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Achieving Efficiency with Descriptive Analytics






Santiago, Chile

Use Case

Data Analytics


Banco de Chile is a leading financial institution in Chile that offers a wide range of banking and financial services. Its focus on customer service, innovation, and social commitment has positioned it as one of the country's most important and trusted banks. The Bank has Travel Club, which is the loyalty and benefits program offered by Banco de Chile.

It allows customers to earn points for their transactions and loyalty, and then redeem them for a wide variety of products, services, and experiences. The program aims to reward customers and provide them with additional benefits for their relationship with the bank.

Business Challenges

Understanding customers: One of the most important challenges was to understand the needs, preferences, and behaviors of customers. Obtaining a clear and up-to-date view of the customer profile was essential for selecting the right products that are attractive and relevant to them.

Customer segmentation was often incomplete (not considering all data sources in this implementation) and was a manual process carried out in spreadsheets without considering the security of sensitive information contained in them.

Another challenge was to ensure a diversified and attractive product offering. Adapting the product offering to different customer segments was also a challenge to be considered. Unifying multiple data sources and creating a data model foundation was key for the sales department to make the best decisions.

For this project, 11 sources of interest were defined to be included in the Data Lake for processing and use in creating the model that was visualized in a single dashboard. The model needed to include a consolidated customer base and their interactions with marketing campaigns, sales, and the contact center. Visualizing this model needed to be able to relate customers and their interactions and, more importantly, provide the company with information about potential customers to be contacted in the future for marketing campaigns.

The business needed to have 1 year of transactional data (sales, marketing, and contact center) available on the dashboards. In the starting state, 11 data sources were considered, which were not related or communicated with each other, and a considerable amount of manual intervention and analysis were required to consolidate the information and identify potential customers.

Solution Proposed

The solution focused on the final product that the user needed to access centralized information about Travel's customers and their interactions with the business and the bank. For this purpose, AWS services were selected that allowed for the secure and continuous migration of data from various platforms. Automatic data flows were created to model the various data in a structured manner, ensuring the security of sensitive customer data. The Customer data model was configured to reside within AWS Redshift.

Dashboards and insights were generated in AWS Quicksight, which is the perfect tool to read the structured model from Redshift, allowing for analysis of customers by consumption, location, service channels, and generating segmentation based on all the data collected from their different platforms and interactions. AWS Quicksight enables the creation of new reports for future analysis in case customer segmentation changes to consider other data from the created model.

In addition to this, Travel requested the option to download the customer information list for email marketing purposes, giving this solution a secondary purpose to assist in their daily marketing operations.

AWS services used as part of the solution

  • AWS Lambda: Used to execute integration and ingestion functions.

  • AWS Glue (Data Catalog): Used to store the created analytical datasets.

  • AWS Social Media Service: Sends notifications for each StepFunctions execution error in addition to alerting Cloudwatch alarms.

  • Used to manage users and roles, as well as the permissions used by the different resources implemented in the account.

  • AWS Quicksight: Used as a BI tool to generate reports. Applied solution for easy-to-understand insights.

  • AWS Redshift: Used as the primary data warehouse for analytical models.

  • AWS StepFunctions: Used to execute data transformation and ingestion pipelines.

  • LakeFormation: Used for permission and location governance in the Data Lake.

Results and Benefits

We created a single data bank of Travel's customer data and a corporate data lake that allowed for customer segmentation and a better understanding of consumers.

Now they can easily create customer segmentation in Quicksight, modify it as needed in the report, download the result, and conduct email marketing campaigns easily and automatically.

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