Insurance brokerage uses Catalytic to drive process efficiency, accuracy
Customer Company Size
Mid-size Company
Region
- America
Country
- United States
Product
- Catalytic
Tech Stack
- Automation
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- System Integration
About The Customer
The customer is a national insurance and financial services firm that facilitates the selection of insurance packages for businesses of all sizes. The company has a footprint across 40 offices in 15 states and serves over 20,000 customers. The firm employs 750 people and is involved in a significant amount of manual work in delivering its services. Proposals include quotes from several different insurance carriers on up to five different types of insurance, including medical, dental, life, and more. Each client proposal is typically a dozen or more pages and uniquely formatted based on the insurance carrier and the specific insurance type.
The Challenge
The insurance and financial services firm was struggling with a manual quote-to-proposal process that was inefficient and negatively impacting revenue. The process involved employees manually reviewing each quote, extracting more than 200 fields of data to create a spreadsheet, and developing a consolidated report presented to the client. Once a plan was selected, the company would have to prepare a customer onboarding booklet with all coverage details. The entire process was tedious, time-consuming, and prone to error. The cost of business with small clients was incredibly high due to the manual nature of the process.
The Solution
The company’s leadership team approached Catalytic to help deploy automation to improve the process. They had five goals in mind: save significant employee time, deliver proposals to clients more quickly, reduce typos and manual errors, build a repository of quote data and use business intelligence and machine learning to make it smarter over time, and establish a success case to expand intelligent automation across the company and its subsidiaries. Catalytic automated five of six steps to reduce the processing time per quote to approximately one minute with more than 95% accuracy.
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.
