Leveraging Return Analytics: Turning Data into Actionable Insights for Your Business 

Returns are a cost center for businesses. But when you dig deeper, returns actually hold a lot of valuable insights into customer expectations from your business and the areas of improvement you need to focus on. 

By diving into return analytics, eCommerce businesses can go beyond optimizing reverse logistics. They can improve their understanding of customer preferences, common product/ service challenges, and changing trends. 

In this post, we’re going to explore the key metrics and data points of return analytics that can help businesses derive actionable insights for growth in competitive markets. 

What are the key metrics to track in return analytics for business insights  

To get a clear view of the returns process and its impact on the business, here are some key metrics to track: 

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• Return rate - The percentage of total orders that are sent for returns. A high return rate highlights issues with product quality, description, or the assistance offered for shopping. 
• Average return window - The average time it takes for customers to initiate a return after receiving the product. When the return window is too short, it indicates low product quality and when too high, it may highlight an issue with the returns process. 
• Cost per return - The total cost associated with each return including shipping, processing, and restocking. This metric gives a clear image of the financial impact of returns on the business. 
• First-time vs repeat return rate - Track how many customers are repeating returners in comparison to those returning an item for the first time. A high repeat return rate indicates an issue with the product quality or not being able to meet customer expectations. 
• Time to process return - The time it takes for a return to be fully processed from the moment of initiation to refund. Faster processing times contribute to positive customer experiences. 
• Return rate by product type - Measure of which products typically see higher return rates. This signals potential quality or functionality issues with the same, and can be critical for better catalog and inventory management. 
• Customer satisfaction score - This score captures customer sentiment and satisfaction with the return process, and is a key indicator for loyalty. 
• Average time to approve - The average time it takes for your team to approve a return request. 
• Average time to receive the item back at the warehouse - The time it takes for you to mark return requests as received. 
• Average time to refund - The average time it takes for you to process a refund on a return made.  
• Average time to send replacement - The time it takes you to send a replacement for the product returned by the customer. 
• ROI per return - The profitability of processing product returns. This is calculated by comparing the revenue generated/ recovered from returned items to the costs associated with handling the requests. [Free calculator]

• Revenue per return - Average revenue recovered from each returned product, calculated by dividing the total revenue from returned items by the number of returns. 

What are the key data points to analyze in return analytics for business insights  

Apart from return metrics, businesses also need to evaluate the data that comes through from these requests. Some of them are: 

• Reasons for returns - Take note of the common reasons customers file for returns. Usually, this includes sizing issues, product defects, unmet expectations, product damage, or simply a change of mind. This can help you improve product descriptions, and quality control and set better inventory forecasting.
• High-return products - Identify the different categories of products or items that tend to see a higher return rate. This helps your business pinpoint problem areas such as misleading product pictures or descriptions, faulty materials, FAQs, or the shopping assistance offered around them. 

• Customer preferences - Gather what customers requesting returns tend to prefer - refunds, exchanges or store credits. This can help create a better return policy and customer engagement campaigns. For example, offering exchanges secures your sale and store credits can guarantee the customer comes back for another purchase once a return is processed. 

• Return timeframe - Track how long customers wait before initiating returns. A short return timeframe may indicate buyer’s remorse or product dissatisfaction with the first impression; a longer return timeframe could imply confusion or delayed deliveries. 
• Return delays - Identify common causes for return delays. These could include unclear return instructions, lack of easy access to return shipping labels, or return mode options. This can help you streamline the process further for ease of customers and efficiency for the business. 
• Customer sentiment - Collect feedback on the return experience to gather insights into customer emotions and satisfaction. If customers feel frustrated, the returns process needs improvement in order to prevent negative reviews and boost customer loyalty. 
• Return volume by sales channel - If you sell across multiple touchpoints, take note of the channels that see the most returns. This could help highlight issues with audience targeting, product quality, or the ongoing campaign optimization of the website, app, marketplaces, social shops, and other platforms. 
• Return rate by customer segment - Break down returns by customer segments based on data such as location, purchase frequency, average order size, and demographics. This can help highlight unique behavioral patterns and improve campaign targeting to attract the right shoppers or improve the status quo of products. 

How does a returns management platform simplify analytics 

While most businesses are often tracking their return rates, consolidating data across channels can be tough - especially when you’re selling in global markets or across multiple digital platforms. This is where a returns management platform helps: 

• Automate data collection - Returns management platforms can automatically gather return data across channels. This simplifies tracking and brings more accuracy to the return analytics report to get a unified view of the process. It also helps cut down on manual data entry and potential errors. 
• Customizable reports - A good solution offers customizable return analytics dashboards to track key metrics in real time. This enables businesses to monitor trends such as return reasons and return volume by category/ channel in real time to detect issues early and make proactive adjustments. 
• Predictive analytics - Advanced platforms integrate with your tech stack and leverage AI to offer predictive insights. These are insights based on historical return data to forecast trends, helping the business with better inventory and marketing management. Return prime allows brands to collect relevant information during the return process to improve the business strategy with data backed insights.‍
• Automate feedback collection - Return management platforms either offer automations or integrations with feedback collection apps. This helps you gather customer feedback during and after return requests are processed through key channels like email or SMS. 

• Customer sentiment analysis - Tools that are powered by AI go one step further to help you analyze customer sentiment accurately. Using NLP, they enable businesses to understand conversations through parameters like tone of voice or the type of words used. 
• Fraud detection - Some returns platforms come with fraud detection capabilities. This helps analyze return patterns and highlight suspicious behavior. The systems can be designed to alert potential fraud, helping reduce unnecessary losses. 
• Inventory and restocking optimization - A returns system integrates with inventory management systems to streamline restocking processes. Access to real-time data and updates helps minimize revenue loss due to understocking or over-stocking. 
• Personalized customer experiences - Returns management platforms can also help tailor customer experiences. They could help set up workflows to offer refunds, store credits, or exchanges based on customer requests, conversations, and past interaction data. 

• Multi-channel integration - A seamless integration with different marketing and sales channels can help businesses ensure consistency in return policies and processes. This creates a positive customer experience, irrespective of the touchpoint they choose. 

Conclusion 

Return analytics are an untapped domain for most businesses. 

As brands obsess over expanding marketing and sales channels, customer acquisition costs, and the like, return analytics help you identify opportunities for business improvement and growth - all within the existing systems and processes. 

With the right return analytics platform, businesses can uncover data that helps them improve their products/ services, marketing offers, and customer experiences at scale to grow in competitive markets. 

Looking for a solution that helps processing returns while improving your GMV and customer experiences? Get started with Return Prime today. 

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