DATA: THE NEW OIL
Turning data into information
Data is the new oil. The comparison is accurate, because just like oil, data is nothing more than a kind of raw material. By analyzing data, Proximus succeeds in achieving greater efficiency in various areas. The customer enjoys that.
By definition, a telecom operator has a lot of data at its disposal. This involves data about customers, how they use the network, the questions they ask the call center, the interventions carried out by technical teams, the operation and consumption of the cooling installations in the data centers and so on. But data in itself does not constitute useful information. This is where Proximus' Data Science Center of Excellence comes into action. The team collects, processes and analyzes data in order to compile insights, predictions and action points.
“Our field of work is very broad,” says Stephanie Cox. “We analyze data around the energy consumption of our buildings, but we are also active in the analysis of text and photographs.” Much of the team's focus is on the analysis of network data. “We receive data over the network through more than five hundred different technical measurements,” Stephanie explains. “For example, we measure voltage and resistance on the copper lines, but we also receive sensor data from the street cabinets and log files from modems and decoders.” Using this data, Proximus is able to detect technical problems in the network in real time and even predict technical problems proactively. “At the same time, we learn a lot. The intention is always to gain new insights with our analysis and then to use those insights to improve the customer experience or make our processes more efficient. If we know the cause of a problem, we can better predict where and when an intervention will be needed in the future.” Moreover, the insights gained from the analysis of the network data allow Proximus to create a very targeted list of investment priorities. “From our analyses, we know, for example, the locations with the greatest need for fiber.”
STEPHANIE COX studied commercial engineering and specialized in mathematical methods. She started her career at IBM. Stephanie switched to Proximus and built a Data Science Center of Excellence with thirty employees.
At the same time, it is not only Proximus that benefits from the results of the data analysis. “For example, we developed a project to streamline customer interventions,” Stephanie explains. When a customer encounters a network problem, they call the call center. If the request cannot be solved remotely, Proximus will send a general technician to the site. In practice, this technician sometimes had to call in a specialist, for example a welder. In this way, one customer request sometimes required several interventions. “We have created a machine learning model that allows us to predict in advance whether a welder will be needed,” Stephanie continues. “The input for that decision comes from the data we collect over the network.” Sometimes it turns out that a new modem solves the customer's problem.
Better customer experience
“If a technician is needed, the model predicts the location of the intervention: in the street cabinets or at the customer's premises. They do not have to stay home or take time off.” At the same time, Proximus measures the customer experience to gain insight into what customers experience as positive or negative. “That helps us to adjust the offer and the service,” Stephanie concludes. “When a customer calls the call center with a specific problem, we can forward it to the employee with the best chance of finding a solution.” The advantage for Proximus is that this means fewer unnecessary interventions, and therefore it cuts costs.
“WITH OUR DATA ANALYSIS WE GAIN NEW INSIGHTS, WHICH WE CONVERT INTO CONCRETE ACTIONS TO IMPROVE THE CUSTOMER EXPERIENCE AND MAKE OUR PROCESSES MORE EFFICIENT.”