1. Search, Advertising, and Discovery

Strong customer experience starts with highly personalized recommendations, targeted product offers, and search relevance. There are three main use cases for personalized recommendations with AI:

Search relevance and item discovery: 49% of online purchasers to find what they're looking for, browse past the first page. To enhance the shopping experience for clients and assist them in finding the ideal goods, search and item discovery are essential elements. Natural language processing (NLP) is a technique used by AI-powered search engines to interpret and process queries. After that, the search engine presents the highest-ranked search results based on the meaning. eCommerce teams may uncover the most relevant results for a consumer and gain a deeper understanding of the true intent behind a search term with the help of AI-powered search relevancy.

Advertise and make recommendations: Retailers can show relevant ads and offers based on past search, browse, add to cart, and buy activity. Retailers may gather consumer information, combine insights, and provide a customized buying experience with machine learning. Machine learning recommender systems make use of a recommender function that forecasts the user's rating for a particular product based on user data, such as browsing and purchase history. Improved improved data can help marketers who want to reach consumers with offers and advertisements. Re-engaging clients who may have abandoned their cart is made easier with the use of targeted advertising.

Product suggestions: These are essential to increasing return on investment for commerce teams trying to increase product sales. To provide tailored product suggestions, ML models examine past purchases and create lookalike client populations. ML models, for instance, can suggest products that are similar to one another, that are commonly purchased together, or that are purchased by audiences that resemble them. Retailers benefit from product recommendations because they increase average order value and encourage repeat purchases.

2. Demand Forecasting and Inventory Management

AI applications for supply chain management and logistics can dramatically accelerate processes in the global supply chain. There are three main use cases for supply chain management with AI:

Demand Forecasting: Demand volatility is one of the biggest issues facing supply chain managers. Demand forecasting driven by AI makes use of machine learning algorithms to anticipate and identify shifts in consumer demand. To find links in massive datasets, machine learning algorithms leverage any connected data, such as product attributes and classifications, as well as historical time series data, such price and promotions. In order to minimize inventory loss, this enables eCommerce teams to identify demand trends and project future demand fluctuations.

Inventory management: Accurate AI-enabled demand forecasting has significant downstream impact on inventory management. Improved forecasting can lead up to 65% reduction in lost sales due to inventory that is out of stock. In addition to creating more accurate inventory, AI can help streamline aspects of warehouse management using Internet of Things (IoT) devices. With IoT, retailers can optimize warehouse operations and shipping processes with real-time inventory control.

Dynamic Pricing: Retailers can implement dynamic pricing to boost profit by using better inventory management and demand forecasts. Teams can switch from manual, static, traditional pricing to dynamic pricing, which updates in real time. In order to create a model based on the input parameters, AI algorithms use past sales and pricing data, market demand, outside events, and rival pricing. Among the many advantages of dynamic pricing are improved market segmentation, cost savings, and ROI maximization.

3. Chatbots and Customer Service

In order to maintain consumer engagement and boost sentiment, customer service is becoming more and more crucial. It can be difficult, nevertheless, to respond to a large amount of client requests across several channels. Live agents can sometimes be more expensive and slow to respond. Chatbots with AI capabilities play a critical role in assisting in the resolution of various customer care issues. AI-driven chatbots are virtual assistants that aid in answering consumer questions by utilizing conversational AI and natural language processing. There are four key ways Chatbots can support customer service:

• Engage and respond to customer inquiries: Chatbots can provide guidance for product related questions and answer frequently asked questions about sizing, product variants, or discounts.
• Boost sales processes: Chatbots can help provide product recommendations and reduce cart abandonment by reminding customers of products they may have left in their cart.
• Offer post-sale support: Chatbots can provide order tracking, returns and exchange processing, and collect customer feedback.

4. Content Understanding

As media consumer technology progress, content types are always changing and expanding. Large volumes of user-generated content, such as seller and merchant information and customer reviews, can be found on eCommerce websites. eCommerce teams require powerful content comprehension systems in order to keep up with the volume of material being created, offer the best recommendations, and maintain user engagement on your platform. Three main use cases are involved in developing a robust content comprehension system for eCommerce websites

Data enrichment: The foundation of a robust recommendation system is the addition of content identification and categorization to the content metadata. Teams can leverage granular data for targeted personalization, enhanced content ranking, and the detection of unclassified content by enriching content metadata. In our previous guide, we explained how data labeling is the process of assigning context to data so machine learning algorithms can achieve the desired result. As much of user-generated content is unstructured, data enrichment is useful for content teams to build richer personalization and recommendation systems.

Content intelligence: Demand forecasting and consumer behavior analysis depend heavily on a thorough grasp of new trends and content dissemination. Trend detection is a crucial use of content intelligence. Teams can swiftly identify microtrends as they emerge on a daily basis by using machine learning to process and label films. Then, using multi-modal inputs, human-in-the-loop techniques are employed to identify trend signals. Teams in charge of eCommerce may now more effectively analyze and respond to trends in order to identify areas where the company can expand.

Trust and safety: Malicious actors and offensive content are spreading more widely over communities and platforms. Scalable detection is essential for safeguarding your brand and clientele. Use powerful AI algorithms to automatically identify dangerous or harmful user-generated material. eCommerce teams can reduce manual moderation by utilizing AI models with human-level precision that improve with the scale of content.

5. Enriched Product Data

Superior product catalog data is the foundation of eCommerce data. Precise product catalog information comprises specific characteristics including product descriptions, color, material, size, brand, and taxonomy that are shown on the product detail page (PDP). eCommerce businesses can invest in three primary use cases for catalog data

Catalog production: When eCommerce teams are creating new shopping experiences on social media and other channels, catalog production is a wonderful place to start. Teams can use creation to compile, enhance, and update product information from seller feeds and the open web. A machine learning infrastructure can supply all items that are available together with their corresponding attributes by ingesting brands, sellers, or websites. Applications include social commerce, in which a social networking platform's native shopping feature is present. This enables new shopping opportunities for customers on existing digital web applications.

Attribute enrichment: To improve product taxonomy, rank items based on relevancy, and get more detailed search results, add attribute data to your current products. Using machine learning models that rely on named entity identification and picture classification approaches, attributes are extracted from both text and images. It's critical to improve the underlying data in product catalogs since erroneous information might result in misleading product suggestions, subpar search results, or improper product category taxonomy. Product teams aiming to enhance search and relevance must prioritize attribute enrichment, as search and recommendation algorithms rely heavily on precise product attributes.

Detailed product data such as descriptions, attributes, variants, and interactive media have a compound effect on the revenue generated for eCommerce companies.

Product matching and duplication: AI-accelerated human annotation can assist in the elimination of product variants, the merging of product duplicates, the correction of inaccuracies on product information pages, and the removal of inconsistencies in order to enable item authority. When given details about two different products, a matching endpoint determines whether or not they match and provides the associated model confidence score. In order to give clients more accurate results, product duplicates can be eliminated from the catalog with the use of product matching.

eCommerce teams can improve engagement, discoverability, and conversion on product websites with accurate and rich product data.

6. AI-Generated Product Imagery

In our previous guide, we explained how diffusion models have the power to generate any image you can imagine. This has a multitude of applications for marketers and brand managers to generate new product imagery for ad creatives, campaigns, and social media. Research has shown that conversion rates double with the number of images of a product.

The quantity and caliber of product photography that advertisers and merchants may use to give customers an engaging shopping experience is currently limited. This load is increased by the cost of photoshoots, the size of product catalogs, and the variety of audience tastes.

Teams can utilize generative AI to help tackle this problem by producing a large number of high-fidelity product photos in various settings while preserving retail product brands.