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Elevating Customer Support Efficiency in E-commerce with Level AI

In the dynamic landscape of e-commerce, delivering seamless customer support efficiency is a perpetual challenge. This case study explores how an e-commerce powerhouse specializing in performance apparel that focuses on creating comfortable and versatile activewear for activities like fitness, yoga, running, and outdoor pursuits partnered with Level AI to improve its contact center operations.

Navigating Pain Points:

The e-commerce enterprise grappled with substantial spikes in call volume, occasionally necessitating phone line shutdowns to manage the surge effectively. These spikes were exacerbated by a recent transition to a new ERP, Dynamics 365, bringing forth a cascade of changes in the backend infrastructure.

Faced with high agent turnover, lengthy onboarding, and minimal coaching, the contact center team struggled to deliver exceptional customer service. Determined to turn things around, the company set out to improve agent performance and customer satisfaction through three key initiatives: analyzing customer interactions systematically, establishing clear performance metrics for agents, and developing personalized coaching programs targeting specific areas for improvement. However, supervisors soon found themselves buried under mountains of daily tasks, leaving minimal time to craft effective performance scorecards and coaching plans. Even for the few conversations they managed to analyze, identifying key topics for targeted improvement proved challenging.

To make matters worse, a fractured view of customer data across various platforms –-CCaaS, CSAT, AI Analytics & CRM–buried the voice of the customer, which left supervisors struggling to make improvements.

Objectives:

The company sought a solution to automate and enhance QA processes, create agent performance scorecards, and derive valuable insights from customer interactions. The ultimate objective was to boost agent efficiency by speeding up response times, identifying productivity bottlenecks, and, therefore, providing a better customer experience.

Additionally, the company was looking for a solution that could provide detailed insights into customer sentiments, including empathy, language usage, and other key sentiments. These insights will not only assist supervisors in identifying coaching opportunities but also hold the potential to significantly enhance agent performance.

Level AI-Driven Growth: 

Level AI became a crucial ally for this e-commerce company by identifying and tagging important conversations for analysis. This includes instances where agents received low scores and situations where customer satisfaction is at risk. In addition, the tool offers valuable context to conversations, scores every interaction (including calls, emails, and chats), and provides automation in manual tasks like auto-summaries and auto-dispositions to boost their customer support efforts.

By harnessing the power of Level AI’s Agent Assist, the agents can easily address difficult customer queries without having to memorize the company knowledge base, thereby substantially reducing agent onboarding and training time.

In addition, the e-commerce company leverages Level AI’s out-of-the-box dashboards to gain comprehensive insights into customer satisfaction. In Level AI, they introduced a conversation tag that flags instances when a customer encounters a technical issue. Consequently, they have now set up a dashboard to compare Net Promoter Scores (NPS) specifically when these “technical issues” tags are activated. This allows managers to pinpoint how platform problems directly affect customer satisfaction, hence facilitating platform updates, ultimately leading to improved customer experiences.

The company expresses genuine appreciation for the depth of insights provided by Level AI, which goes beyond simple analytics. The software’s ability to categorize and understand the voice of the customer stands out, empowering the team with valuable information for informed decision-making and proactive customer support strategies.

Conclusion:

This case study exemplifies the transformative potential of Level AI in reshaping customer support operations for an e-commerce giant. From automating QA processes and generating detailed scorecards to unlocking valuable insights and providing real-time assistance, Level AI emerges as a strategic partner in the company’s journey toward operational excellence. The collaboration holds the promise of not just resolving current challenges but also setting the stage for a more efficient, insightful, and customer-centric approach to customer support.

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