Webinar Wrap-up: Driving rich customer experience through intelligent catalog management

Let’s get one thing straight – E-commerce product catalog management is hard! Presenting accurate product information on your website or mobile app for hundreds of products in different categories, each with its unique serial number, vendor, seller, pricing, and dimensions is not easy. There is no room for error. One misstep, and you could lose your customer. Taking care of your product catalog and presentation is one of the first steps to driving online sales, be it a regular day or a special sale event for major holidays such as Diwali or Black Friday. Wondered how big E-commerce platforms like Flipkart manage their product catalog and sell millions of products in a day?

To learn more about the data-driven strategies that power our everyday online shopping experiences, we invited Krishnendu Majumdar, Vice President of Catalog at Flipkart – one of India’s biggest E-commerce platforms offering over 80 million products across 80+ categories to our webinar session hosted by our Head of AI labs, Shashank Shekhar. In this article, we have summarized the key points discussed in the webinar on building a winning product catalog for E-commerce companies.

The importance of E-commerce catalog management

The right product information at the right place, at the right time, is critical for a seamless online shopping experience. A well-organized catalog with accurate and relevant product information makes it easy for shoppers to find what they are looking for and make quick buying decisions. In the competitive E-commerce landscape, building a robust catalog is crucial for driving user experience and sales.

Need for artificial intelligence and machine learning in catalog management

Consumer behavior is constantly changing. What’s in style today can become obsolete in a matter of weeks. To keep up with the needs of consumers, E-commerce companies must make sure that their catalog is up-to-date, diverse, and accurate. In addition to understanding customer behavior, online stores also have to plan for scale as more and more new products are added to the website every day. With the increasing numbers of Stock Keeping Units (SKUs) on the platform and quite a few product data being created by the sellers, catalog management is one of the most important areas in E-commerce.

E-commerce companies have to continuously monitor KPIs to grow their catalog and business. It is humanly impossible to track multiple rapidly changing variables and metrics that impact the E-commerce catalog. There is a need to track and analyze live data from multiple sources. On one side you have data from the sellers on the available items or products and on the other side, you also have to monitor the data from customers to know their buying preference. AI-enabled analytics help in bridging this gap between what is available and what customers are looking for.

E-commerce catalog management is more than just managing data

By leveraging machine learning and deep learning, E-commerce companies can detect any unexpected anomalies, and identify new trends in near real-time. These data-driven insights are critical for driving the quality of the product catalog and driving conversions. At the end of the day, the more appealing and relevant the product information is, the more products you sell online. This involves more than just mapping the right product information and images. Companies have to continuously track metrics to understand new shopping behavior and trends.

The variables that impact E-commerce sales are complex and dynamic. Multiple factors including demographics, seasonal festivals, impact online shopping behavior. Simple details in the product description can have a major impact on E-commerce sales. For instance, during the Raksha Bandhan festival in India, there is greater demand for Rakhi (a traditional bracelet). By understanding the customer preference for the product, the common search terms, and their buying pattern, E-commerce companies can make their user-facing catalog information easy to find and comprehend. Products can be promoted based on popularity and holidays.

Why align product description to customer preference?

E-commerce companies have to match what customers are looking for with the products that are available or offered by sellers. Machine learning algorithms help E-commerce companies to get early visibility into trends and styles, making it easier to align product information to the needs of customers. NLP is also used to get more information from product descriptions.

By enriching product catalog with relevant information and images, E-commerce companies can drive more sales. For instance, the popularity of Bollywood movies can drive sales for certain products such as Bahubali Sarees or Kabir Singh Sunglasses. While there is no brand or seller by the name Kabir Singh, adding the term in the product description of the relevant style of sunglasses helps users in discovering the product easily.

A well-organized and relevant product description thus makes it easier for shoppers to find products of interest. It also helps in driving user experience by making it easier for users to navigate through the app and find products that match their requirements. Additionally, having a relevant description gives your products better visibility in search engine results.

Product themes and recommendations

Product recommendations play a major role in driving E-commerce cart value and revenue. Artificial intelligence helps E-commerce companies in identifying new products and removing duplicates from their catalog. Machine learning algorithms also help in automatically categorizing similar products according to themes, making it easier to show relevant product recommendations to shoppers. By suggesting related products, E-commerce companies can drive more sales.

Smart merchandising

In India, the data quality related to retail merchandise (especially product description, etc.) is very low. This is especially the case with seller uploaded products and can prove to be a major barrier for pre-paid orders. Online shoppers also tend to rely heavily on product images to make their buying decisions.

AI in this case helps in giving insights on ways to position products in an appealing way on the E-commerce website. Analytics also helps in identifying any mismatch of descriptions or information that needs correction. Machine learning algorithms can quickly identify erroneous product listings from sellers that would otherwise go unnoticed. E-commerce companies can thus leverage AI to enhance product merchandising, minimize errors, and improve product discovery.

With a wide range of use cases including OCR and NLP, artificial intelligence makes the E-commerce catalog management process more efficient. Companies can leverage AI-driven analytics to reduce the margin of error to build a more wholesome and high-quality catalog. Artificial intelligence and machine learning thus have vast applications in E-commerce catalog management including data enrichment, facilitating compliance, and delivering a great brand experience to both sellers and customers.

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