Success Stories

Discover how our customers are using Dataiku DSS to build their own competitive advantages.

Data Science & Quantum Computing United - the Future of Data Science & Finance

Data science is revolutionizing financial institutions. The emergence of machine learning as the dominant approach to analyzing data represents a rapid enhancement in both capability and in the skills base and management structures required to implement competitive solutions. Many firms are seeking external help to identify and exploit new opportunities.
The sensitivity of the data, the regulatory requirements, the sheer volume of high-resolution information, and the criticality of the systems make this area among the most challenging in which to operate. QxBranch prides itself in delivering state-of-the-art solutions that navigate this environment seamlessly

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Dataiku for Data-Driven Improvement of Physician Performance and Patient Care

By adopting Dataiku, our customer's Quality Management team developped a productivity analysis application. With this application, the team has been able to implement best practices amongst the hospital’s physicians, improving patient care while avoiding superfluous costs:

  • hospitalization average length is now below national standards;
  • unnecessary drug prescriptions have significantly decreased, resulting in savings that amount to an estimated $1.3 million per year;
  • once reticent, physicians are now involved and frequently try to modify their personal practice choices based on this data analysis to increase care and cost.

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How AramisAuto Uses Dataiku for In-House Data Product Deployment and Team Scalability

The implementation and active support of Dataiku has enabled AamisAuto to fully leverage the capability and power of predictive analytics. Data analytics projects are now fully developed in-house. Thanks to team autonomy, the company has realized key real-world analysis goals including but not limited to:

  • Deployment of a high ROI advanced analytics project, from scratch, in less than 3 months;
  • Comprehensive training of their data scientists and BI engineers;
  • Ability to scale their data analysis projects to a wide array of business use cases;
  • Growth of their data team to a wider range of expertise levels and training backgrounds.
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How BlaBlaCar Uses Dataiku for BI Self Service Analytics

With Dataiku, Blablacar's BI teams around the world easily collect & use performance indicators on demand. This has resulted in a few significant improvements including but not limited to:

  • Huge increase in BI productivity,
  • Decrease in delivery time,
  • Quick and accurate BI reports translating into optimized conversion rates and improved retention of users and customers.

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How Chronopost Uses to Dataiku to Optimize Production Costs & Develop New Commercial Offers

By adopting Dataiku as a key tool in their Big Data and avanced analytics initiatives, Chronopost has successfully:

  • Increased BI productivity: BI teams around the world easily collect & use performance indicators on demand,
  • Optimized operational means and costs involved in package delivery,
  • Created new commercial offers at optimized production costs,

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How Coyote Uses Dataiku to Improve Core Product Accuracy and Efficiency with IoT

With Dataiku, Coyote has been able to improve their core product’s accuracy and efficiency, while establishing a data-driven spirit within the entire company. The entire process has resulted in several competitive advantages such as:

  • Speed limit reliability increased by 9% on analyzed datasets;
  • Automation of the speed limit correction process;
  • Increased customer loyalty.

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How Coyote Uses Dataiku to Optimize Marketing Campaigns

Thanks to this predictive behavioral analysis in Dataiku, Coyote optimizes marketing and sales campaigns based on its customer profiles. This application results in several advantages:

  • Increase the performance of outbound call campaigns by + 11% efficiency,
  • Adapt marketing campaigns thanks to increased knowledge of the actual uses of the service,
  • Significantly improve data management.

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How Dataiku has Enabled a Hospital to Significantly Decrease Staffing Costs and Turnover

Thanks to Dataiku, our customer has been able to keep individual schedules optimized to ensure better patient care and to improve staffing productivity:

  • the Dataiku run predictive analytics models are 47% more accurate than historical average predictions;
  • 11% decrease in staffing costs, saving around $730k per year;
  • an estimated 9% decrease in staffing turnover in year 1 since application deployment.

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How L'Oreal's HR Uses Dataiku to Optimize Cross Disciplinary Knowledge Transfer & Optimize Productivity

With Dataiku, L'Oreal has successfully built and deployed a visual application that enables the Operations HR team to identify three types of thought leaders amongst their employees:

  • The "global brokers": those who participate in knowledge transfer on a global level (not specific to one topic or to one group)
  • The "group brokers": those who lead knowledge transfer on a local level (within a group)
  • The "dealers": those who "move" conversations from one focus to the next
  • Thanks to this data analysis, internal discussion tools are increasingly used as real knowledge transfer tool that allow teams worldwide to freely share their business know-how for better productivity.

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How PagesJaunes Automatically Targets & Fixes False Query Results to Improve Customer Satisfaction

With Dataiku, PagesJaunes now has the capacity to explore and correlate heterogeneous and multiple data sources to deduce rules and models that can create value. PagesJaunes has developed models that are continuously improving customer satisfaction all the while boosting their Category Manager’s productivity by:

  • Closely monitoring and managing unsuccessful searches,
  • Automatically detecting the most critical signals & applying the most relevant rules
  • when interpreting a query,
  • Targeting and fixing false query results.
Since the project started, more than ten PagesJaunes’ collaborators have been trained to Big Data technologies (Hadoop, machine learning, statistics) with DSS.

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How Parkeon Built a B2C Data-Driven Parking Prediction Application for Android and iPhone

With “Path to Park”, the only Parking Prediction Application powered with ML algorithms, Parkeon is able to propose a simple and intuitive example of a modern data product to users around the world. The application is powered predictive models that are continously learning from incoming parkmeter and geographical data. The app, which was ready for widespread use in under 6 months, predicts parking availability in each street according to parking meter data and points of interest data. Thanks to machine learning and to a hybrid architecture, as the application's use grows, so does the data it ingests and the quality of its predictions.

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How Showroomprivé has Diminished Churn Rates with Self Service Analytics

Before Showroomprivé started using Dataiku to internalize the design and deployment of their data solutions, they'd not only externalize the whole research phase involved in such projects, but they also focused mainly on descriptive analytics. Amongst other in-house solutions, Showroomprivé uses Dataiku to automatically detect, amongst mono-buyers, potential churners with an AUC of 0.819! By detecting potential churners with 77% accuracy, Showroomprivé's marketing team can now orient campaigns strategically.

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How Trainline Uses Dataiku DSS to Get a Global Understanding of Marketing Acquisition

With Dataiku, Trainline is now able to get a global understanding of marketing budgets allocation. The entire process has resulted in several competitive advantages, such as:

  • Data processes construction time going from one month to one week;
  • Data engineers multiplying their efficiency by three.

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How VoyagePrivé Uses Dataiku's Self Service Analytics to Optimize Marketing Campaigns and Increase Transaction Value

Tooled with Dataiku and made more efficient with an agile machine learning analysis methodology, Voyage Privé can now optimize their marketing & sales campaigns based on precise customer segmentation. The entire process has resulted in several competitive advantages, such as:

  • A 6% increase in the total transaction value by unit member;
  • The complete internalization of the company’s data workforce.

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Patient Scheduling Optimization (Patient No Show Predictive Analytics)

In 2015, Joel Portice, Intermedix CEO, recognises that to keep up with the rapidly growing data-centric ecosystem, his organisation had to invest in advanced analytics to enhance both solution development and data monetization. With this goal in mind, Intermedix creates a team mandated to tackle this new strategic direction. For this new team, the choice selecting an analytics platform that helped scale out advanced analytics projects  became crucial. Previously, Intermedix relied on traditional business intelligence technologies and on hand-coded predictive models using R or Python. One of the first important use cases that Intermedix’s analytics team prototyped and effectively delivered using Dataiku DSS, focused on improving their management solutions for office-based physicians, with a module to predict a patient no-show.

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