The client services department at Orange has a data science team which, until two years ago, was performing mostly ad-hoc analysis for the business and had limited ability to work on more complex machine learning (ML)-based projects. In order to scale out the team and expand their scope, they had to overcome two key challenges:
- Tooling: Only those who knew the team’s tool and its proprietary language could work with data, which limited the use of data to statisticians or data scientists. Even then, the data was difficult to access,making projects difficult to get off the ground. At the same time, the tool was geared toward BI and wasn’t capable of supporting ML-based data projects.
- Hiring: The data team at Orange was struggling to hire talented data scientists fresh out of university and with lots of ambition as well as creative ideas (traits they were seeking to enliven their data science practice). Unfortunately, this was largely a function of the tooling challenge, as young data scientists were largely looking for jobs where they could work with open-source tools (such as Python or R). Anyone they did bring on board had to learn the legacy tool and took months to get up to speed and start being productive
How Orange Sparked Change With Dataiku
Change at Orange came from the bottom-up and began with the client services data team wanting to work on more cutting-edge ML projects. In order to spend more time on ML, the team realized they needed to empower people (like analysts) to work on their own simple data analysis projects, freeing up the data team’s time for more advanced data science work with potentially bigger impact.
By enabling analysts and business people to work on data analysis themselves, data practices are more infused throughout the client services organization and not siloed to just one team. Today, there are more than 100 analysts and other business users across Orange who are empowered — with Dataiku — to work with data.
Ultimately, Orange chose Dataiku to facilitate change on the technology side. Dataiku allowed the data team to work on ML projects, plus it facilitated getting new data scientists up to speed quickly — working in cutting-edge tools they wanted to use.
At the same time, Dataiku allowed analysts to work independently on their side, while also not alienating veteran employees with lots of valuable experience but who didn’t necessarily want to learn a new tool or system.
Accelerating ML Use Cases at Orange
Once the team was armed with Dataiku, they were able to start transitioning smaller BI projects to the business and work on ML use cases.
In addition, analysts and business people are more empowered than ever thanks to Dataiku. Key performance indicator (KPI) dashboards that previously took up to a month or more to build and update now take one week, maximum. The success in use cases on the ML side has allowed the team to continue to grow, going from six data scientists three years ago to more than 25 people both on the data science/analytics and on the data tooling side.
A few examples of how the team has been able to accelerate on the ML side include:
Call Load Detection & Triage
When a client calls Orange customer service, all agents might be busy with other callers. So the team set out to create a ML-powered system that anticipates this volume and offers to call the customer back at a certain (less busy) time while still respecting the business’ service-level agreements (SLAs).
In addition to call center data, the model also takes into account the reason for the customer’s call to be able to more accurately determine priority as well as how long the call might take. Note that there are more than 400 skills in which a particular agent may be specialized, so this was a non-trivial task. Overall, the model took less than a month for the team to build using Dataiku.
Preventing Unwanted Charges
The client services team’s main goal is to improve customer net promoter score (NPS), which means increasing overall satisfaction. One of the biggest sources of frustration for clients can be excessive charges, so the data team wanted to address this problem with ML (specifically clustering) by looking at different variables that might point to clients incurring charges — e.g., overseas charges or going over call/data volume. Dataiku allows the team to perform clustering quickly to understand what certain portions of the client population have in common that the business side might want to take action on.