Qlik > Case Studies > Driving Higher Revenues and Accelerating a Digital Transformation with Qlik Cloud

Driving Higher Revenues and Accelerating a Digital Transformation with Qlik Cloud

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Customer Company Size
Large Corporate
Region
  • America
Country
  • United States
Product
  • Qlik Cloud
  • Qlik Sense Enterprise SaaS
  • Microsoft Azure
  • Databricks
  • Azure Data Storage
Tech Stack
  • Cloud Computing
  • Data Analytics
  • Internet of Things (IoT)
  • Data Storage
  • Business Intelligence (BI)
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Revenue Growth
  • Productivity Improvements
  • Customer Satisfaction
Technology Category
  • Infrastructure as a Service (IaaS) - Cloud Computing
  • Infrastructure as a Service (IaaS) - Cloud Storage Services
  • Analytics & Modeling - Big Data Analytics
Applicable Industries
  • Consumer Goods
  • Retail
Applicable Functions
  • Sales & Marketing
  • Business Operation
Use Cases
  • Predictive Maintenance
  • Real-Time Location System (RTLS)
  • Inventory Management
  • Demand Planning & Forecasting
Services
  • Cloud Planning, Design & Implementation Services
  • Data Science Services
About The Customer
CSC ServiceWorks (CSCSW) is a company that services laundry locations and air services at gas stations across the United States. These services are often overlooked because when they’re working well, no one thinks twice about them. But these are historically coin-based industries. As currency has increasingly moved into the digital sphere, these services are less convenient for the general public. CSCSW has been working to bring these services into the modern world by converting regular tasks to internet connected machines, including digital payments. About a decade ago, the payment technology for laundromats and air machines started to shift so that people could pay with a swipe of a card. It was a nice leap forward. As the years rolled on, the rise of the Internet of Things (IoT) made smart payments with mobile devices possible, connecting information on these payments to a main network. Now, CSCSW can monitor all of their machines from a central location, provide convenient app based payment, and offer a higher level of service. This has greatly improved the experience for their customers and has resulted in increased revenue for both their property managers and CSCSW.
The Challenge
CSC ServiceWorks (CSCSW) services laundry locations and air services at gas stations across the United States. These services are often overlooked because when they’re working well, no one thinks twice about them. But these are historically coin-based industries. As currency has increasingly moved into the digital sphere, these services are less convenient for the general public. About a decade ago, the payment technology for laundromats and air machines started to shift so that people could pay with a swipe of a card. It was a nice leap forward. As the years rolled on, the rise of the Internet of Things (IoT) made smart payments with mobile devices possible, connecting information on these payments to a main network. Now, we can monitor all of our machines from a central location, provide convenient app based payment, and offer a higher level of service. Just as CSCSW served a traditional coin industry, we had a traditional on-premise data warehouse platform when I joined the company as Chief Digital Officer in 2019. We have developers for these warehouses, who would run and share reports with others when necessary. It was functional in the past, but as we gained more data this wasn’t a viable solution. With connected machines, our thirst for data and analytics within the company grew exponentially. We needed to have easier access to the data within the organization so people could get the information they needed on their own, and developers could focus on more important work besides pulling queries and running reports. We needed a more efficient way of dealing with data and driving value for our consumers, clients and employees.
The Solution
CSC ServiceWorks moved to set up a modern, cloud-based unified data model with Microsoft Azure, Databricks, and Qlik Cloud analytics. They have numerous data sources, such as their machines, their customers, their app, their core revenue systems and their customer service systems. They ultimately drive those into their enterprise data lake, Azure Data Storage. Within Azure Data Storage, they use Databricks to process, curate, and produce data. Then they use Azure Synapse to normalize information across these different systems. On top of that data model is where Qlik Sense Enterprise SaaS resides. Qlik Sense produces the analytics and reports that they use to run the business. Qlik Sense processes heavier data loads faster than any other platform. They can create common data models across various data sets to get a standardized view of the data, as well as creating customizable tools and filters to take a closer look at the details as needed. Since all of the data is managed in one place and is accessible to everyone, they save costs. They don’t have to deal with hardware, infrastructure configuration, or upgrades that often require larger budgets to manage. Instead, their IT team can focus on projects that push their digital acceleration forward instead of spending most of their time on routine maintenance.
Operational Impact
  • Improved customer experience through more convenient payment methods and a mobile app that provides machine availability and notifications.
  • Increased engagement with consumers through the app, offering promotions and loyalty rewards.
  • Improved service team efficiency by proactively monitoring for machine issues and making repairs when a potential issue is noted.
  • Improved operations team efficiency by dynamically setting collection routes based on the amount of coins in the machine.
Quantitative Benefit
  • Reduced 15% of service truck rolls that were false positives, saving $2–$3 million a year on service routes.
  • Increased revenue through improved customer experience and engagement.

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