Tredence build a holistic view of a large apparel retailer’s customer base using Customer Cosmos on Google Cloud Platform


Tredence works with one of the largest apparel retailers in North America, where we provide a wide range of services and solutions to advance their data, analytics, business intelligence, and governance capabilities.   

Over the course of a year, Tredence worked with this customer to lay the foundation of the future – migrating legacy workloads into GCP services to provide more intelligence about this retailer’s customers.  

With the foundation in place, our customer was looking for a one-stop solution which can help fulfill their vision to enable an enhanced customer experience and drive top and bottom-line improvements to the business. 

The client was interested in addressing some of the following challenges:  


  • Create seamless experiences across omnichannel touchpoints (Web, App, Paid & Owned Media) 
  • Automate content and creative delivery 


  • Real time personalization of app and web pages for signed-in and anonymous customers 
  • Determine Next Best Experience and personalize touch points across all paid and owned channels 
  • Allocate and optimize offers for each customer individually 

Measurement & Optimization:  

  • Measure ROI accurately for each media & marketing investments to the tactical level with no latency 
  • Estimate marketing spend required to deliver different growth objectives 
  • Optimize budget across funnels, channels, and publishers to maximize return 

The Challenge 

Unfortunately, these insights are difficult to obtain without the proper technology and solutions to aggregate a wide variety of customer data that exists across the organization, in a range of disparate systems.   

Many of the challenges that this customer faced are commonly faced across industries.  They include:  

  • Siloed Data and lack of connected data leading to poor customer insights  
  • Inefficient data models and poor workflows lead to low analyst productivity and heavy dependency on IT 
  • Lack of data availability 
  • Inability to move past the one-size-fits-all strategy, resulting in suboptimal experience and eventually losing customers to competition  
  • Dated segmentation models leading to prioritizing the wrong customers or customers who are already engaged
  • Lack of comprehensive customer strategy 
  • Lack of holistic measurement strategy 
  • Off the shelf Customer Data Platforms (CDP’s) are limited in their capabilities and being stretched to their limits 

To address these challenges, Tredence has developed a holistic customer analytics solution offering called Customer Cosmos.   


Customer Cosmos is implemented within our customers’ enterprise ecosystem as a white box solution. Unlike off-the-shelf Customer Data Platforms (CDP’s), no customer data leaves our clients’ infrastructure and the power of customer data rests within the company.  

Availability of all customer data collected, observed, predicted, and stored in a single place allows multiple teams within the organization to harness and build applications keeping customer data at the center of the design process. 

Customer Cosmos is an architecture pattern combined with proprietary data models that help to accelerate our clients’ journeys to tap into the full power of their data.  Below is an overview of the technical architecture of the Cosmos Solution that Tredence developed with this customer.  

Customer Cosmos Architecture:  

Services Used 

This deployment of Customer Cosmos used a variety of different GCP services as part of the end-to-end solution deployment.  Below is a table highlighting the various GCP services that were used to deploy Cosmos, and the rationale for each of the services used.  

Technology Requirement 

GCP Service 

Rationale to use the selected GCP service 

Cloud Data Warehouse 


  • BigQuery democratizes insights with a secure and scalable platform. It is a fully managed, serverless SQL data warehouse that allows for speedy SQL queries and interactive analysis of large datasets 
  • Serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility 


Object Storage 

Cloud Storage 

  • GCS provides a robust storage solution to store incoming flat/semi/unstructured source files such as marketing performance data, external factors etc. 
  • Provides a scalable storage solution to host the file system needed for the data lakes 

ML Development 

Vertex AI Workbench 

  • Vertex AI Workbench provides a single environment for data scientists to complete all of their ML work, from experimentation, to deployment, to managing and monitoring models. 
  • It’s Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities is used to develop AI/ML models for Cosmos 

Data Processing Engine 


  • Dataproc clusters are quick to start, scale, and shutdown, with each of these operations taking 90 seconds or less, on average 
  • Dataproc gives the maximum features and flexibility of operating the clusters in the cloud while maintaining a low learning curve for the developers. 
  • Dataproc’s auto scaling feature helps in seamless processing during peak workload periods? 
  • PySpark based AI/ML model can be executed on Spark cluster through Dataproc 
  • Spark clusters can be created within Cloud composer DAGS 


Cloud Monitoring 

  • Cloud Monitoring is used to create alerts for data pipelines executed through Cloud composer. 


Cloud Composer 

  •  Cloud composer is used to orchestrate the DE and ML pipelines. 

Key Benefits

Customer Cosmos has provided a wide range of benefits to the organization, including:  

  • High performing, cost effective compute and analytics capabilities  
  • Resolving enterprise data challenges, eliminating the need to store customer data across different platforms 
  • Enabling the organization’s personalization strategy
  • Improved measurement of marketing campaigns  
  • Enhanced customer insights  

In summary, Customer Cosmos has enabled our client to integrate completely new analytics capabilities into their business processes, while understanding their customers at a much more granular level.  And we’ve helped them do it faster – implementing in a fraction of the time that other solutions in the market take.


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