Post-Holiday Retail Reinvented: How AI Streamlines Returns, Logistics, and Customer Service
With the holiday season behind us, retailers have refocused their efforts on addressing the flood of service requests that typically follow.
You can count on a surge of returns and exchanges to process, as well as items that arrived damaged or defective. Purchasers and gift recipients will have questions about policies, receipt requirements, and return logistics. When it comes to handling returns and exchanges, you’ll need to inspect items, re-stock inventory, monitor fraudulent activity, and refund customers in a timely manner, which can be difficult when you’re dealing with a high volume of transactions.
After you handle re-stocking, you may experience issues with excess inventory or stockouts that can force a delicate balancing act when planning for the next season. Furthermore, overtaxed reverse logistics protocols can cause shipping delays and bottlenecks in the supply chain that negatively impact the customer experience.
Subsequently, many retailers are turning to artificial intelligence-enabled tools to improve efficiencies and service quality during the post-holiday period. Using these tools, you can streamline workflows, implement prioritization protocols, and automate repetitive tasks.
Streamline Returns and Inventory Management
High volumes of returns and exchanges can overwhelm inventory systems, warehouse staff, and customer service teams. Although everyone involved knows fast return and exchange processing is directly tied to customer satisfaction, they’re struggling to manage the complexities, such as unpredictable inventory fluctuations and fraudulent abuse of return policies.
Applying AI and machine learning (ML), retailers can automate the entire returns cycle, such as generating labels, updating inventory, and processing refunds. The technology also handles validating return requests, updating stock levels, and sending confirmation emails.
AI can automate the categorization of returns (e.g., re-sellable, defective, or damaged) and adjust inventory accordingly; ML algorithms can flag suspicious patterns (e.g., frequent high-value returns or returns without receipts) to prevent fraud.
Prioritize High-Value Customers
When it comes to the customer experience, your ability to identify, prioritize and resolve pressing inquiries can prevent dissatisfaction and costly attrition.
AI uses natural language processing (NLP) to assess the tone and urgency in customer communications (e.g., social media, chat, and emails). The technology can identify emotions such as anger and frustration, as well as urgency indicators such as “ASAP” and “immediately.”
Issues with a negative sentiment are flagged and prioritized for quicker resolution, and some systems deliver proactive triggers for immediate responses when warranted, such as an automated apology or confirmation that a request has been escalated.
Additionally, AI can crosstab this data with customer categories based on specified factors, such as loyalty status, purchase history and issue complexity — customers that score high on these blended indicators receive elevated service levels.
Automate Repetitive Tasks
Retailers need workable solutions that improve efficiencies during the demanding post-holiday period. Many discover AI-enabled tools that automate high-volume, repetitive tasks can unburden teams to focus on high-value objectives while speeding up routine processes.
For example, AI-driven supply chain and inventory management systems can update inventory levels for returned or exchanged items while they’re being processed, and identify items eligible for re-stocking, routing them to the appropriate store or warehouse. The technology can also automate reordering for high-demand items to prevent stockouts. For example, AI can flag high-demand electronic gadgets as ready for resale and prioritize the restocking within hours of receipt.
When it comes to reverse logistics, AI systems tailored to logistics optimization and workforce automation can streamline the transport of returned items to stores and warehouses and automatically assign warehouse staff to sorting and re-stocking items. When coordinating logistics, AI solutions that specialize in real-time route optimization, network analysis, and cost minimization can determine the fastest path for transporting returned items to the nearest distribution center, reducing delays in the returns process and minimizing operational costs.
Overall, AI is revolutionizing post-holiday retail operations, streamlining processes, prioritizing critical tasks, and automating routine workloads. The technology is enabling retailers to improve efficiencies, lower costs, and improve customer satisfaction.
Meagan White is chief marketing officer of Kibo, a composable commerce platform.
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. Previously, she led the North America marketing and sales development team at MoEngage. She has more than a decade of marketing leadership experience within the Martech, customer experience, and content management sectors, managing marketing strategies in various functions, including demand generation, digital marketing, product marketing, and communications. She holds a master’s degree from Boston University.
