Due to a recent transformation initiative, a multi-national financial services company was inundated with client data that they didn’t know how to best leverage. They were in need of a data science modeling strategy that could help them predict client growth potential.
They looked to phData to assist them in leveraging data-driven solutions to grow their business and improve their overall data sciences best practices.
phData’s initial data deep-dive strategy coupled with customer interviews helped identify a machine learning proof of concept: customer behavior insights can be revealed by tracking and segmenting transactional patterns. Following this concept, we created a model that not only segmented existing customer households and provided unique insights on each but also predicted the expected revenue growth for those households.
In addition, phData helped the client to establish and document best practices for how to continue this work to become a truly predictive organization.
A multi-national financial services company sought to draw business insights from an immense amount of client data that resulted from recently-completed digital transformation initiatives. Because individual financial advisors are fundamental to the financial services company’s success, it was imperative that the client data be interpreted and easily accessible to help them leverage data-driven solutions. Financial advisors can utilize customer behavior insights to create actionable, customized strategies for their clients.
Through a 10-week data strategy and machine learning engagement, phData and the client identified and refined a use case resulting in a data model: estimating a client’s total net worth of investable assets based on their transaction behavior, their relationships among household accounts, current trends, and client demographics.
The customer chose to work with phData because of our best-in-industry data science and machine learning practices.
By the end of the engagement, phData had created a model for the company that predicts revenue growth potential of existing households as well as a customer segmentation model to provide insights into households.
Additionally, phData helped the company develop a best practices document to guide their future efforts in becoming a truly predictive organization. The document included a step-by-step modeling plan, future prototypes, usable data sources, and other data analysis topics to explore.
Lastly, it included a strategy for data science development, including architecture recommendations, machine learning operations best practices, and words of caution.
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