Technology Category
- Analytics & Modeling - Machine Learning
- Functional Applications - Enterprise Asset Management Systems (EAM)
Applicable Industries
- Finance & Insurance
- Retail
Applicable Functions
- Maintenance
- Sales & Marketing
Use Cases
- Construction Management
- Infrastructure Inspection
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Banca Mediolanum is a leading Italian bank, insurance, and asset management company with over 1.2 million customers. The bank has 2,200 core employees who work with 4,300 affiliated financial advisors, assisting with family asset and portfolio management. Banca Mediolanum is listed on the Borsa Italiana and the FTSE MIB index. The bank was the first in Italy to take its services online and has built a reputation as one of Italy’s leading retail banking institutions. Its sophisticated marketing operation brings in over 10,000 new customers a month.
The Challenge
Banca Mediolanum, a leading Italian bank, insurance, and asset management company, was facing challenges in managing and analyzing the vast amount of data it was collecting from various sources. The bank's sophisticated marketing operation, which brings in over 10,000 new customers a month, was being hampered by the diverse formats in which data was arriving. The bank was struggling to make the most of its available information and was in need of a powerful data hub that could handle the massive and highly varied data. The bank was also looking for a solution that could minimize infrastructure maintenance, allowing its teams to focus on adding value.
The Solution
Banca Mediolanum turned to Google BigQuery, a powerful data hub that could handle the bank's massive and highly varied data. With assistance from digital analytics experts at BitBang, the bank migrated its existing data to Google Cloud in just one week. The bank also made extensive use of Google Analytics 360, DoubleClick, and AdWords, all of which seamlessly integrated with the Google Cloud infrastructure. The simplicity of Google Cloud and the ease with which it was deployed allowed the bank to create proof of concept iterations of new processes without generating excessive costs. Furthermore, Google BigQuery's simple and intuitive interface enabled staff to run queries and generate insights soon after the software was deployed. The bank is now looking to extend the reach of its marketing with machine learning algorithms based on Tensorflow, created around models optimized by BitBang.
Operational Impact
Quantitative Benefit
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