Reduced Support Ticket Volume by 58% with Autonomous AI Service Agent

We helped a retail brand shift its customer service processes with the adoption of an autonomous AI support agent based on Salesforce Agentforce. Massive amounts of automated order tracking and return queries were automated, allowing intelligent, scalable, and contextual customer support delivery.
Customer
Online Retailer
Country / Region
United Kingdom
Industry
Retail & eCommerce
Banner - Reduced Support Ticket Volume by 58% with Autonomous AI Serv

Highlights

Autonomous Case Resolution Engine
Contextual Order Data Access
Intelligent Escalation Framework
Unified Commerce Integration
Client Requirements

Autonomous Order & Return Query Solution

The support agent was to be an autonomous AI-powered system capable of answering various repetitive queries that involved order tracking, delivery, refunds, and eligibility to make returns using real-time CRM and commerce information.

Unbroken CRM and Order Data Integration

There was a need to have a single system where the history of orders and the position of the shipment, as well as details of the case, could be accessed contextually by the AI agent to be able to respond with accuracy and personalization.

Smart Escalation for Complex Cases

Client needed a defined process of escalation that would automatically hand over complex or exception-based cases to human agents and preserve the entire conversation and context.

Challenges

Large number of Repeat Queries

There was redundancy in order tracking, shipping status, and return-related inquiries of customer service teams, and this caused the customer service teams to have less time when dealing with high-value customers.

Poor Response During Peak Seasons

Ticket volumes increased drastically during promotion campaigns and during holidays, resulting in delays in response time and deteriorated customer experience indicators.

Low Real-Time Order Visibility

The support agents had to manually alternate between the CRM and commerce system to access the details of orders, which added to the average handling time and operational inefficiencies.

Ineffectual Escalation Management

Escalations did not have continuity. In the instances where cases were handed over to human agents, there was no proper synchronization of conversation history and order data.

After Challenge - Reduced Support Ticket Volume by 58% with Autonomous AI Serv
After Challenge - Reduced Support Ticket Volume by 58% with Autonomous AI Serv
Solutions

Implementation of Autonomous Support Agent

The Salesforce Agentforce was used to implement an autonomous AI-powered service agent, in which conversational logic and workflow automation were set. Automatic identification and resolution of routine order tracking and the cases of returns were performed automatically.

Unified Service and Commerce Data Framework

The configuration of integration between Salesforce Service Cloud and Salesforce Commerce Cloud allows accessing the contextual information about the history of orders, status of shipping, and refund eligibility. The data synchronization was designed in such a way that the accuracy of the response would be in real time.

Smart Escalation Logic Setup

The agent structure was the home of smart escalation rules, where exception triggers and sentiment detection were introduced. Complex cases were automatically diverted to human agents, and the entire context of interaction was maintained.

Automated Case Lifecycle Management

Workflow configuration in the Salesforce ecosystem automated case categorization, status updates, and resolution tagging. Reporting dashboards were designed in such a way that they tracked the rates of resolution, ticket deflection, and service performance.

Are you ready to change customer support using autonomous AI agents? Allow our Agentforce experts to develop scalable and smart service automation to match your retail operations.
Technical Architecture
Key Features
Technical Stack
COMPANY

An online fashion and lifestyle product retail company based in the UK and operating as an omnichannel retail brand, with thousands of daily online orders and increasing customer care requests on the digital channels.

The AI service agent helped save repetitive tickets by a significant amount and enhanced customer satisfaction. The support teams are now concerned with more complicated matters, whereas straightforward cases are solved immediately and in an appropriate manner.

Conclusion

The successful implementation of an autonomous AI service agent was carried out in this engagement to address these bottlenecks in its operations. Through the Salesforce Agentforce, an intelligent workflow was set up to solve routine cases automatically. Overall, a smooth connection between Salesforce Service Cloud and Salesforce Commerce Cloud was in place to serve as the foundation of the contextual data access and the correct response generation.

This had the effect of cutting down support ticket volume by 58 percent, average handling time by far, and customer satisfaction scores by a significant margin. The solution provided a scalable, AI-based service model that could sustain all long-term retail development and operational excellence.

Benefits
  • On-demand replies made to frequent customer questions.
  • Major reduction of ticket deflection by autonomous automation.
  • Real-time commerce information provides precise and personal responses.
  • There were centralized dashboards that boosted operational decision-making.

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