Data and AI
Transport, Trade and Logistcs
The client is a major player in logistics and has been a pioneering company since its inception. It has a footprint of operations that spans several countries.
As such, the company is no stranger to data-driven innovation and already uses a wide range of digital marketing techniques to communicate with its target market.
Fragmented Data Sources and Arduous Analyses
As a sectoral leader, the company was already collecting behavioral, attitudinal, and transactional metrics 24/7, from its own applications as well as from third parties such as Google Analytics and Adobe Analytics. With such a wide variety of sources—intelligent marketing automation tooling, websites, customer surveys, social media, online communities, and loyalty programs—as well as its own legacy systems, maximizing its data potential and developing cohesive marketing programs was a challenge. The multinational services company needed an entirely new data platform.
An Integrated Approach to Data-driven Marketing
To build a new platform and develop an integrated approach to data-driven marketing and maintain a sustainable transfer of knowledge, Xebia put together a project team comprising platform engineers, data engineers, a Scrum master, a product owner, and employees from the client organization. The team evaluated different ways of working and finally chose Agile Scrum in combination with DevOps principles from the various options available.
AWS and DevOps Principles for Bespoke Data Analysis
After helping the client organization to define its roadmap, the team built the initial MVP data platform on AWS within a month. It also designed the architecture and developed a new cloud-native platform on AWS using AWS-native services and open-source toolings, such as Hashicorp Terraform, Apache Airflow, and Jenkins. The new platform integrates multiple data sources—including on-premises databases, cloud sources and third-party providers—and can quickly add new ones to identify and analyze target customer segments. The platform facilitates data analysis on demand, replacing standard reporting. Each scientist has a personal data science stack to develop predictive models, and after each analysis, the cluster cleans up automatically, saving costs.