case study

Emeritus Insights

Centralized Marketing Data Warehouse for Emeritus Insights

Education

Business Impacts

Data Silos Unification

Improved Data Quality

Data-Driven Decision Making

Comprehensive Insights, Dashboards, and Reports

Customer Key Facts

  • Industry : Education
  • Country : Singapore
  • Size : 1000+ Employees

Problem Context

Emeritus Insights, part of the Emeritus Institute of Management Pte Ltd, is a Singapore-based provider of online educational services. Accessible on the Emeritus App and website, Emeritus Insights is a one-stop solution to build 50+ in-demand skills. Working Professionals can learn from 5000+ bite-sized video lessons from the world’s best to build skills for success. Their marketing strategy involved employing various online channels like Google Ads, Facebook Ads, and Linkedin Ads. However, the data from these platforms were housed in silos. The client wanted to build a single view of these disparate data sources to analyze data, generate actionable insights, and perform downstream analytics with AI/ML applications.

Challenges

  • Data Consolidation across different data sources
  • Generating Marketing Insights at ad/ campaign level
  • Automated data ingestion from different data sources
  • Maintaining the data consistency

Technologies Used

Google Cloud Platform

Google Cloud Platform

Google Cloud Identity Access Management

Google Cloud Identity Access Management

Google Cloud Storage

Google Cloud Storage

Google Cloud Scheduler

Google Cloud Scheduler

Google Cloud Functions

Google Cloud Functions

Google BigQuery

Google BigQuery

Google Analytics 360

Google Analytics 360

Built a Robust, Future-Proof Marketing Data Warehouse by Leveraging Google Cloud Platform

Solution

Quantiphi built a robust, future-proof Marketing Data Warehouse by leveraging the Google Cloud Platform. The data collected from disparate sources like Google Ads, Facebook Ads, and LinkedIn Ads was ingested to Google's BigQuery through a third-party data connector and custom scripts on GCP. Data architecture was developed by keeping in mind the rules, standards, and models that will govern data collection, consolidation, integration, and consumption

The marketing data warehouse designed in BigQuery acted as a basis for generating actionable insights, fine-tuned reports, and crafting data-driven marketing strategies. It will also equip the client to perform downstream analytics and use this for numerous AI/ML applications.

Results

  • Consolidated view of data across 13 different sources (marketing channels, website analytics tools, CRM, social media channels, and more) to be used as a single source of truth for analytics and reporting
  • Reduction in average querying time from 2-3 minutes to just 50 seconds
  • End-to-end data automation of transformation and ingestion process of around 50 GB of incremental data pipelines
  • Generation of Fine-tuned dashboards and reports, highlighting KPIs of interest to different business stakeholders

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