![case study](https://cdn.quantiphi.com/2022/08/93f8535d-marketing-analytics-case-study-banner1.png)
Customer Data Platform for an American Payment and Financial Services Provider
BFSIBusiness Impacts
Increased ROI on ads spend
Built a foundation to achieve data-driven marketing analytics
Provided a 360-degree customer view
Enhanced efficiency in ad hoc rule management
Customer Key Facts
- Country : United States
- Size : 2400+ Employees
- Industry : BFSI
Problem Context
The client is a US-based cross-border P2P payments and money transfer company that provides services across the world at both digital and retail locations. They needed a 360-degree view of their customers to achieve real-time communication with them based on the ad hoc rules created by the client’s marketing team.
Challenges
- Multiple entry points for data from various sources
- Integration of data from web and application-based sources
- Difficulty in real-time data ingestion from sources to Google BigQuery
- Maintaining bidirectional dataflow to and from Salesforce
![](https://cdn.quantiphi.com/2024/03/3a0f1407-marketing-analytics-challenge-image.png)
Technologies Used
![Google Analytics 360](https://cdn.quantiphi.com/2022/08/5ac17efb-google-analytics-360.jpg)
Google Analytics 360
![Firebase](https://cdn.quantiphi.com/2024/02/Cloud-Dataflow.png)
Firebase
![Google BigQuery](https://cdn.quantiphi.com/2024/02/BigQuery-e1656078051285.png)
Google BigQuery
![Looker](https://cdn.quantiphi.com/2024/02/2348c9b8-looker.png)
Looker
![Google Cloud Function](https://cdn.quantiphi.com/2024/02/Cloud-Functions-e1656078444597.png)
Google Cloud Function
![Salesforce](https://cdn.quantiphi.com/2024/02/e5ffe9ba-salesforce-logo.png)
Salesforce
![Google AI Platform](https://cdn.quantiphi.com/2022/08/AI-Platform.png)
Google AI Platform
![Google App Engine](https://cdn.quantiphi.com/2024/02/9cd2ad31-app_engine.png)
Google App Engine
Solution
Quantiphi initiated data engineering by cleaning and transforming client's data. We built rule-based engines to process the data and cater to ad hoc requirements by referring to the real-time Customer Data Platform.
We built various ML-based models, like Churn, CLTV, Segmentation, and Propensity, in order to enable the client to identify the next best action for their customer.
Result
- Improved marketing-related attributes like return on ad spend (ROAS) and optimal targeting of customers
- Unified view of every customer at an individual level from various data sources to identify them using unique Golden IDs
- Facilitated data-driven marketing analytics for an intelligible understanding of business operations and marketing efforts
- Enhanced efficiency of ad hoc rule management