Fivetran Case Studies Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce

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	Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce Fivetran

Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce

Fivetran

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	The adoption of Fivetran and Coalesce has brought significant operational benefits to Paytronix. The company can now offer its clients real-time information about their customers' activity, enabling them to run more effective marketing campaigns and offer tailored messaging that strengthens the personal connection with the brand and boosts engagement. The new system has also reduced the development time, allowing analysts to focus on their core tasks instead of creating ETLs. This has led to a single source of truth for the company's data, increasing trust in the data among the analysts. The ability to perform real-time predictive modeling at scale has also opened up new possibilities for the company's data science initiatives.

Impact #1

The adoption of Fivetran and Coalesce has brought significant operational benefits to Paytronix. The company can now offer its clients real-time information about their customers' activity, enabling them to run more effective marketing campaigns and offer tailored messaging that strengthens the personal connection with the brand and boosts engagement. The new system has also reduced the development time, allowing analysts to focus on their core tasks instead of creating ETLs. This has led to a single source of truth for the company's data, increasing trust in the data among the analysts. The ability to perform real-time predictive modeling at scale has also opened up new possibilities for the company's data science initiatives.

Benefit #1	Fivetran provides reliable, worry-free data ingest every 15 minutes
Benefit #2	With Coalesce, two new team members were able to complete a high-profile transformation in one month, a task that previously took the entire team 6 months without much progress
Benefit #3	Real-time predictive modeling is now possible at scale

Benefit #1

Fivetran provides reliable, worry-free data ingest every 15 minutes

Benefit #2

With Coalesce, two new team members were able to complete a high-profile transformation in one month, a task that previously took the entire team 6 months without much progress

Benefit #3

Real-time predictive modeling is now possible at scale

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Overview

Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce

Operational Impact

Quantitative Benefit