保险经纪公司利用 Catalytic 提高流程效率和准确性
公司规模
Mid-size Company
地区
- America
国家
- United States
产品
- Catalytic
技术栈
- Automation
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 应用基础设施与中间件 - 数据交换与集成
适用功能
- 商业运营
- 销售与市场营销
用例
- 补货预测
服务
- 系统集成
关于客户
客户是一家全国性保险和金融服务公司，为各种规模的企业提供保险套餐选择服务。该公司在 15 个州设有 40 个办事处，为超过 20,000 名客户提供服务。该公司拥有 750 名员工，在提供服务时涉及大量手工工作。提案包括来自多家不同保险公司的报价，最多涵盖五种不同类型的保险，包括医疗、牙科、人寿等。每个客户提案通常有十几页或更多页，并根据保险公司和具体保险类型采用独特的格式。
挑战
这家保险和金融服务公司一直在努力应对从报价到提案的手动流程，这种流程效率低下，对收入产生了负面影响。该流程需要员工手动审核每份报价，提取 200 多个字段的数据来创建电子表格，并开发一份提交给客户的综合报告。一旦选择了计划，公司就必须准备一份包含所有保险细节的客户入职手册。整个过程繁琐、耗时，而且容易出错。由于该流程的手动性质，与小客户开展业务的成本非常高。
解决方案
该公司的领导团队与 Catalytic 接洽，希望他们能部署自动化来改善流程。他们有五个目标：节省大量员工时间、更快地向客户提供提案、减少拼写错误和人为错误、建立报价数据库并使用商业智能和机器学习使其随着时间的推移变得更加智能，并建立成功案例以在公司及其子公司范围内推广智能自动化。Catalytic 自动化了六个步骤中的五个，将每个报价的处理时间缩短到大约一分钟，准确率超过 95%。
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
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.
