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Transforming Customer Support with Data-Driven Insights: A Case Study of an Online Restaurant Reservation Platform

About the company

A leading global online restaurant reservation platform, revolutionizing how people discover and book dining experiences worldwide. With a vast network of partner restaurants and a growing customer base, the company strives to provide exceptional customer support while ensuring the quality and efficiency of its services.

Challenges

The company faced critical pain points in understanding customer sentiment and lacked visibility into the driving factors behind their interactions. Their primary concerns included inadequate data on product feedback, agent performance metrics, and a lack of an efficient Quality Assurance (QA) coverage model. The challenges they encountered include:

Limited visibility into the voice of the customer

The product team had limited visibility into the factors driving customer issues, concerns, and trends making it challenging to align product development with customer needs. This was the most worrying especially since the company has two distinct customer groups – the restaurants who are using it to manage reservations and the patrons who are using it to browse restaurants and make reservations.

Inefficient manual processes that are draining resources and impacting AHT

Their team’s average handling time (AHT) was lengthened by manual tasks such as call summarization and interaction dispositioning, which also impacted operational efficiency.

Difficulty tracking agent performance

The company lacked a statistically relevant and organizationally balanced QA coverage that could accurately and efficiently assess and track agent progress to improve performance and ensure consistent service quality in customer interactions.

Lack of insights into common issues and their root causes

They did not have sufficient or accurate data on the product, agent performance, and interaction trends to identify the root causes (whether the issues are process, product, or service-driven) of customer and agent issues to make informed decisions to improve the product, agent performance, and customer experience.

Level AI Solution

Comprehensive Data and Voice of the Customer (VoC) Insights

Level AI’s analytics module automatically analyzes 100% of customer interaction data across all channels. Backed by Level AI’s proprietary Generative AI technology that understands natural language, the system identifies and categorizes customer intents, concerns, and trends to produce robust, out-of-the-box analytics and reporting for QA, agent performance, coaching, and voice of the customer (VoC). The ongoing automated analysis provides the company with actionable insights around customer concerns and trends, as well as clear issue attributions. This continues to enable them to understand the root causes and make informed decisions for business growth, product improvement, and customer experience.

QA and Workflow Automation 

Established an automated QA assessment workflow to increase coverage, enhance training efficiency, gain deeper insights into agent performance, and improve operational efficiency and reporting capabilities.

Workflow Automation

Automated manual tasks such as call summarization, interaction dispositioning, and categorization to reduce AHT and streamline operations.

Holistic Executive Alignment

Provided accurate and easily shareable analytics and reports to ensure alignment at the executive level to support and prioritize contact center initiatives.

Results

Accurate Analysis of Customer Issues and Drivers 

The AI-driven ability to automatically categorize customer concerns provided the company with a clear understanding of the causes of issues while eliminating the tedious, manual effort that usually accompanied VoC insights. With the VoC solution, they had a full analysis of customer trends and complaints based on 100% of their conversation data. This provided them with clarity on:

  • Common customer issues for restaurants and patrons, ranking from most to least prevalent.
  • The underlying causes and drivers of these customer issues that they were lacking before. For instance, anecdotally, as a product whose core offering is to allow users to make reservations, the company knew that issues with reservations were at the top of the list. However, they could not pinpoint whether these were product issues, user errors, or something else entirely. To solve for this, Level AI automatically analyzed all conversation data and populated the Voice of the Customer analysis to provide insights into the exact reasons behind reservation issues that customers were experiencing. 

They had complete clarity into the top reasons behind reservation issues, which included issues with table capacity, trouble accessing their accounts, and double bookings. They were also able to dive deeper into the top questions that customers were contacting support about that encompassed details for both user questions and product issues, such as:

  • How to change the cut-off time or change the hours of operation
  • How to modify the reservation time
  • The customer was charged for a canceled reservation

For the first time, the company can reliably and effortlessly drill down to the exact causes of customer issues to problem-solve efficiently and effectively.

  • The platforms on which customers are experiencing these issues include the online system for restaurants to manage their reservations, the company’s app, and the website.

Improved QA Coverage

With a comprehensive and AI-driven evaluation process, the company saw a significant improvement in QA coverage to ensure consistent service quality and adherence to standards,  leading to better agent training and performance insights.

Enhanced Agent Workflow Automation and Operational Efficiency

Reduced after-call work and 18% lower AHT, improved productivity, streamlined agent workflows, and boosted operational efficiency with Level AI’s ability to automate manual tasks, including case summarization, categorization, and interaction dispositioning. 

Executive Alignment

Level AI’s comprehensive reporting and easy sharing capabilities provide their executives with a clear picture of customer satisfaction and operational performance, fostering alignment across the organization.

Why Level AI 

QA Automation

Level AI’s robust and advanced AI-powered QA automation capabilities provide superior insights into agent performance, crucial for effective coaching and training.

Agent Workflow Automation

Level AI goes beyond basic case management to intelligently automate tasks like call summarization and categorization, along with the ability to push this information through to integrated platforms, to significantly improve agent and operational efficiency. 

Analytics and Insights

Level AI’s suite of out-of-the-box AI-driven analytics tools automatically analyzes all conversation data to provide instant, granular, and comprehensive reports and insights into agent performance, coaching, and voice of the customer (VoC) with customer trends and sentiments. 

Executive Alignment

Level AI is strongly committed to ensuring customer success. From the get-go, the Level AI team worked closely with their team to align with their executive and organizational vision for data-driven decision-making and improving customer support operations. Our unique hands-on onboarding, implementation, and post-implementation approach and support continuously drive the success of the partnership with Level AI.

Through a combination of cutting-edge technology and a commitment to understanding the client’s unique needs, this leading global online restaurant reservation platform successfully transformed its customer support operations by partnering with Level AI, leveraging our advanced automation, analytics, and QA solutions. They addressed the challenges that brought them to Level AI and are continuing to demonstrate the power of data-driven insights and automation in improving operational efficiency and customer satisfaction in the hospitality industry.