In 2023, the e-commerce industry was largely focused on strengthening brand equity, enhancing the customer experience, and keeping up with the evolution fueled by new platforms and artificial intelligence-based technologies. Notably, the adoption of composable commerce, which refers to building a modular, component-based, cloud-native tech stack, empowered companies to build the agility required to remain competitive.
As we turn the page to 2024, you can expect that composable commerce will establish itself as the industry standard and a key component of market leadership. You should also anticipate that AI will advance every aspect of your operations. In addition to improved efficiencies, you can count on substantial cost savings and transformative insights into supply chain resiliency, inventory management, fulfillment optimization, market dynamics, and customer behavior.
The pace of innovation in 2024 will be unprecedented; to stay on track with crucial e-commerce industry developments, keep your eye on these five trends.
Next-Generation Predictive Analytics
Demand forecasting algorithms will capture and analyze large volumes of data from a diverse set of sources, such as historical sales, seasonality, trends, economic indicators and weather patterns. The output of this analysis will deliver more timely, accurate and actionable results; this insight will help you reduce storage costs and overstock while aligning product availability with customer demand.
Applying predictive analytics, you can anticipate supply chain disruptions by analyzing historical data and identifying correlations and patterns. For example, the analysis might show a pattern where a few suppliers often experience delivery delays during certain weather conditions. When you have this information ahead of time, you can implement contingency plans early on, which could include switching to alternative suppliers, increasing safety stock, or re-routing orders to alternative fulfillment outlets.
Streamlined Fulfillment
AI bridges the gap between offline and online retail interactions by capturing customer touchpoints across channels, such as in-store visits, mobile apps and online interactions, to build a unified customer profile that contains purchase history, preferences, and behavioral patterns. The algorithm ensures that you achieve cross-channel consistency; for example, if a consumer adds an item to their online shopping cart and then decides to shop in a physical store, the app or in-store shopping assistant can identify that shopper’s cart to facilitate in-store pickup or suggest available alternatives.
Omnichannel fulfillment optimization will also rely on AI to automate delivery channel selection in real time. For instance, if a product is available in a nearby physical store, AI can route the order for in-store pickup or same-day delivery to reduce costs and wait times. Or, if a fulfillment center reports a stockout for an item, AI automates the immediate re-routing to an alternative facility.
Pricing algorithms will automate the consistent administration of promotions, discounts and special offers across all channels. Using these tools, you’ll be able to adjust prices based on availability, demand and customer behavior.
AI-Powered Customer Service
Customer support teams will deploy powerful AI-enabled virtual assistants and chatbots that will become more sophisticated and knowledgeable over time. These tools solve your need for providing support around the clock, improving the accuracy of responses and routing customers to human agents when needed. Furthermore, with three-quarters of customers surveyed reporting that they prefer chatbots over human agents to solve their queries, you’ll benefit from resource conservation and happier customers.
AI will also predict customer complaints and issues proactively to help prevent attrition. Algorithms will streamline returns, exchanges and refunds by reducing processing times and providing a friction-free customer experience. When it comes to customer sentiment, you’ll be empowered to respond more thoughtfully to customers’ concerns and moods.
You’ll also be able to analyze your reviews and customer feedback to gather insights into needs, preferences and pain points. Applying AI-driven insights into aspects that customers like or dislike, you can develop or refine products to better align with customer expectations. You’ll also be able to pinpoint areas for customer service improvement by analyzing common customer complaints and support issues.
Full Data Integration
AI will facilitate your native search capabilities. You can use the technology alongside composable commerce and order management solutions or as a standalone; this will make it much easier for your nontechnical users to set up their navigation and fine-tune search results with boost-and-bury biasing rules — these capabilities will draw from your AI processes, including large language modeling (LLM) and natural language processing (NLP).
You can also apply AI to combine search and inventory data to simplify filtering and searching inventory by type and fulfillment. For example, you can infuse search results into inventory data to verify product availability and ensure a seamless shopping experience because you have confidence that the relevant items are in stock.
When it comes to order management, accurate inventory management will minimize excess inventory and stockouts for smoother fulfillment. Over time, AI will play an increasingly important role in directing where and how to move inventory based on both historical and web demand.
Improved Search Results
Leveraging deep learning models, AI-based neural search capabilities will improve your search results. Although traditional methods rely on keyword matching, neural search understands semantics and context; effectively, neural search will translate human language into machine language to deliver richer search experiences.
Because neural search converts inputs into numerical representations (embeddings), your model will be able to interpret relationships and meanings between words across multiple formats, including text, sound, documents and images. Neural search can capture both structured data (e.g., phone numbers, ZIP codes, and dates) as well as unstructured data that’s otherwise difficult to harness (e.g., mobile data, social media files, and PDFs).
Using neural search, your employees will be able to find the precise information they need faster, which frees up time to invest in other high-value projects that are vitally important to high-functioning teams. Because your technology is training itself, your developers will benefit from easier and faster information retrieval, code development and debugging.
The landscape of e-commerce in 2024 is poised for unparalleled innovation. As we witness the integration of next-generation predictive analytics, streamlined fulfillment, AI-powered customer service, full data integration, and improved search capabilities, we encourage you to stay current with these trends to capitalize on the dynamic and evolving nature of the e-commerce industry in the coming year.
Meagan White is the head of marketing at Kibo, a composable commerce platform designed to simplify the complexities of delivering modern customer experiences that span order management, e-commerce, and subscription services.
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