About the Customer:
Our customer is a leading on-demand service platform in Southeast Asia, offering a range of services including food delivery, ride-hailing, and digital payments.
Founded in 2010, it has rapidly expanded to become a key player in the region’s tech and transportation sectors.
Business Challenges:
- Our customer needed an OLAP data model and data warehouse to manage increasing volumes of complex food delivery data, including transactions, customer preferences, and performance metrics for faster decision-making.
- There was a requirement to de-normalise clickstream data in Google Cloud Platform (GCP) Big Query for better insights. Clickstream data from food platform (such as user interactions, clicks, and page views) was highly granular and stored in a normalised format, making it difficult to derive quick insights.
- They wanted to empower their pre-sales teams and merchant partners (e.g., restaurants) with better insights to optimise pricing, promotions, and product offerings. Without accurate, accessible data, decision-making was reactive and based on limited or outdated information.
Embitel’s Solution:
- Designed and Developed an OLAP Data Model and Data Warehouse
- A custom OLAP (Online Analytical Processing) model was created to allow our client to aggregate and analyse large datasets across different business dimensions like customer demographics, order types, and geographic locations. This helped streamline the analysis process and improved decision-making on operational metrics.
- A centralised data warehouse was built to house the OLAP model and serve as a single source of truth for all food delivery data. This data architecture facilitated high-performance querying and reporting, helping teams at the organisation make more informed decisions.
- Denormalized Clickstream Data in GCP Big Query for the Food Delivery Platform
- Our team optimized the customer’s clickstream data by de-normalizing it in GCP Big Query, which boosted query performance and enabled quicker insights into user interactions on their food platform.
- This de-normalization simplified the data model, making it easier to uncover patterns, trends, and opportunities for improving the customer journey, ultimately enhancing user experience and driving higher customer retention.
- Developed and Published BI Reports Using Tableau and PowerBI
- BI reports were created using Tableau and PowerBI, enabling business users at the company to visualise key metrics such as order volumes, customer acquisition costs, and campaign effectiveness.
- Interactive dashboards and custom reports were published for internal teams and merchants, empowering them to make data-driven decisions and optimise their business operations, from marketing campaigns to supply chain management.
- Assisted Pre-sales Teams and Merchants in Making Data-Driven Decisions
- Customised dashboards and reports provided the customer’s pre-sales teams with real-time insights, enabling them to assist merchants in optimizing promotional activities, pricing strategies, and product offerings.
- The solution also allowed merchants to assess the effectiveness of their promotions, empowering them to quickly adjust and implement changes based on performance data.
- Provided Insights to Improve Average Order Counts and Evaluate the Impact of Campaigns and Promotions
- Data insights were used to identify key drivers of order volume, such as peak hours, regional preferences, and popular menu items, helping to optimise offerings and promotions.
- The solution also helped evaluate the effectiveness of campaigns, allowing our customer and their merchant partners to measure ROI on marketing efforts and adjust strategies to drive higher average order counts and customer engagement.
- Established Standard Practices for Clean Code and Maintained a Data Catalog for Data Dictionary and Lineage
- Best practices for writing clean, maintainable code were established, ensuring consistency, quality, and long-term scalability of the data architecture.
- A data catalog was implemented to document data lineage and definitions, helping the team ensure data quality, maintain compliance with standards, and facilitate data governance, making it easier for users to trust and understand the data.
Embitel Impact:
- Enhanced tracking of KPIs and performance metrics helped align strategies with business goals and customer needs.
- Improved revenue generation per click/view basis.
- Increased average order counts and sales through effective campaign analysis.
- Maintained high data quality and compliance with data standards.
Tools and Technologies:
- Database: Oracle, Azure Synapse, Google Big Query, Amazon Redshift
- Language: SQL, Python
- Tools and Technologies: Microsoft PowerBI, Tableau, Apache Preset
- Data Modelling: Erwin, Draw.io
- Data Catalog: Unity Catalog, Azure Purview
- Data Quality: Great Expectations
