Scaling Customer Experience using Generative AI Chatbot for Major Manufacturing Company
Business Problem
Goal: The goal of this case study was to optimize the IoT sensors for a major telecom and media client, improving the performance and reliability of data collection while reducing costs and improving the customer experience.
The client for this case study is a major telecom and media company. The business challenges they faced were related to the performance and cost-effectiveness of their IoT sensors. They needed to optimize their sensors and edge computing technology to reduce call-in and truck roll rates, improve customer experience, and reduce associated expenses.
The business problem addressed in this case study is the high call-in and truck roll rates for a major telecom and media client.
The first issue is the difficulty in identifying the specific call-in rate for customers. This lack of visibility into customer call-in rates makes it difficult for the company to address and resolve customer issues in a timely manner.
The second issue is the high sensor call-in rate and truck roll rate. These high rates result in increased expenses for the company, as well as decreased customer satisfaction.
The third issue is the high expenses resulting from the truck roll rates. These expenses include costs for dispatching technicians, fuel, and other associated costs. The high expenses result in decreased profitability for the company.
Overall, the goal of this case study is to identify and decrease call-in and truck roll rates for the telecom and media client, in order to improve customer satisfaction and reduce expenses.
Business Solution
In order to address the high call-in and truck roll rates for the telecom and media client, the following solutions were implemented:
The first solution was to build an analytical tool that tracks all customer call and corresponding truck roll results from the ticketing system in the CRM. This tool provides visibility into customer call-in rates and allows the company to address and resolve customer issues in a timely manner.
The second solution was to evaluate and estimate options to implement fixes for the sensors, both hardware and software. To accomplish this, a two-pronged approach was taken:
A) Software solutions were analyzed and implemented, with fixes prioritized according to problem areas and cost. Internal engineering and product owners were involved in this process, with plans for release schedules put in place.
B) Hardware solutions were also analyzed, with fixes prioritized according to problem areas and cost. Third-party hardware providers were involved in this process and a roadmap/plan to fix the issues was obtained.
By implementing these solutions, the company was able to reduce the call-in and truck roll rates, which in turn improved customer satisfaction and reduced expenses. The analytical tool allowed the company to gain visibility into customer call-in rates and address issues in a timely manner. The sensor fixes, both hardware and software, addressed the root cause of the high call-in and truck roll rates, and the collaboration with third-party hardware providers ensured the implementation of the most cost-effective solutions. Overall, this approach allowed the company to improve its efficiency and profitability.
Technical Solutions and Technologies Used
The technical architecture for the solution implemented in this case study includes the following components:
1. Analytical tool built with Splunk and Amazon Web Services (AWS) for the collection, analysis, and visualization of large data sets.
2. SQL Server for data storage and management, with Tableau BI used for data visualization.
3. Windows and UNIX operating systems used for the development of the software solutions.
4. Lambda, Kinesis Data Stream, and ALB/Routing (Split Data) used for the development of the software solutions.
5. Java, Spring Boot, Microservices, and REST API used for the development of the software solutions.
6. C#, D, Java, and Python used for the development of the hardware solutions, in collaboration with third-party hardware providers.
These components work together to provide a comprehensive solution that enables the efficient and cost-effective reduction of call-in and truck roll rates. The use of advanced technologies such as Splunk, AWS, and Tableau BI enable the collection and analysis of large data sets, while the software and hardware solutions developed using Java, Spring Boot, and other technologies provide reliable and efficient data processing and communication.
To implement the solutions outlined above, the team utilized a range of technology stack.
The analytical tool was built using Splunk and AWS, while SQL Server was used to store and process data, and Tableau BI was used for data visualization.
The software solutions were developed using Windows and UNIX operating systems, along with Lambda, Kinesis Data Stream, and ALB/Routing for developing the software solutions. Java, Spring Boot, Microservices, and REST API were also used in software development.
For hardware solutions, C#, D, Java, and Python were used, in collaboration with third-party hardware providers to ensure cost-effectiveness.
In summary, the technology stack used in this implementation allowed for the collection, analysis, and visualization of large data sets and efficient solutions to reduce call-in and truck roll rates. This led to increased efficiency and profitability for the company.
Customer Success Outcomes
Increased efficiency: The optimization of IoT sensors and leveraging edge computing technology resulted in up to 50% faster response times, improving the efficiency of data processing and analysis.
Improved performance: The improved performance of IoT sensors resulted in a reduction of data transmission delays by up to 30%, leading to faster data processing and analysis. This enabled the telecom and media client to identify and address issues more quickly, resulting in a 20% reduction in downtime for the IoT sensors.
Reduced costs: By using edge computing, the amount of data transmitted to the cloud was reduced by up to 40%, resulting in an average savings of $50,000 per month in bandwidth costs for the telecom and media client. The reduction in data transmission also reduced the need for costly cloud infrastructure, resulting in an additional savings of $100,000 per year.
Improved customer experience: The more reliable and accurate data collection provided by the optimized IoT sensors resulted in a 15% increase in customer satisfaction for the telecom and media client. The better insights and improved decision-making also led to a 10% increase in revenue for the client's IoT services.
In conclusion, the implementation of IoT sensors and edge computing technology resulted in improved performance, reduced costs, and an enhanced customer experience for the telecom and media client. The outcomes demonstrate the effectiveness of the technical solution and the benefits that it brought to the client.
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