Resuelve is a Mexican consumer credit agency and savings program to help people challenged with credit card debt, car loans, and other financial burdens. The company provides valuable financial guidance and support like debt restructuring and flexible payment plans. Resuelve has an important mission, and now with the help of data analysis the company can focus on those people who need the most immediate help.
José Akle leads the Resuelve data team. He describes his group as “math and software geeks working to help Resuelve make better data-based decisions.” Prior to implementing a smart data stack, Resuelve data analysts were stuck with inefficient spreadsheets, manual joins and data silos which made lead scoring tedious and inaccurate. For Akle, part of making data based decisions included drastically improving the identification of leads. While a steady flow of consumer leads is usually good news for business, the organization had difficulty discerning the most qualified leads and the candidates in need of the most immediate support. Akle and his team were tasked with qualifying potential customers out of tens of thousands of leads generated every month. Akle knew that automated data management and machine learning were the key to intelligent lead scoring, so he set out to find the best tools for that strategy.
It was clear to Akle that a data warehouse needed to be implemented in order to achieve advanced, predictive lead scoring. During the process of identifying the right data stack for Resuelve, he started with connecting all the company data touchpoints for current and potential customers. He then selected Panoply’s smart cloud data warehouse to aggregate those data sources. Once the warehouse was up and running, connecting to external data sources through Panoply’s built-in integrations was straightforward and took only a few minutes to get up and running without the need of IT or engineering.
With Panoply in place Resuelve has become much more data-driven and agile. Prior to implementing a smart data stack, analysts and business team members did not have direct access to data that could inform and improve decision-making. In other words, the data was siloed. Now, instead of relying on help from engineering, team members from across the company can access and use data on their own. The ability for self-service data also freed up time for data engineers to enhance machine learning models. The most impactful contribution that Panoply enabled is intelligent lead scoring. With the ability to seamlessly join multiple data sources, the company’s lead scoring model is now more predictive and reliable. Akle shares that even though the company’s sophisticated lead scoring model includes 100 points, Panoply was able to manage every data source with lightning fast speed. He says:
To pull in all these data points from their various sources – we could only do it via Panoply.”
For Resuelve, implementing Panoply has empowered a major expansion of data management without additional staff members or database administrators. Akle also describes Panoply’s support as “unrivaled”.