Sara Kim highlights the production and sourcing decisions that manifest into product returns and what retailers can do to avoid them.
Tell me if you have heard these before.
These are typical stories for retailers, but not without a heavy lift. Sourcing departments, designers, and merchants work tirelessly with their vendors to make it happen. Yet despite everyone's best intentions, the trade-offs from these actions can lead to mistakes which lead to returns.
After years of working in product development and production, both for retailers and vendors, I understand retailers' struggles and what vendors do to meet retailers' needs. I've seen, managed, and mitigated many issues and know what can arise. Starting with the ones above:
I mention these points not to suggest they are bad practices or to insinuate that they will invariably result in adverse outcomes. I tell them to emphasize where and why breakdowns happen, which can increase product returns.
What can be done? How do you identify the damaging side effects from fabric, color, sewing, or other quality issues to avoid them in the future?
To gain the necessary insights for better production and sourcing, artificial intelligence (AI) can play a vital role. AI-powered analytics and data processing can identify patterns and trends that are not immediately apparent, enabling proactive measures to be taken.
For instance, AI can analyze year-over-year (YOY) vendor performance to identify any consistent issues or improvements. Retailers can add this information to their vendor scorecard, highlighting areas where additional support or intervention may be needed.
Furthermore, AI can dissect the performance of each category, style, and product produced by each vendor, allowing companies to identify specific areas of concern. By examining the data, businesses can pinpoint product vulnerabilities, such as particular styles, designs, or materials that are more prone to be returned.
AI can verify the reasons for returns attributed to specific vendors. Retailers can identify patterns and common issues associated with particular vendors, enabling targeted actions to address the root causes. Was the product returned due to the fabric or quality of the merchandise? Or was the return entirely out of the control of the vendors' actions?
Returns prevention begins with production, as addressing production-related issues is crucial to minimizing product returns and associated costs. By determining the root causes of these issues and leveraging AI-powered insights, companies can proactively prevent returns, enhance customer satisfaction, and improve their bottom line.