AI use cases in e-commerce are set to continue expanding.
Increasing numbers of businesses are harnessing the technology, as it can help them automate processes, offer personalised customer experiences (CX), and make data-driven decisions. AI has the potential to assist e-commerce businesses to recommend products to customers based on data to help them understand and identify new purchasing behaviours or trends.
In the world today, customers have higher expectations than ever before in line with generative AI (Gen AI), prompting organisations to be more proactive. With this in mind, AI Magazine considers some of the leading use cases for AI in e-commerce.
10. Personalised product recommendations
Organisations can utilise personalised product recommendations to understand customer behaviour and preferences. They can do this by using data analysis and machine learning techniques, implementing recommendation algorithms, and personalising user interfaces, among other things.
With shoppers eager to use AI applications, personalised recommendations are becoming more frequent. Retailers can now use customer reviews and ratings and incorporate social media data into AI to improve overall experiences.
9. Virtual shopping assistants
Virtual assistants are increasingly impacting the way that customers purchase, providing creative opportunities for e-commerce retailers. This type of technology is on the rise, as virtual shopping assistants harness AI to create human-like conversations with customers and help answer any queries.
Unlike chatbots, AI assistants can use natural language processing (NLP) to adapt to what the user is saying and provide tailored recommendations. As a result, they can help to improve customer engagement, save company time and money, and create a more seamless online shopping experience.
8. Dynamic pricing
Dynamic pricing refers to the practice of varying the price for a product or service to reflect changing market conditions. Companies can leverage AI in this way to drive greater data-led decision-making and market research, in addition to optimising their profit margins and attracting new customers within their target market.
AI algorithms can analyse market demand, competition and customer behaviour to set prices, improving customer satisfaction.
7. Fraud detection
Companies are looking to increasingly harness the power of AI to tackle fraud, during a time of misinformation. The technology can proactively identify possible fraud by analysing large volumes of data and interpreting user behaviour.
AI is able to analyse transaction size, frequency and purchase history to identify fraudulent risks. More broadly, AI can enable companies to spot early indicators of fraud, in addition to speeding up the development and testing of new detection models.
6. Smarter search (visual search, NLP, CV)
AI-powered search engines can help to deliver more accurate search results by understanding the context behind a user’s search query. For instance, the technology can help to provide personalised search results for a user based on their real-time preferences, thereby providing more intelligent insights for businesses.
WIthin e-commerce platforms, AI-powered search is able to understand natural language queries, leading users to products that are more relevant to them. Businesses are then able to use this data to improve customer satisfaction.
5. Upselling
AI can be harnessed to enhance upselling strategies in ecommerce by leveraging machine learning algorithms and data analysis. AI tools can analyse customer data like purchase history, browsing behaviour and preferences to create detailed customer profiles and segments.
Based on these insights, AI-powered recommendation engines can offer highly personalised and relevant upsell suggestions in real time during the purchasing process – increasing the likelihood of successful upsells and boosting average order value.
4. Inventory management
By leveraging AI, ecommerce companies can streamline their operations and enhance customer satisfaction through efficient inventory management practices.
Machine learning algorithms can analyse historical sales data, customer behaviour patterns and market trends to accurately forecast demand. This enables optimised inventory levels, reducing overstocking and stockouts. AI can automate reordering processes, ensuring timely replenishment based on real-time data. Additionally, AI-powered predictive analytics can identify slow-moving items, allowing businesses to strategise clearance sales or discontinue unprofitable products.
3. Improved demand forecasting
By analysing historical sales data, customer behaviour and market trends, AI can generate more accurate predictions – providing ecommerce retailers with a competitive advantage.
This data-driven approach allows for highly accurate predictions of future demand, minimising the risks of overstocking or stockouts. Additionally, AI can continuously learn and adapt to changing market dynamics, ensuring that forecasts remain relevant and reliable. With improved demand forecasting powered by AI, companies can make informed decisions, reduce costs associated with excess inventory and provide a superior customer experience through better product availability.
2. Reducing manual labour
AI can help to automate manual tasks in e-commerce workflows to improve efficiency and boost productivity. Likewise, businesses can rely less on human labour and streamline operations as a result.
Despite public anxieties about AI replacing human workers, responsible companies are at a great advantage when they harness AI to support - rather than supplement - workers. In doing this, organisations are able to reduce downtime and make their operations run smoothly.
Likewise, implementing automation can also work to support improved decision-making.
1. Chatbots
AI chatbots are fast-becoming an increasingly important tool in e-commerce, as they can help businesses interact with customers in a broad range of different ways. For instance, they can help to offer a 24/7 customer service, which leads to greater customer support and reduces burden on human teams.
Likewise, chatbots can help customers navigate product selections and answer any questions about a specific product or service. They can even make personalised recommendations, tailoring the customer service to the individual.
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