Our Work

Case Studies

Real-time Customer Loyalty Management for a Global Beauty Company

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Numbers Speak For Themselves!

Curated Products

4800 +

Curated Products

Product Categories

Challenge

The client, a prominent beauty company, wanted to enhance their customer loyalty program to provide real-time updates on tier status and accumulated points. With an ever-growing customer base across various countries, handling vast data amounts while ensuring a consistent and personalized experience for customers was a challenge.

Our Approach

Our solution centered around using Google Firestore for real-time data handling and Google BigQuery for robust data analytics. The approach was divided into two main parts:

1. Bulk Updating of Customer Data

Initialization:

Data Extraction:

Data Processing:

Data Loading to Firestore:

2. Real-time Order Processing

Initialization:

Data Processing:

Order Handling: For each order received via POST request, the script calculated the total price and converted it based on the country code.
Tier Calculation and Update: The script updated the customer's accumulated price, determined the new tier, and calculated progress towards the next tier.
Order Date Handling: The order date was recorded according to the UAE timezone.

Data Loading:

Solution

Our solution centered around using Google Firestore for real-time data handling and Google BigQuery for robust data analytics. The approach was divided into two main parts:

1

Bulk Updating of Customer Data

Initialization:

Data Extraction:

Data Processing:

Data Loading to Firestore:

2

Real-time Order Processing

Initialization:

Data Processing:

Order Handling: For each order received via POST request, the script calculated the total price and converted it based on the country code.
Tier Calculation and Update: The script updated the customer's accumulated price, determined the new tier, and calculated progress towards the next tier.
Order Date Handling: The order date was recorded according to the UAE timezone.

Data Loading:

Challenges & Achievements

Challenges

Data Volume: Handling a large volume of customer data without lag or performance issues.
Real-Time Processing: Implementing a system that updates customer tier information instantly after every purchase.
Currency Conversion: Managing currency conversion logic for different countries.
Integration Complexity: Coordinating between Firestore and BigQuery to ensure data consistency.

Achievements

Efficient Data Handling: Bulk updating allowed for efficient handling of large datasets.
Personalized Customer Experience: Real-time updates enabled customers to see their loyalty tier status instantly, enhancing their shopping experience.
Scalable Solution: The system is built to handle additional growth, accommodating the retailer's expansion plans.
Analytics Integration: By logging key information into BigQuery, the client can now leverage advanced analytics to derive insights and make data-driven decisions.

Achievements

Efficient Data Handling: Bulk updating allowed for efficient handling of large datasets.
Personalized Customer Experience: Real-time updates enabled customers to see their loyalty tier status instantly, enhancing their shopping experience.
Scalable Solution: The system is built to handle additional growth, accommodating the retailer's expansion plans.
Analytics Integration: By logging key information into BigQuery, the client can now leverage advanced analytics to derive insights and make data-driven decisions.

Secondary Info

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Architecture Diagram

Conclusion

Through intelligent integration of Google Firestore and Google BigQuery, we delivered a system that not only meets the client’s current needs but is scalable for future growth. The project demonstrates our expertise in building real-time, data-driven solutions that enhance customer engagement.

Solution

Build a self-service Data Mart to service Standard & Ad-hoc Reporting

Client has been provided with a new level of performance optimization, scalability, and analytical depth by embracing advanced data modeling techniques as below:

Multiple Transactional Big Data Source, data migrated to Google Cloud
Dally ETLs developed to transform data on a regular basis
Large Scale Power BI Data Marts developed for the self service reporting
Additional standard dashboards developed on Power BI
Incremental Refresh and Data Partitioning implemented on Power BI data models through Tabular Editor and SQL Server Management Studio
Alerts and Data Quality Checks set up for proactive monitoring

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Brittany Foxx

Abu Dhabi