About Client:
A leading real estate management company with a diverse portfolio of residential and commercial properties across multiple regions. The organization manages a wide range of operations, including property maintenance, tenant relations, leasing, sales, and customer service.
With a growing portfolio and high volumes of customer interactions, efficient communication and access to reliable customer insights are essential for delivering consistent service and maintaining strong tenant relationships.
Background:
For years, the company relied on a combination of legacy systems to manage customer interactions. Its primary CRM was basic, outdated, and offered limited integration capabilities.
Most importantly, the CRM was disconnected from one of the company’s most important customer communication channels—phone calls.
Agents manually documented and summarized conversations, often using inconsistent formats and levels of detail. As a result, valuable context from customer interactions was frequently lost, making it difficult for teams to understand previous conversations or maintain a complete customer history.
The organization also lacked a structured way to analyze call data at scale. Recurring maintenance concerns, customer sentiment, leasing inquiries, and service trends remained hidden within thousands of hours of unstructured conversations.
The client needed a modern solution that could transform call interactions into structured, actionable data while giving agents a unified view of each customer.
Challenge:
No Actionable Insights from Calls: Without automated call analysis, the client had limited visibility into recurring customer concerns, maintenance trends, service issues, and other patterns hidden within conversations.
Inefficient Agent Workflows: Agents spent significant time taking notes, updating multiple systems, and searching for historical information. The lack of a unified customer view also made personalized service more difficult.
Unstructured Data as a Roadblock: Thousands of hours of call transcripts and conversations existed as unstructured information that could not easily be analyzed or incorporated into operational decision-making.
Fragmented Customer Context: Customer information was distributed across disconnected systems, preventing agents from quickly understanding previous interactions, open issues, and relationship history.
Poor Customer Experience: Customers often had to repeat information because agents lacked complete context from earlier interactions. This created frustration, slowed issue resolution, and affected trust in the service experience.
Solution:
To address these challenges, the client partnered with Supply Medium to implement a cloud-native customer intelligence solution powered by Microsoft Azure and Dynamics 365.
Real-Time Transcription and Analysis:
Using Azure AI services, customer calls were automatically converted into searchable text, creating structured digital records of conversations that could be analyzed and connected with existing customer information.
This reduced dependence on manual note-taking while preserving important context from customer interactions.
Data Ingestion and Unification:
Call transcripts were stored in Azure Data Lake Storage Gen2, creating a scalable repository for conversation data.
A custom processing pipeline extracted relevant information such as tenant names, property addresses, issue categories, and other key entities. Sentiment analysis was also applied to provide additional context around customer interactions.
These insights were integrated into Dynamics 365 Sales and Customer Service, bringing customer records, conversation history, call insights, and service information into a unified environment.
Intelligent Agent Experience:
Dynamics 365 was enhanced to provide agents with a comprehensive 360-degree customer view.
Agents could access call summaries, sentiment insights, historical interactions, property information, and relevant customer context from a single interface.
This reduced time spent searching across systems and enabled agents to provide faster, more informed, and more personalized service.
Proactive Insights and Automation:
Azure Machine Learning was used to analyze patterns across customer conversations and identify recurring themes.
The platform could surface trends such as frequently reported maintenance issues, common leasing questions, recurring service concerns, and other patterns requiring attention.
These insights enabled teams to move beyond reactive customer service and identify opportunities for proactive operational improvements.
Outcome:
15–20 Minutes Saved per Interaction: Automated call summaries and unified customer histories reduced the administrative effort required for each interaction, saving agents approximately 15–20 minutes.
Higher Agent Productivity: Reduced note-taking, automated data capture, and centralized customer information allowed agents to focus more time on resolving customer needs.
More Personalized Customer Experiences: Access to complete interaction histories and relevant context reduced the need for customers to repeat information and enabled more consistent service.
Faster Issue Resolution: Unified customer information and conversation insights helped agents understand issues more quickly, supporting improved first-call resolution.
Actionable Customer Intelligence: Leadership gained visibility into recurring maintenance concerns, customer sentiment, common leasing inquiries, and potential employee training needs.
Improved Self-Service: Insights from frequently asked questions and recurring customer concerns helped the organization refine FAQs and other self-service resources.
Scalable Foundation for AI Innovation: The cloud-native Azure ecosystem established a flexible foundation for future AI-driven capabilities, automation, advanced analytics, and customer experience improvements.
The transformation enabled the client to turn previously underutilized call data into actionable intelligence, helping agents work more efficiently while delivering faster, more informed, and more personalized experiences across the real estate portfolio.