Revolutionizing Customer Support in Real Estate: A Case Study on AI-Driven Transformation
Introduction: The company is a Real Estate investment trust that embarked on a significant expansion, increasing its store count by 50%, the imperative to enhance customer support became evident.
With over 3,000 locations and a steadfast commitment to delivering exceptional customer service, the company faced multifaceted challenges, including transcription inaccuracies, an outdated user interface, and the daunting task of integrating sophisticated technologies. At this pivotal moment, the VP of the Sales Center for this company recognized an opportunity for transformation.This case study will explore how Level AI’s technology revolutionized this company’s approach to customer support, setting a new standard in the industry.Here’s an in-depth look at the challenges that propelled this company towards this innovative journey.Challenges
Transcription Accuracy and Insight Extraction:
The company’s customer interactions were hindered by suboptimal transcription services, with an accuracy below 80%. This shortfall significantly impeded their ability to understand customer needs and extract actionable insights.
Outdated User Interface & Key man-Risk:
The outdated user interface of their previous QA solution posed significant challenges for them. Scott observed that the interface was not intuitive, making data tracking and operational effectiveness cumbersome. This outdated UI created barriers for team leads and agents, hindering their ability to efficiently manage QA operations.
Additionally, the complexity of the interface required technical expertise to navigate, leading to a key-man risk in recruiting and retaining skilled staff. The reliance on individuals with specialized knowledge further exacerbated the challenge of maintaining operational continuity and efficiency.
Rising Operational Costs:
The company’s operational expenditures rose as handling times were approximately 8 to 9 minutes. The per-hour approach of their previous QA solution contributed to increased costs, necessitating the need for cost-effective solutions without sacrificing service quality.
This search for the optimum QA tool prompted them to investigate novel solutions that could strike the best balance between price and performance."
Legacy Keyword-Based QA System
Their previous QA solution relied on keyword detection, making it challenging to categorize and analyze client inquiries. This keyword-based approach lacked contextual awareness and the ability to discern underlying client intent, falling short of true AI-driven capabilities.
Over time, maintaining the accuracy of keyword lists became increasingly burdensome, requiring frequent updates and manual intervention. This hindered operational efficiency and highlighted the need for a more streamlined and advanced solution.
Costly Feature Enhancements:
With their previous vendor, upgrades and new features were often treated as separate, costly add-ons. The additional charges for essential enhancements, such as more accurate transcription services, were particularly irksome, as noted by Scott Hansen, which accelerated their need to find a new partner
Post-Merger Challenge: Understanding New Customer Segments
After merging with another company, the company needed to quickly adjust and learn about its new, larger customer base. The main challenge was finding better tools to figure out what these new customers wanted and needed, to help increase sales and the value of each order.
The Solution
Project OutcomesHere are a few outcomes from their Collaboration with Level AI:Cost Savings: Adopting Level AI substantially reduced their costs due to its competitive pricing, avoiding the need for expensive upgrades or additional features, and technical staff to maintain the system.
Operational Efficiency: Level AI introduced an intuitive user interface (UI) with advanced intent recognition and Generative AI capabilities streamlined the QA process and category management, significantly reducing manual effort and increasing productivity.
Enhanced Agent - Customer Insights: The implementation of advanced sentiment analysis and automated categorization provided deep insights into customer sentiment and behavior, enabling data-driven strategies to improve customer satisfaction for their clients.
Improved Service Quality: High transcription accuracy and simplified calibration processes led to more consistent and accurate evaluations of agent performance, directly contributing to an uplift in service quality.
Seamless Integration and Customization: Easy integration with existing CRM systems and the ability to swiftly implement customized features ensured that the transition to Level AI was smooth, minimizing downtime and enhancing user adoption.
Conclusion: Elevating Self-Storage Sales & Customer Service Through Innovation
The partnership with Level AI exemplifies how integrating advanced AI into the self-storage industry can transform customer service. Level AI's platform addressed critical challenges such as transcription accuracy and operational efficiency, directly enhancing customer experience and reducing costs.
By streamlining processes and improving service quality, Level AI has become an integral part of their strategy to provide superior customer service. The ongoing commitment to leveraging Level AI's capabilities, including real-time coaching and CRM integration, underscores a forward-thinking approach to customer satisfaction.
This collaboration sets a benchmark for the self-storage industry, highlighting the importance of innovation in delivering exceptional service in an increasingly competitive market.
