Predictive Analytics

How to Boost Customer Loyalty Using Analytics and Actionable Insights

Returns data offers companies priceless insights that improve the customer experience.
How to Boost Customer Loyalty Using Analytics and Actionable Insights
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Guest Writer
Founder and CEO of Return Logic
3 min read
Opinions expressed by Entrepreneur contributors are their own.

As ecommerce grows, so do return rates: About one in three online purchases is returned. For online apparel purchases, the rate is closer to 40 percent. That has triggered the growth of the returns-management software marketplace, offering retailers solutions aimed at making the processing and dispensation of returned goods faster and more efficient.

Related: How to Use Predictive Analytics to Ride the 'Home-Surfing' Wave

That’s important. The more quickly and efficiently a return is processed, the better the outcome: The consumer gets his or her refund faster. The retailer minimizes reverse-logistics costs. And the company offering the product recoups the maximum recovery value.

Retailers should absolutely be taking steps to make returns-processing more efficient for everyone, including the setting up of effective omnichannel returns processes.

But there is a significant lost opportunity just making product returns more efficient, and it’s this: Returns represent an untapped opportunity to build customer relationships with high lifetime value.

It may seem counter-intuitive to look at a product return -- an inherently negative experience that reflects customer disappointment -- with building strong customer relationships. But the opportunity to gain information about that customer and the reason behind the return is a valuable and underused resource.

Retailers that embrace the opportunity to leverage such returns data stand to gain priceless insights about their products and their customers that will help them prevent future returns from happening in the first place.

When retailers collect better data about products being returned and then apply advanced analytics to that data, they learn a lot about their customers and their expectations about products. Advanced analytics has become essential to success in retail. According to Gartner, “Retailers will not be able to compete in the digitalized marketplace without advanced analytic capabilities.”

Related: How to Make Predictive Analytics Work for Your Business

Beneficial scenarios that the use of analytics offers

1. A customer who is asked to comment in his or her own words about a product return sees that the retailer values that opinion and wants to improve. That heightened interest enhances the integrity of the brand.

2. When customers identify issues that can be resolved quickly, such as the replacement of defective batteries in a lot of smartwatches or the addition of dimensions to a product description, the retailer can act fast to prevent subsequent returns.

3. Gaining long-term insights, such as information about a fabric that pills too much, or poor sizing, can help a company shape next season’s product design and merchandising.

4. Segmenting customers and comparing their behaviors to returns data helps retailers identify high-value customers who will spend far more than they return. Retailers can choose to market differently to this group, including offering more liberal returns rules.

Related: HR Analytics: How Should Big Data Be Used in the Workplace?

Most importantly, over time, returns data feeds a cycle of continuous improvement: The retailer’s understanding of its customers and their product preferences drives better buying, product design, merchandising, marketing and logistics processes.

The entire business gets better and better at meeting the customer’s needs and increases the company's ability to delight the customer the first time around. Customer satisfaction goes up, and return rates go down. When retailers analyze and act on returns data, everybody wins.

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