Revamping Sales Compensation Model with IoT: A Case Study on CUNA MUTUAL Group
Technology Category
- Networks & Connectivity - RFID
Applicable Industries
- Cement
Applicable Functions
- Sales & Marketing
Use Cases
- Time Sensitive Networking
Services
- System Integration
About The Customer
CUNA MUTUAL Group is a financial services company that has been in operation for over 80 years. The company is committed to helping credit unions, businesses, and individuals build financial security. With a revenue of $5B and assets worth $44.3B, CUNA MUTUAL Group is constantly innovating to stay ahead of the evolving financial landscape. The company operates on the principle of 'people helping people' and aims to create a more equitable financial system that improves the lives of their customers.
The Challenge
CUNA MUTUAL Group, a financial services company with $5B in revenue and $44.3B in assets, was facing a significant challenge in managing their sales data. The company was heavily reliant on spreadsheets for reporting, with 70 people depending on these reports on a weekly basis, resulting in the creation of 4,000 spreadsheets annually. The data frequency was also an issue, as salespeople were receiving weekly information, while real-time data was available in Salesforce. The process of increasing data frequency was unsustainable and inefficient. Additionally, the company was using a point-based sales compensation system, which was driving the wrong behavior among employees who were focusing on maximizing points rather than revenue.
The Solution
The company adopted Alteryx, a data analytics platform, to address these challenges. The platform enabled the company to eliminate the use of spreadsheets and move to daily updates, saving thousands of hours and cutting cycle time by a third to a half. The company also developed a new revenue-based sales compensation system using Alteryx. The build was completed in three months, with the team creating a single source of truth by gaining access to source systems, aggregating, cleaning, and blending the data. They then created a data science model based on metrics and customer attributes to forecast revenue. The new system was rolled out to all 500 frontline salespeople, leading to the best sales results in the past 10 years.
Operational Impact
Quantitative Benefit
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