Automated Return Processes

𝗔𝗜 𝘄𝗶𝗹𝗹 𝗻𝗼𝘄 𝗵𝗮𝗻𝗱𝗹𝗲 𝘆𝗼𝘂𝗿 𝗿𝗲𝗳𝘂𝗻𝗱 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗮𝘁 𝗔𝗶𝗿 𝗜𝗻𝗱𝗶𝗮 Air India is deploying Salesforce's Agentforce, autonomous AI agents that will handle their entire refund process from start to finish. I was skeptical at first. I mean this is Air India we're talking about - not exactly synonymous with cutting-edge customer service. Then I looked closer at what they're actually doing: → They identified their most painful customer friction point (refunds). → They're automating the ENTIRE process end-to-end. → They're starting small, proving the concept, then expanding to voice. Here's why this matters for every business leader: Most companies use AI to make bad processes slightly faster. Air India is using it to fundamentally reinvent the customer experience. What's fascinating is how they're approaching this - not with massive, risky transformation, but with a targeted strike at their biggest pain point. For Air India, this could be the difference between surviving and thriving in an ultra-competitive market. For the rest of us, it's a masterclass in pragmatic AI transformation.

Last year I had a call with the VP of ecommerce of a $300M+ retail company who was convinced their 32% return rate was "just the cost of doing business" When I dug into their data I discovered that almost half of post-purchase revenue loss is preventable. This happens all the time, retailers are pouring their heart and budget into hitting sales targets, only to watch a third of that revenue disappear due to inefficiencies and refunds. It's demoralizing to be a retailer these days. It doesn't have to be this way! Here's the playbook we used to help that company recover over $6.8M in just 4 months: Most retailers focus on the wrong metrics, for example they celebrate $10M in sales while silently losing $3.2M to returns, and another $1M to operational inefficiency, plus $800K to return fraud and abuse. Quick observations: Your "best customers" are killing you! 37% of "VIP shoppers" are serial returners, they look great in your CRM but they're negative margin customers. We found one customer returning over $14K → this is totally preventable! This is our framework that we developed after working with hundreds of enterprise retailers in the past 5 years: Prevent returns Enable size/style swaps and allow for uneven exchanges (more expensive or cheaper options) Store credit options instead of refund Relevant product recommendations for exchange and upsell Analyze the return reasons by product - this can save you a lot of products from being returned! Results: Over 60% reduction in refunds b) Prevent fraud and abuse Fraud rules to prevent return abuse Automate policy enforcement and verification of product quality before the product is sent back Product inspection workflows at the warehouse level Results: the highest we seen last year for a customer was over 90% c) Streamline Operations Setup rules for returns routing to the closest warehouse or outlet stores Minimize clicks and enable a scan, scan, refund workflow Centralize all returns data and actions into one system, to prevent system switching Results: 42% faster processing Returns are not a cost of doing business. They're a goldmine of hidden opportunities. But here's the truth: Most retailers will read this and do nothing. They'll keep losing millions because "that's just ecommerce." The smart ones will see this as the competitive advantage it is. What side do you want to be on? P.S. If you're a retail executive seeing 20%+ return rates, DM me. I'll share our full framework as it’s way more detailed.

Is Your Self-Service Only Solving Simple Issues? It’s Time to Tackle the Complex Every business wants customers to self-solve, troubleshoot, and enjoy a seamless self-service experience. But here’s the reality: most AI implementations are only handling the easy stuff—call routing, password resets, answering FAQs. There’s no real value in just solving the simple. The true game-changer is enabling your self-service to tackle complex issues like product troubleshooting and returns. So, how do you get there? Phase one: Build the right foundation and guardrails 1. Workflows: How to get it done You need dynamic workflows that act as guardrails, guiding the AI to provide the right context at the right time. It’s like giving them a personalized roadmap to navigate intricate issues. 2. Integrations: Where to get It done Your AI needs to tap into various systems. Integrations allow it to access context from your CRM, inventory systems, or order management platforms. Without this, it’s like navigating without a map. 3. Actions: What data to use Specify what data your AI should use within these systems. For example, pulling up a customer’s purchase history to assist with a return or accessing product specs for troubleshooting. Phase two: Level up with full AI process automation 4. LLM Instructions: What outcomes to accomplish Provide clear instructions to your AI’s language model. Specify the outcomes you want it to achieve, like resolving a technical issue or processing a refund. 5. Triggers: When to get it done Set up triggers that determine when the AI should step in. This could be when a customer clicks on a troubleshooting link or requests a return. — At Zingtree, we’ve seen how this two-phase approach empowers businesses to move beyond the simple and tackle the complex. Is your self-service ready to tackle complex issues? What hurdles are you facing in making AI work for the tough problems? 💬 #CustomerService #AI #Automation

