As evident from our recent articles, incorporating AI technology in ecommerce businesses can yield numerous advantages. AI can streamline operations, offer personalised services, and ultimately increase sales.
Therefore, it is no longer optional but essential for retailers to embrace AI adoption in order to sustain growth and stand out in the market. Ecommerce businesses must leverage AI to devise innovative customer engagement strategies, optimise online checkout processes, and implement cost-effective digital commerce practices.
To gain a deeper insight into utilising AI in ecommerce, this guide offers an overview of the key AI applications in the ecommerce landscape.
AI for Ecommerce Use Cases
There are many advantages that businesses can gain by using AI technology, and for a detailed understanding, you can refer to our article on the subject.
The focus of this article is on the practical applications of AI in ecommerce. To provide better clarity on the utilisation of AI in this field, we have highlighted some use cases.
1. More Targeted Recommendations
A strong customer experience is based on personalised recommendations, targeted product offers, and relevant search results. In order to achieve this, there are three main use cases for AI-powered personalised recommendations:
Search and item discovery are crucial components in enhancing the customer shopping experience by helping them find the right product. AI-powered search engines use natural language processing (NLP) to understand the query and present the most relevant search results.
By utilising AI-powered search relevance, ecommerce teams can gain a better understanding of the customer’s intention behind a search term and display the most relevant results.
Retailers can deliver targeted advertising and offers based on a customer’s search history, browsing behaviour, and purchase history through machine learning.
By using a recommender function, which takes into account the customer’s browsing and purchase history, machine learning recommender systems can predict the rating a user will assign to a given product.
Better customer data can aid brands in delivering advertising and offers that are highly targeted and can aid in customer acquisition and cart abandonment.
Product recommendations play a crucial role in boosting product sales and improving ROI. Machine learning models analyse purchasing history and build lookalike customer audiences to provide personalised product recommendations.
Some common techniques include similar products, cross-sell and upsell. Product recommendations add value to retailers by encouraging repeat purchases and increasing the average order value.
2. Predictive Inventory Management
The integration of AI technology benefits the supply chain management and logistics and significantly boosts the efficiency of global supply chain processes. There are two primary use cases for AI in this field:
Forecasting demand accurately is a major challenge in supply chain management due to demand volatility. AI-powered demand forecasting leverages machine learning algorithms to predict and identify changes in consumer demand.
By using historical data, such as pricing and promotions, and associated data like product features and categories, ML algorithms can identify relationships in large datasets. This enables eCommerce teams to recognize demand patterns and forecast future demand fluctuations, ultimately reducing inventory loss, which is essential in inventory management.
By leveraging on demand forecasting, online businesses can implement dynamic pricing strategies to increase profit. Dynamic pricing allows teams to move away from traditional, static pricing models to those that change in real-time.
Similarly, AI algorithms use historical sales and price data, market demand, external events, and competitor pricing to generate a model based on the input parameters.
3. Creating Product Images at Scale
Product photography is one challenge most business owners face. One of few key reasons is the costs associated with photoshoots and the scale of product catalogues.
To tackle this problem, business owners can utilise generative AI technology that can quickly produce high-quality product images in a variety of settings while still adhering to brand standards.
4. Detailed Product Description
Apart from product images, accurate and detailed product catalogue data is also essential for successful ecommerce. This includes comprehensive attributes displayed on the product detail page, such as product descriptions, size, colour, brand, material, and product taxonomy.
There are two primary use cases that ecommerce companies can focus on to improve their catalogue data:
Catalogue creation is an excellent starting point for ecommerce businesses that are building new shopping experiences on platforms like social media. By collecting, enhancing, and refreshing product data from various sources such as seller feeds and the public internet, machine learning infrastructure can offer all available products with their associated attributes.
This is especially useful for social commerce, where shopping is natively built into social media platforms, thus enabling new shopping opportunities for customers on existing digital web applications.
Ecommerce companies can improve the customer experience by incorporating high-quality product images and interactive media such as 360-degree views, videos, and augmented reality (AR) features.
Providing detailed product information through descriptions and visuals is highly valued by customers, as it helps them make informed purchasing decisions. This, in turn, can increase sales and reduce returns and refunds.
5. Improve Customer Service
Delivering exceptional customer service is critical to keep customers happy and foster positive brand sentiment. However, managing a high volume of customer requests across multiple channels can be a daunting task.
Employing live agents can also be an expensive and time-consuming process. To address these challenges, AI-powered chatbots have become an indispensable tool for customer support. Chatbots are virtual assistants that use NLP and conversational AI to provide prompt responses to customer inquiries.
Here are three ways chatbots can help enhance customer service:
Chatbots can provide 24/7 support, offer personalised experiences, and free up human agents to handle more complex issues.
Chatbots can assist customers in tracking their orders, processing returns and exchanges, and gathering feedback.
Chatbots can recommend products and remind customers of the items left in their cart, thus reducing cart abandonment.
This guide has outlined the main applications and use cases of AI in eCommerce. With the retail and eCommerce sector constantly evolving, it’s essential to embrace innovation to meet customer demands.
If you’re interested in learning more about AI in ecommerce, check out our blog. We have more articles on this topic that can help you gain a better understanding of this field and the tools available to achieve your goals.