Office supplies retailer discovers key issues in contact center data
Company Size
1,000+
Region
- America
Country
- United States
Product
- Luminoso
Tech Stack
- Text Analytics
- Natural Language Processing (NLP)
- Data Aggregation
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Natural Language Processing (NLP)
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Retail
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
- Root Cause Analysis & Diagnosis
Services
- Data Science Services
- System Integration
About The Customer
The customer is a North American office supplies retailer that operates multiple contact centers and receives constant customer feedback through its website and call centers. The retailer deals with a high volume of customer interactions, including text from online chats and call transcripts. Despite having a large amount of feedback data, the retailer struggled to extract actionable insights to improve customer support processes. The company needed a solution to analyze and understand the feedback to identify and address critical issues that were affecting customer satisfaction and operational efficiency.
The Challenge
Despite receiving constant customer feedback from its website and call centers, this North American office supplies retailer was blind to its lessons. The insights its Contact Center Team could use to identify, address, and reduce issues that chased away customers were buried within its more than 2.5 million documents. The feedback included text from online chats and call transcripts across multiple contact centers and agents. With thousands of messages and calls each day, in-house solutions could not scale or process enough data in real time – let alone aggregate across channels and sources. An effective solution would: Analyze constant, high-volume, aggregated streams of feedback, Help identify and understand prevalent, critical issues, Uncover insights to improve customer support processes.
The Solution
With Luminoso, the team analyzed its aggregated data, surfacing trends, unknown issues, and root causes. Initially focused on prevalent concepts, the analysis yielded unexpected insights. For example, a site migration bug prevented logins to the retailer’s rewards site. The team had assumed this was due to forgotten usernames or passwords. And from chat transcripts, the team learned that issues with modifying or canceling online orders had led to an influx of calls. The Contact Center Team coordinated with the Website Team to fix the migration issue and add the ability to change or cancel orders. While tracking these fixes to ensure resolution, the team continued to monitor data to capture issue recurrence – and identify new problems.
Operational Impact
Quantitative Benefit
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