1280pxx Deloitte
Customer Story

Large Scale Customer Journey Analysis with AWS

Deloitte uses AWS Data Services to analyze and optimize data throughout the lead-to-sales process and beyond.



Deloitte GmbH

Software Development & Kubernetes Engineer

Industrial Sector
Customer since


Independent development

From features through automated deployment pipelines

Versioned and well documented infrastructure

Through Terraform.

Unified Configurations

And auditable security concepts.

Modular, Simple and Scalable Pipelines


Possibility of cross-analyses across various data sources


Implementation of security best practices

Through a structured rights and roles concept.

In A Nutshell:


  • Task: In this customer project, the aim was to bring together all the collected data in the group in a data lake and make it usable.

  • Team:

    • A total of 25 people consisting of POs, DevOps Engineers, Cloud Architects, Data Engineers and Dashboard Developers.

  • Project duration: +2 years


  • Consolidation of all customer-related data sources (Marketing, Sales, Finance, …) in a central data lake
  • Analyze data to better understand and optimize the lead-to-sales process

  • Creation of a data lake template that can be easily rolled out to other, international markets

  • Implementation of a self-service system for the analysis of various data sources

  • Building machine learning models for predicting sales processes

  • Clarification of the legal requirements for the use and analysis of the data


  • Use of GitHub Actions for automated integration and deployment processes
  • Use of Terraform for code-driven infrastructure provisioning
  • Creation of Terraform modules for uniform infrastructure configurations
  • Data lake and data archive hosted on Amazon S3
  • Building a data warehouse with Amazon Redshift
  • Creation of dashboards for graphical representation of data based on Redshift views and tables
  • Replication of complete infrastructures for multiple markets using Terraform Fully automated provisioning using Terraform


  • Independent development of features through automated deployment pipelines
  • Versioned and well documented infrastructure through Terraform
  • Unified configurations and auditable security concepts
  • Modular, simple and scalable pipelines
  • Possibility of cross-analyses across various data sources
  • Implementation of security best practices through a structured rights and roles concept
  • Breaking down data Silos

Project Events::

Automated CICD processes

By using Terraform, the central overview of cloud resources and infrastructure was clearly defined. Another benefit of this was the improvement of developer workflows and integration in various DevOps processes. This made the project more responsive to different business requirements. The complete infrastructure could thus be made available very quickly for markets in different countries and the specific requirements of the sub-teams could be addressed in each situation.

Connection of various data sources

In the next step, all available data sources in the company were located and analyzed. This includes, for example, data from marketing, website traffic, dealer information, vehicle data, or finance data. Much of the data contains personal data and was treated separately in accordance with the GDPR and legally secured with appropriate contracts.

Structure of a Data Lake

After the use of the data has been legally clarified, each data source is extended with interfaces to AWS S3. Very sensitive data initially ends up in an area that can only be accessed by a small group of developers. From there, the data is then prepared and distributed for the various use cases. S3 serves as a central storage and long-term archive which can be accessed by various parties in the company via self-service.

Data Warehouse with Amazon Redshift

By using AWS Redshift, the customer was able to take full advantage of data solutions in the cloud. The solution scaled optimally with the customer’s requirements. Due to the very performant data warehouse, the customers were very satisfied with the speed of data provisioning. The design reduced the cost and effort required for extensive analysis of the data. Technical advantages of Redshift such as easy management of interfaces to the data lake were further reasons for using AWS RDS.

Visualizing the Customer Journey

With the help of Tableau Dashboards, the data was clearly visualized and made available to end users in a concise manner. The underlying basis for the dashboards in this case are Redshift (Spectrum) tables, which are built on the existing data warehouse model. This enables departments such as marketing to better assess the effectiveness of their campaigns and initiatives and what optimization potential there is. The entire process, from the customer’s first contact with the brand to the sale of the products and beyond, could thus be analyzed and optimized. This was previously impossible simply because of the separate data silos.

Prediction of purchase decisions

Another requirement of the customer was to make predictions about the customers’ purchasing decisions. By providing data, the team was able to use models to make predictions about customers’ future purchasing decisions. For this purpose, historical data from various source database systems about the customers were collected and used. The models and the system are continuously fed with data, further trained to make better and better predictions about the purchasing decisions of the end customers. For example, the models could be used to predict how the demand curve would change if certain price adjustments were made.


The further development of the project is ongoing. The use of various AWS data services has proven to be the right choice for this use case due to its scalability and modular design. By using Terraform and CI/CD, the release and deployment processes have been accelerated and automated. From the customer’s perspective, it is now possible to target the end customer more personally. Data projects help customers generate a 360° overview of the customer journey of different target groups. With the knowledge acquired, the customer was able to optimize and tailor its marketing. In addition, the technical set-up makes it easy and cost-effective to scale to additional markets.

Technology Stack:

Cloud infrastructure:

  • Diverse AWS Services

Data Services:

  • AWS Glue

  • AWS Redshift

  • AWS S3

  • AWS Athena


  • GitHub Actions

  • Azure DevOps

  • Terraform

Software Entwicklung:

  • Python

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Paul Niebler

GF - Management, HR                                                                          Group 8


Phillip Pham

GF - Delivery, Sales, Finance                                                                Group 8


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Head of Google Cloud                                                  Group 8

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