International Publisher of Daily Business News Transforms Business Model with Birst
Customer Company Size
Large Corporate
Country
- Worldwide
Product
- Birst
Tech Stack
- Salesforce
- SSRS
- Jaspersoft
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Cloud Planning, Design & Implementation Services
About The Customer
The Company is one of the world’s leading business news organizations. It is recognized internationally for its authority, integrity, and accuracy in providing essential news, commentary, data, and analysis for the global business community. The Company underwent a significant transformation from print to digital, with a new business model focused on subscription revenue replacing advertising revenue.
The Challenge
The Company underwent a significant transformation from print to digital, with a new business model focused on subscription revenue replacing advertising revenue. The Company sought a solution to democratize Business Intelligence (BI) across the organization and drive the Company in new directions. The objectives were to transition from an ad revenue-based business to a subscription revenue model, enable a deep understanding of the subscriber base, and provide the organization with self-service analytics to make data-driven decisions. However, the Company faced technical challenges, including a highly customized “spaghetti” of legacy applications in place and the need to replace SSRS and Jaspersoft reporting solutions.
The Solution
The Company chose Birst for its robust integration with Salesforce and its automatic aggregation of multiple data sources to provide a 360-degree view of the customer. Birst's easy-to-use self-service analytics capabilities were also a key factor in the decision. The implementation of Birst led to the creation of a BI portal for business users to explore governed data sets in a self-service fashion, reducing the reporting burden on the analytics team.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting
Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals.
Case Study
Designing an intuitive UI for effective product demand forecasting in retail
The client, a leading luxury store chain operating in over 100 countries, was facing challenges with their product demand forecasting process. The process involved a significant amount of manual work, with all sales-related data being kept in Excel tables and calculated manually. The client's merchandising and planning experts used a demand forecasting web application to make estimations of customer demand over a specific period of time. The solution calculated historical data and other analytical information to produce the most accurate predictions. However, the client wanted to improve the efficiency and effectiveness of this process, making it faster, more accurate, and less complicated for their employees. They sought to unify all processes under an intuitive UI.
Case Study
YAZAKI Europe Limited: Ensuring rapid, cost-efficient order fulfillment and quicker insights into business performance
YAZAKI Europe, a leading automotive supplier specializing in the production of customized wiring harnesses for car manufacturers, was facing challenges in meeting the automotive industry’s demand for same-day delivery. The company's continuous growth was impacting its ability to analyze performance and complete its end-of-month consolidation and reporting processes for its headquarters in Japan. The company needed to eliminate delays and manual processing in its logistics to ensure extremely efficient operations. The company also needed to boost its data analytics capabilities for its finance and controlling departments to accelerate the delivery of complex financial reports.
Case Study
How the Philadelphia 76ers Win Off the Court Using Machine Learning from DataRobot
The Philadelphia 76ers, a professional basketball team in the NBA, is part of a new wave of sports franchises that are leveraging data analytics to optimize both their on-court performance and business operations. The organization has a strong focus on using data to inform decision-making processes across all levels. One of the key challenges faced by the 76ers' Analytics Team was improving the efficiency of their season ticket renewal process. The team had been using data science and simple modeling techniques, but lacked a dynamic machine learning tool that could adapt and learn as more data was collected. This meant that the team had to do a lot of work in the offseason to produce a static model. The goal was to transform the renewal process from a once-a-year event into a year-round retention process.
Case Study
Getin Noble Bank S.A. Personalizing offers to meet customers’ specific banking needs raises savings deposits by 20 percent
Getin Noble Bank S.A. experienced several years of rapid growth, at twice the pace of the rest of the Polish banking sector. As the market became saturated, customers demanded a higher level of service. To create tailored product offers to meet their needs, the bank needed a more efficient and automated method of customer segmentation. It also wanted to develop effective campaigns with repeatable and predictable results.
Case Study
Milgard: Predicting sales with precision to drive swift, efficient production
Milgard Manufacturing, Inc., a leading producer of windows and patio doors, operates on a demand-driven manufacturing model to avoid overstocking issues. This approach requires the company to predict sales extremely accurately to ensure it has the resources needed to fill incoming customer orders quickly. Sales of windows are influenced by various factors, including housing market fluctuations and regional preferences. Therefore, Milgard needs to ensure that its annual budgets and forecasts accurately reflect the impact of these factors. The company's inflexible approach to planning threatened to hamper business agility, prompting it to seek a better solution.
