What Does Amazon Do With Returns? (Quick Answer)

Understanding the Hidden Journey of an Amazon Return

When that Amazon package shows up at your doorstep, it marks the final step in a long logistical journey. But for returned items, the journey is far from over when you drop them at the post office or courier pickup. Behind the scenes, Amazon has built extensive systems to cost-effectively handle millions of returns each year across its global fulfillment infrastructure.

Let‘s analyze what happens when customers return items to Amazon’s ubiquitous ecommerce engine, and why returns matter so much from technological, environmental, and customer loyalty lenses.

A Record-Setting Volume of Returns

The scale of Amazon’s returns operation dwarfs that of traditional retailers. In 2020 alone, the company reported $61 billion in refunds and product returns globally. With over 1.9 billion items shipped worldwide from Amazon fulfillment centers yearly, even single-digit differences in return rates add millions back into the system.

Costs are climbing too – in 2020, returns accounted for nearly 20% of total landed costs across all Amazon shipments. Yet the company continues refining processes to handle ever-increasing order and return quantities. Investing in efficient, value-generating return systems provides strategic advantages over retailers struggling with rising expenses from 100% online return rates. Amazon even relies on extensive analytics around return data to innovate shipping durability improvements and AI-powered quality checks before items get sent out.

The Critical Journey of an Amazon Return

Once the returned item makes its way from the customer back to Amazon facilities, an intricate process kicks off behind the scenes to determine the next steps.

Upon arrival, unique barcode IDs get scanned to pull up the order details in Amazon’s inventory systems. Packages head to dedicated return processing stations where workers first manually verify product counts match the original order. Physical condition assessments check for damage, defects, missing accessories or other issues.

Amazon leverages intelligent automation techniques like AI-enabled visual inspections and sensors across key points in returns handling facilities. If systems detect defects or other problems for investigation, automatic alerts get triggered to route items to secondaryteams.

After technicians verify return eligibility and condition, software algorithms analyze key data points to recommend the best downstream route for each item:

Return to inventory for resale
Refurbish/repair then resale
Liquidate to reseller
Donate
Recycle residual value
Dispose in landfill as final option

Two Critical Return Streams – Resale Inventory or Liquidation

Amazon prioritizes extracting additional value from quality returns whenever possible by reselling through Amazon Warehouse Deals or adding to regular inventory after repackaging. Software checks for recent price history and sales velocity to project resale viability, and combines this data with input cost margins and current category demand trends. If contribution profit forecasts meet targets, the algorithm flags the unit for resale; otherwise, it gets liquidated in bulk.

Liquidation selling returned items in bulk batches allows Amazon to quickly free up warehouse capacity and recover partial value. Reselling pallets of Amazon returns through B2B channels gives liquidators opportunity to profit while saving Amazon storage and processing overhead.

Whether adding returns back into fulfillment selection algorithms or selling to liquidators, Amazon‘s automated return systems maximize speed, efficiency and cost-effectiveness. The company handles over 10 million returns items each month in the US alone – manual approaches cannot scale, while AI and sensors check quality far faster than humans.

Why Returns Data Matters
<Insert data visualization – maybe expanding decision tree from earlier>

The data gathered from customer returns provides Amazon critical insights around improving core operations, supply chain issues, product defects and even fraud trends. By linking detailed return diagnostics results to order data, product specs, customer details and other signals, machine learning algorithms can uncover significant patterns to drive innovations.

A few examples:

High return rates for a particular item signal potential quality improvements needed from suppliers
Connections between product damage at certain facilities with packing practices highlight areas to optimize protection
Return details indicating missing items or accessories not on original receipt may flag theft and fraud risks for investigation

In addition to driving operational changes, granular data from return trends allows Amazon to give proactive recommendations to shoppers as well. Customers receiving relevant suggestions for complementary products with lower return rates reported much higher satisfaction scores in trials.

Sustainability Starts with Returns

With growing public scrutiny around Amazon‘s environmental impacts, the company spotlights sustainability efforts around returns and reverse logistics. Unsellable returns get recycled wherever facilities exist or donated through programs like Amazon Textiles. Amazon also partners with circular reuse providers to give used merchandise new life across various secondary markets globally.

And improving first-time quality through ML fuelled insights means fewer wasted resources re-transporting fixes.

Still, recycling remains highly limited currently, with most returns sitting in landfills. Amazon is ramping up existing programs while innovating creative solutions to the challenge of extreme volumes with typically low residual value. Expect increasing transparency and measurable targets over the coming years.

The Battle Against Return Fraud

While Amazon maintains generous return timeframes and policies to drive loyalty, these same policies open themselves to potential abuse. Estimates suggest fraudulent returns drain over $18 billion from US retailers annually. Telltale signs like manipulated receipts, patterns of returning high-value goods, or suspicious changes over time indicate likely fraud.

Luckily, the same return data troves that improve other Amazon processes facilitate fraud prevention too. Correlating sensors detecting opened packages with customer return behavior or linking gift card redemptions of liquidated items let automated safeguard filters consistently tighten. While Amazon will likely never eliminate return scams completely with ease of online anonymity, behind the scenes algorithms relentlessly monitor for anomalies to catch exploiters.

Returns Power Ecommerce Innovation
Even as automation around processing returns will only increase, human oversight remains essential at key inspection and decision gates. The total volumes flowing through Amazon‘s reverse supply chain easily overwhelm manual approaches, but technology cannot replicate subjective assessments of damage or quality. Instead, Amazon harnesses AI/ML to optimize human capabilities to rapidly validate and route the millions of returns flooding back daily.

In the world of ecommerce, product returns are simply inevitable – and rather than view them as exclusively adding costs, Amazon builds capabilities to capture value. Detailed return data flows into creating recommendation engines, improving demand forecasting and shaping supplier negotiations. The prime example of tech-driven returns innovation in action, Amazon reimagines returns as another channel to understand customers, develop sustainable processes and empower data-led decision making.

So next time you return that impulse buy to Amazon, know it‘s soon off to power analytical engines and reuse programs underpinning innovations in responsiveness, quality and circularity. Even for returned items, the journey‘s far from over.