How data analytics and predictive modelling enhance returns efficiency in the footwear industry

The ecommerce footwear segment across the globe is expected to reach $398.40 billion, growing at a CAGR of 3.4%, according to a report on Statista. As ecommerce continues to thrive, so does the need for innovative solutions to streamline returns and improve the customer journey. Predictive analytics and data modelling have emerged as the secret weapons for footwear brands to harness the true power of their customers' data. 

Consider a scenario in the bustling world of ecommerce where a customer purchases a pair of shoes online and eagerly anticipates their arrival. The package arrives promptly, and excitement fills the air, only to be replaced by disappointment as the shoes don’t quite fit as expected. 

In this case, not only is the customer experience hampered, but it also affects the business’s profitability. These hassles could have been easily avoided if the business had used the customer data points to predict their sizes. 

In this article, we will explore how predictive analytics and data modelling allow businesses to enhance the efficiency of the ecommerce returns process. 

As it is a fairly new technology, there are doubts about implementing predictive modelling. If you are an ecommerce footwear brand, here are a few ways to use predictive analytics and data modelling. 

Right sizing and fit

According to a study conducted by McKinsey and Company, the apparel sector has a return rate of 25%, as opposed to the 20% return rate in the ecommerce sector. A customer might need different sizes in a sports shoe as they have to be a snug fit, but a sneaker needs a little room around the foot to be comfortable. 

With data modelling, a footwear brand can understand these product deviations. For example, Myntra allows you to create various buyer profiles. When you purchase under these buyer profiles, the platform can extract sizing data with the help of predictive data algorithms. Hence, the returns due to wrong sizes are reduced significantly. 

Fraud detection

Returns fraud is a common plague for the entire apparel section. Issues such as wardrobing and fake items in the return parcels continue to impact ecommerce businesses from this sector. However, returns are a crucial part of the online shopping experience. 

Using data modelling, you can detect customer behaviour from fraudulent cases. Additionally, the insights from predictive analytics will allow you to determine the frequently targeted items. You can implement additional delivery security for these items, like doorstep quality checks. 

Returns management

Most companies optimise delivery routes to enable faster and more efficient deliveries. Similarly, the reverse logistics process has to be optimised to enable the best possible ecommerce returns experience. You can use real-time traffic data as well as location data to choose the best pickup routes. 

Predictive analytics can also be used to detect and address various reasons for product returns. For example, if a product is frequently returned due to sizing complaints, you might want to update the sizing chart. It is also key in enabling better quality control and reducing the cost of each return. 

Customer lifetime value 

Data modelling aids in calculating customer lifetime value. By understanding the long-term experience, brands can tailor their returns policies and incentives according to customer requirements to improve retention. According to Harvard Business Review, increasing customer retention rates by just 5% can increase profits by 25-95%.

This customer retention technique is also key in rewarding loyal customers and increasing your customer retention rate. The average customer lifetime value will also allow you to determine the right financial budget for your marketing strategies. In this way, you can maintain profitability. 

Data modelling stands as the linchpin of success in the realm of reverse logistics optimisation. Its multifaceted contributions–ranging from accurate demand forecasting and efficient route planning to sustainable decision-making and customer-centric strategies–redefine how companies manage the complexities of product returns. 

As the landscape of commerce continues to evolve, those adept at harnessing the power of data modelling will not only navigate the challenges of reverse logistics with finesse but will also seize opportunities to enhance sustainability and customer satisfaction, thus setting the stage for long-term success in the ever-dynamic world of supply chain management.

The author is Co-founder, Tech & Product, ClickPost, an ecommerce shipping and courier integration platform.