Reduce Return To Origin: Scale Address Validation


Return to Origin (RTO) is a significant problem for businesses that rely on accurate and timely delivery of goods to their customers.

When a delivery attempt fails due to an incorrect address or the recipient being unavailable, the courier returns the package to the sender or the warehouse, resulting in additional costs, delays, and customer dissatisfaction.


One way to address this problem is to leverage location data and intelligence to verify addresses at scale. Companies can apply machine learning and artificial intelligence techniques to address data. They can create predictive models that accurately identify and correct address errors. These models work even with incomplete or incorrect information. The models automate address validation, reducing manual verification time and cost. Additionally, they enhance delivery speed and accuracy.

Sellers are seeing a huge inflow of orders as e-commerce starts to spread widely over the entire nation. We may anticipate an exponential rise in the number of suppliers and purchasers as internet access expands and makes inroads into tier II and tier III cities of India. In addition to the daily deluge of opportunities and orders that sellers must deal with, they are sometimes tormented with Return to Origin, or RTO, orders. If not maintained in check, RTO orders might reach up to 30% for some e-commerce market players.

Common Reasons for RTO:

  • Incorrect spelling of the recipient’s name or address
  • Missing information, such as a postal code or apartment number
  • Typographical errors in the address
  • Moving without updating the address on record
  • Incomplete or outdated address information
  • Recipient’s address is not recognized by the delivery service
  • Deliberately giving the wrong address as no intent to purchase
  • Customer changed their mind
  • Customer not present to receive the order at the time of delivery

While altering your product portfolio, using accurate descriptions and images, and communicating well with the users can solve some reasons, most of these reasons require scrutiny at the address level.

Wrong addresses can have a significant adverse impact on e-commerce businesses:

  • Increased shipping costs: Returned to Origin (RTO) shipments result in additional shipping costs for the business.
  • Reduced customer satisfaction: Delivery of products to the wrong address or failure to deliver the product can lead to frustrated and dissatisfied customers.
  • Stock Inventory: When a customer enters the wrong address and the package is returned to the e-commerce business, the e-commerce business may overstock the inventory. This can result in excess stock occupying valuable warehouse space and capital. On the other hand, if a customer enters the wrong address and the package is lost or delayed, the e-commerce business may experience a shortage of inventory. This can lead to out-of-stock situations, dissatisfied customers, lost sales, and negative reviews.
  • Lost sales: When a package is returned to the sender, there is a risk of losing the sale as the customer may choose to purchase the product from a competitor.
  • Inefficient operations: The process of managing RTO shipments and resolving delivery issues can be time-consuming and lead to operational inefficiencies.

“How to Reduce RTO In E-commerce?” is one of the issues that has plagued the e-commerce industry for a long time.

If you take proper precautions to minimize these returns, the solution to this question can be easier than you think.

measures to reduce rto
Some practical strategies for reducing returns on your website

GeoIQ’s Approach To Solving Issues Related to RTO at Address Level:

  • Verification of shipping addresses: GeoIQ identifies invalid and incomplete addresses. Increase order delivery rates and enrich your RTO models with address quality scores by breaking down the address into components.
  • Order Profiling: Enrich your user data with 3000+ GeoIQ attributes to predict fraud propensity and other metrics and instantly flags orders that are potentially dangerous and fraudulent and give specific explanations.
  • Granularity: Get RTO insights of the state, city, etc at street level, going beyond pin codes and learning about the factors that influence your RTO losses.
  • Model Customization: To continuously increase accuracy it’s important to identify the key variables that have the greatest impact on the likelihood of returns. These variables could include factors such as product quality, shipping time, and customer satisfaction in accordance with the demands of your business. GeoIQ’s ML algorithms learn, and develop over time to stay updated and effective. As the business evolves and new data becomes available, the model needs to be updated to ensure that it continues to accurately predict the likelihood of returns., and localize the learnings. To provide better customer service It’s important to understand the processes and challenges involved in RTO, such as handling customer returns, managing inventory, and maintaining product quality.

Other Use Cases of Location Data and Intelligence:

Prediction Models: To increase the accuracy of your prediction models, enrich addresses with more than 3000 geographic characteristics. This is true for all models, including:

  • Fraud risk assessment
  • Credit risk assessment
  • Affluence assessment, and more

Evaluate Intent and Prioritize Leads: Set goals for your company and give priority to leads with high address quality scores. The accuracy of the address is another sign of buying intent. Type-by-finger mistakes, blank fields, and other mistakes all reveal malicious intent.

Models for Collections: Determine the contact ability and reachability of addresses to evaluate the likelihood of collections at the time of customer onboarding. Reduced NPAs by at least 10% are the result for lending companies.

Geo-coding: Addresses can be geocoded at scale and in real-time using advanced geocoding techniques. The validation and geocoding of postal addresses can be very advantageous for enterprises, e-commerce companies, and professionals.

Let’s see how:

  1. Accuracy: Geo-coding uses a combination of address data and geographic coordinates to pinpoint a location. By validating addresses at scale, E-commerce businesses can ensure that they have accurate address information for their customers. This can help reduce the number of returns due to incorrect or incomplete address information.
  2. Efficiency: Validating addresses at scale can help E-commerce businesses streamline their RTO processes. By ensuring that address information is accurate, businesses can reduce the time and resources required to handle returns. This can help reduce costs and improve overall operational efficiency.
  3. Customer Satisfaction: Accurate address information can help ensure that customers receive their orders on time and at the correct location. By reducing the number of returns due to incorrect address information, E-commerce businesses can improve customer satisfaction and loyalty.
  4. Fraud Prevention: Validating addresses at scale can also help E-commerce businesses prevent fraudulent activities such as fake addresses or shipping to non-existent locations. Geo-coding can detect if an address is valid or not, and this can help prevent fraudulent activities and reduce losses due to such activities.
  5. Data Insights: By using geo-coding to validate addresses, E-commerce businesses can gain valuable insights into their customer base. This can include information such as regional sales trends, popular shipping locations, and areas with high rates of returns. These insights can help businesses optimize their operations and improve their RTO processes.


In conclusion, by validating addresses at scale, e-commerce businesses can reduce the number of returned packages due to incorrect or incomplete addresses, which in turn reduces RTO costs, piling inventories, and operational inefficiencies caused due to higher percentages of return to origin orders.

Visit GeoIQ to know more about how we are solving some of the biggest problems in retail and e-commerce through location data.

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