Most brands don't have a good returns process. The right technology makes it seamless. I have been in countless warehouses where the returned items are received at the furthest dock. Then put in the back of the building...in a pile...sorted through and processed over several days. Even to my surprise we did a project in the past where the customer said, “I don’t want the returns near the front of the building. It’s dirty and a mess.” (that statement started my mission to improve returns within the warehouse) Now with a combination of robotics and the right software, returns can be handled efficiently. It can become an asset to the fulfillment operation and the brand. 💰I did a study for an apparel brand which identified they could reduce inventory on hand by 10% resulting in a $5MM savings. They averaged 48 hours for processing returns. A shelf to person system like the one shown in the video incorporates all parts of the fulfillment operation. Inbound items are processed then put directly into the pickable location. The item is not touched again until an order is placed for it. The right software can work wonders. If a returned package enters the building that is currently out of stock or low on stock, it is given a high priority and routed by system directly to processing. A worker validates and processes the return then puts it into the pickable location. This puts the product back up on the storefront quickly for re-ordering. In stock items mean more sales for the brand and happier customers. Build a fulfillment system that works for every part of the operation, not just a happy path. Leave a comment or repost if you found this useful! ♻️ Q: Have you ever scrambled to find a product in a pile of returns?

𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 𝟰.𝟬: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗲𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 In the past, reverse logistics was often viewed as a costly necessity—managing product returns, waste disposal, and recycling with limited efficiency. But Industry 4.0 is changing the game. The primary aim of reverse logistics 4.0 is to help maximize the recovery of the remaining value from end-of-life (EOL) products and appropriately dispose of the non recyclables. By integrating IoT, AI, cloud computing, and blockchain, organizations are turning reverse logistics into a strategic enabler—reducing waste, cutting costs, and driving sustainability while unlocking new revenue streams. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀? • 𝗙𝗿𝗼𝗺 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗼 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲: AI and big data help forecast return volumes, optimize collection, and improve decision-making. • 𝗙𝗿𝗼𝗺 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱: IoT-powered tracking and blockchain create end-to-end visibility in reverse supply chains. • 𝗙𝗿𝗼𝗺 𝗰𝗼𝘀𝘁 𝗰𝗲𝗻𝘁𝗲𝗿 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲 𝗱𝗿𝗶𝘃𝗲𝗿: Smart remanufacturing and resale extend product lifecycles, reducing waste and boosting profitability. 𝗧𝗵𝗲 𝗦𝗺𝗮𝗿𝘁 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗰𝘂𝘀𝗲𝘀 𝗼𝗻 𝗳𝗶𝘃𝗲 𝗸𝗲𝘆 𝗮𝗿𝗲𝗮𝘀: 1. 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 – IoT-enabled bins, cloud-based tracking, and AI-powered demand sensing. 2. 𝗦𝗺𝗮𝗿𝘁 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Automated AI-driven sorting, real-time inventory tracking, and dynamic waste dashboards. 3. 𝗦𝗺𝗮𝗿𝘁 𝗥𝗲𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 & 𝗥𝗲𝗰𝘆𝗰𝗹𝗶𝗻𝗴 – Digital twins, predictive analytics, and AR-assisted maintenance. 4. 𝗦𝗺𝗮𝗿𝘁 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 – Fleet optimization, autonomous vehicles, and blockchain for transparency. 5. 𝗦𝗺𝗮𝗿𝘁 𝗗𝗶𝘀𝗽𝗼𝘀𝗮𝗹 – AI-driven landfill management, cloud-based leachate monitoring, and sustainable disposal solutions. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 𝟰.𝟬 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻: 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 – AI-driven return prediction & automated restocking. 𝗔𝘂𝘁𝗼𝗺𝗼𝘁𝗶𝘃𝗲 – Sustainable vehicle end-of-life disposal & part remanufacturing. 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 – Smart WEEE recycling & resource recovery. 𝗣𝗵𝗮𝗿𝗺𝗮𝗰𝗲𝘂𝘁𝗶𝗰𝗮𝗹𝘀 – Real-time monitoring of expired/recalled drugs. 𝗥𝗲𝘁𝗮𝗶𝗹 – AI-powered seamless return experiences. The Future of Reverse Logistics is Smart, Data-Driven, and Sustainable Reverse Logistics 4.0 isn’t just about managing returns—it’s about creating new value, driving sustainability, and gaining a competitive edge. Ref: https://lnkd.in/df4NtCj2