Job Agency Moves to Real Time Insights
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Sisense
Tech Stack
- ETL tool
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Professional Service
- Software
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
Services
- System Integration
- Software Design & Engineering Services
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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