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Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Cement
- Equipment & Machinery
Applicable Functions
- Maintenance
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
- Time Sensitive Networking
Services
- Data Science Services
The Challenge
Paytronix, a customer engagement platform for restaurants and small businesses, was facing a significant challenge in managing and deriving insights from its data. The company was dealing with data from multiple sources, running on various databases, and in disparate formats. The data ingestion tool they were using was unreliable and missed many transactions, leading to a lack of trust in the underlying data. Additionally, the company was using a mix of Scala and PySpark jobs for data transformation, which was custom code and handwritten. This toolset was unable to keep up with the growing demands of the business, and a lot of time was spent on maintenance and break-fix support. The company wanted to focus more on experimentation, but the existing system was not conducive to quick proof-of-concept testing and rapid iteration.
About The Customer
Paytronix is a customer engagement platform that serves over 1,800 brands in the restaurant and convenience store industries. The company helps its clients leverage their customer data to improve the digital marketing funnel and offer customers a seamless experience every time they visit the store, whether in person or online. The company has a team of seven, led by the Director of Data Science, Jesse Marshall, which works closely with the larger Strategy and Analytics team to provide clients with the insights they need to engage with their customers effectively.
The Solution
To address these challenges, Paytronix adopted Fivetran and Coalesce. Fivetran Local Data Processing was chosen for its easy setup and reliability, replacing the legacy ingestion tool. It was used to bring all the data from various sources into Snowflake. Coalesce was recommended by an industry colleague for its ease of use and flexibility. The company had also started using Snowflake Snowpark, which allows data scientists to code in languages other than SQL without having to take data out of Snowflake. Paytronix used Fivetran to bring data in near real time to Snowflake, then used Apache Airflow to trigger transformations in Coalesce and run the models in Snowpark. This setup enabled Paytronix to perform real-time predictive modeling at scale, offering its clients real-time information about their customers' activity.
Operational Impact
Quantitative Benefit
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