What‘s the Deal with Returns Predictions?
Woman looking through crystal ball. Photo credit: Jonathan Sebastiao on Unsplash
Predictive AI in the spotlight
Not all people want their palms read, or their tea leaves analyzed and most of us do not seek out crystal balls. But maybe there are some futures that are worth delving into it. Predictivity has come to returns management. It doesn’t work with magic, the occult or telepathy, it’s straight up computer science, specifically Predictive AI.
Real benefits for all brands
There are real and tangible reasons to encourage this technology. Specifically, when harnessed correctly, it can be used to improve efficiency, reduce costs, and optimize customer satisfaction. What retailer doesn’t want a healthy slice of those benefits?
Where predictive AI is used across the returns journey
Automated and smart returns decision-making
The return is made up of and influenced by various factors and attributes such as product condition, cost, return reason, purchase, and return history of the consumer, etc. By using these attributes, predictive AI can make smart decisions around what is returned and what isn’t. For instance, it can analyze the cost of returns, including shipping, restocking, and processing expenses, to identify areas where cost reductions can be made and to determine which routes are optimal for the specific return.
Automating and prediction returns decision-making reduces fraudulent returns and also reduces redundancy in reverse logistics shipping. In fact, Predictive AI can optimize the entire reverse logistics process, including transportation, sorting, and repackaging. It can determine the most cost-effective and environmentally friendly methods for handling and processing returns and determining their next life.
Demand forecasting and inventory management
One of the bigger challenges facing retailers is maintaining balanced and properly utilized stock levels. Overstock depletes the margins and understock leads to customer dissatisfaction and churn. This precarious tight-rope can be predicted, maintained and balanced using Predictive AI. By forecasting future return volumes using different attributes such as product, location, or time frame, businesses can plan inventory and stock levels more effectively, reducing overstock and understock situations, as well help to determine optimal inventory management strategies, including decisions about restocking returned items or redistributing them to different locations.
Dynamic Return Routing and tracking
AI can help to determine the optimal route for returned items, whether for restocking, refurbishing, recycling, disposal or even donation. It can take into consideration factors like product condition, destination, and available processing facilities and partners. Moreover, since time is always a factor in keeping costs down and profits up, this can all be done in real-time, and not just when the return has already travelled back to the warehouse. Predictive AI can provide real-time tracking and visibility into the return process, helping customers and businesses monitor the status of returns, recalls, replacements and upgrades.
Smart consumer interaction
In the retail industry, customer satisfaction is key. When intelligent consumer interaction prevails, a smart returns management system, can use Predictive AI to analyze return data and gain insights into customer behavior and preferences. This information can be used to improve product quality, customer service, marketing strategies, as well as to provide unique workflows for each customer, as well as differentiated return policies. Predictive AI can segment customers based on their return behavior, allowing businesses to tailor their return policies and services to different customer groups. Predictive models can be used to identify patterns of fraudulent return activity, such as wardrobing (buying and returning items after use) or counterfeit product returns. This helps prevent financial losses.
Quality control and warranty and service predictions
Many retailers have identified issues around fit and quality as key contributors to the consumer decision to return. By harnessing AI-powered image recognition and machine learning, both fit and condition can be assessed. This can help identify and prioritize items for refurbishment, repair, donation or disposal, and also provide insights into what may or may not need to be adapted in either the product manufacturing or the product representation in the catalogue. Similarly, AI can predict when a customer is likely to experience issues: such as service updates needed or warranty expiry, etc. A sure way to achieve customer satisfaction is to ensure timely service, replacement or upgrade of goods and services. AI prediction proactively puts you ahead.
The cherry on top
Not convinced yet? What if I told you that AI can do this without the retailer needing to take an active role in planning, configuring and deploying the system. Isn’t that the ultimate cherry on top. With return management systems like OtailO, the heavy lifting, planification and execution is done by the system. This means that retailers can turn their focus to other high priority activities that need their immediate and dedicated attention. When used with purpose and specificity, AI works for you and for the optimization of your setup. Try it. I predict you will like it.