Job Agency Moves to Real Time Insights
公司规模
1,000+
地区
- America
国家
- United States
产品
- Sisense
技术栈
- ETL tool
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
- 应用基础设施与中间件 - 数据可视化
适用行业
- Professional Service
- Software
适用功能
- 商业运营
- 销售与市场营销
用例
- 实时定位系统 (RTLS)
服务
- 软件设计与工程服务
- 系统集成
关于客户
Bold is a company transforming the career industry through the creative use of technology. They work with job seekers and recruiters to connect the best people and companies. They provide subscription-based services, including resume builders, cover letter builders, interview prep, job postings, and worker postings. Bold collects a huge volume of data, currently 60TB, and actively analyzes 2TB. The company aims to fine-tune their builder tools for maximum efficiency by understanding which subscription types are getting renewed the most, which products are being purchased the most, and the most effective model for connecting employees to employers.
挑战
Bold collects a huge volume of data, currently 60TB, and actively analyzes 2TB. They provide subscription-based services, including resume builders, cover letter builders, interview prep, job postings, and worker postings. Each subscription has different frequencies and levels that need to be tracked. They wanted to see which subscription types were getting renewed the most, which products were being purchased the most, and the most effective model for connecting employees to employers. Their existing tool for visualizing transactional data was not meeting their needs. Balaji Jayapal, Head of BI and Big Data, sought a better way to manage their 2TB of transactional data and visualize it effectively.
解决方案
Balaji Jayapal conducted an extensive search for a new tool, testing several on live marketing data and showing them to various departments to get buy-in. Sisense was chosen for its superior visualizations and ease of use. Sisense was implemented in several creative ways, including system monitoring for variances outside of standard deviation and reducing the number of credit card chargebacks. The Platform team used Sisense to analyze credit card chargeback ratios, identifying the most problematic cards and prompting users to use a new card or bail out of the purchase. This process significantly reduced chargeback rates. Sisense was also used to create 20 Elasticubes and 50 dashboards across various data types, including transaction, business, ecommerce, and marketing data. The ETL tool with Sisense enabled hourly analyses of transactions, applying standard deviation to compare to the prior six months and sending alerts for any significant variances.
运营影响
数量效益
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