Custom CRM for a German SaaS Company

About the Project

Our client, a rapidly growing SaaS provider based in Germany, faced challenges in managing customer interactions, tracking sales pipelines, and handling an increasing volume of support tickets. The client required a custom CRM solution to streamline their sales processes, automate support ticket resolution, and provide actionable insights into customer behavior. QSoft was tasked with developing a custom CRM platform tailored to the client’s unique needs. The main focus was to automate sales workflows, improve the efficiency of the support ticketing system, and deliver real-time analytics to enhance the client’s understanding of customer interactions and engagement patterns.

Project duration: 6 months

Team Size: 6

Satisfaction Score: 94%

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Developing a Custom CRM for a German SaaS Company | Vietnam-Based Tech Team Ramp-Up

Developing a Custom CRM for a German SaaS Company

Project challenges

Challenges

Diverse Customer Requirements:
The client served a wide range of customers across various industries in Germany and beyond, each with unique workflows and requirements. Their existing CRM lacked the flexibility to meet these varying demands, causing inefficiencies in customer management. The challenge was to build a customizable system that could handle diverse customer needs while ensuring smooth scalability.
Automated Ticketing System:
The client’s support team was overwhelmed by the growing volume of support tickets, which required manual categorization and routing. This led to delays in issue resolution and decreased customer satisfaction. Automating this process was critical to improving response times and ensuring that tickets were prioritized and resolved efficiently.
Sales Workflow Management:
The sales team was managing multiple pipelines manually, resulting in missed opportunities and inefficient lead tracking. Without a consistent follow-up process, potential deals were often lost. The challenge was to implement a streamlined system that could automate lead tracking, prioritize opportunities, and ensure timely follow-ups.
Customer Behavior Analytics:
The client lacked the tools to gain real-time insights into customer interactions, making it difficult to understand behavior or predict churn. Without detailed analytics, they struggled to personalize services or make data-driven decisions. The challenge was to create a system that tracked and analyzed customer data across multiple touchpoints in real time, providing actionable insights.

How QSoft solves problems

Our Solutions

Custom CRM Design to Handle Diverse Customer Requirements:
QSoft built a customizable CRM using Node.js and React to handle the client’s diverse customer base. The system allowed users to create workflows tailored to specific customer segments, improving customer management efficiency. Role-Based Access Control (RBAC), implemented with JWT, ensured that teams only accessed relevant data, enhancing both security and focus. Real-time updates on customer interactions were stored in MongoDB, allowing dynamic and up-to-date customer profiles.
Automated Support Ticketing System:
QSoft integrated an AI-driven ticket classification system using Python, LLM to automatically prioritize and route support tickets. The system categorized tickets based on urgency and assigned them to the appropriate support agents, significantly reducing manual processing. Node.js and RabbitMQ managed the backend, automating workflows and escalation protocols to ensure fast resolution times.
Sales Pipeline Automation and Optimization:
The sales pipeline was automated using machine learning models in Python to score leads and prioritize high-value prospects. React dashboards provided real-time visibility, while Node.js managed backend processes, automating lead tracking and follow-up reminders. This system streamlined sales workflows, reducing manual tasks and increasing sales efficiency.
Real-Time Customer Behavior Analytics:
QSoft integrated Apache Kafka for real-time data streaming, enabling the client to monitor customer interactions across multiple channels. Predictive models in Python identified trends like churn risks, empowering the client to take proactive measures. Custom dashboards in Grafana provided actionable insights, allowing for data-driven decision-making and improved customer retention.

Project successful result

Results

Reduced Support Resolution Times by 50%:
The automated ticketing system allowed the support team to prioritize and categorize tickets more efficiently, significantly reducing the time it took to resolve customer issues. With AI-driven automation handling ticket assignment, support agents could focus on high-priority cases without being bogged down by manual processes. This led to a 50% reduction in overall resolution times, greatly improving customer satisfaction.
Increased Sales Efficiency by 30%:
By automating lead scoring, tracking, and follow-up processes, the sales team was able to focus on high-value leads and close deals faster. The CRM provided real-time visibility into the sales pipeline, allowing for more organized and timely interactions with prospects. As a result, sales efficiency increased by 30%, leading to more closed deals and higher revenue.
Improved Customer Insights:
The integration of real-time analytics allowed the client to gain deeper insights into customer behavior, such as product usage patterns and engagement levels. Predictive models helped identify potential churn risks, enabling proactive interventions to retain customers. These actionable insights helped the client personalize their services and improve overall customer retention and satisfaction.