The NexGen UseCase on


A retail business is experiencing challenges with its customer returns process. While the industry standard return rate is 14.5%, this business is facing a slightly higher return rate of around 16%, leading to increased operational costs and impacting customer satisfaction. The main issues stem from inefficient processing due to disjointed CRM and OMS systems and a lack of advanced analytics and AI-driven insights for managing return patterns.

Identified Issues:

  • Return Rate Above Industry Standard: The business experiences a return rate of 16%, which is higher than the industry standard, leading to increased logistical complexities and operational costs.
  • System Disintegration: Current systems, including CRM and OMS, operate in silos, lacking effective communication and data sharing, which exacerbates the challenge in managing returns.

Impact and Loss:

  • Operational Cost Increase: The higher return rate results in a 10% increase in operational costs due to additional handling, processing, and logistics.
  • Inefficiencies in Processing: The disjointed systems contribute to an average delay of 48 hours in return processing, which is above the industry norm, leading to a 25% decrease in customer satisfaction.
  • Increased Customer Service Inquiries: Due to inefficient processing and lack of real-time visibility, there is a 15% increase in customer service inquiries, burdening the customer support system and further reducing efficiency.

Resolution Plan

System Integration using MuleSoft

Integrate CRM and OMS using MuleSoft to create a seamless flow of information. This integration aims to simplify the returns process, making it more transparent and efficient.

Enhancing CRM for Returns Management

Upgrade the CRM to handle return-specific tasks more effectively. This includes tracking return reasons, customer interactions, and processing status.

Implementing AI for Return Pattern Analysis

Deploy AI algorithms to analyse return trends and patterns. The goal is to predict future return behaviours and identify products with higher return rates for proactive quality improvements.

Data Warehousing with DataCloud and Analytics Integration

Implement DataCloud for unification of data from return data, customer information, and order details. Connect DataCloud with an analytics tool like Tableau for advanced data processing and analysis. This integration will enable the extraction of actionable insights from the data stored in DataCloud, supporting data-driven decisions to improve the return process.

User Journey

User Journey of the UseCase


Customer Gained Outcomes

Operational Efficiency

Implementation: By integrating CRM and OMS through MuleSoft and employing AI for data analysis, the processing of returns becomes more efficient. This streamlined approach reduces manual intervention and accelerates the entire returns process. Outcome: This results in a reduction of return processing costs by approximately 10%, aligning more closely with the industry standard. Additionally, time spent on handling returns is decreased by 15%, enhancing overall operational productivity.

Improved Customer Satisfaction

Implementation: The integrated system provides customers with real-time updates on their returns, improving transparency and communication. This, coupled with a faster processing time, significantly enhances the customer experience. Outcome: As a result, customer satisfaction scores improve by 25%, reflecting a more positive experience with the return process and boosting customer loyalty.

Informed Decision Making

Implementation: Leveraging AI and data analytics to gain insights into return patterns, customer preferences, and product performance. This information is used to make informed decisions about inventory management and product quality improvements.Outcome: These insights lead to a more strategic approach to inventory management and product development, contributing to a decrease in the overall return rate by 8%. This strategic decision-making enhances the efficiency and effectiveness of business operations.

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