to the fore by AI adoption can now provide firms with a clear competitive advantage.
https://www.linkedin.com/pulse/automating-e-commerce-catalogs-how-ai-reshape-fashion-lorenzo-orazi/
https://www.pixyle.ai/guides/the-complete-guide-for-automatic-product-tagging-in-e-commerce
