As our smartphones get smarter, we’re
using more Over-The-Top applications like WhatsApp, Viber or Skype, rather than
our telco provider’s voice, and text. This downward trend means shrinking
income for the telco, even though it’s estimated that data usage will grow
beyond 20 exabytes per month in the coming years. When my team of social,
mobile and decision theory researchers at IBM’s lab in Dublin noticed this OTT
trend, we wanted to know, in broader terms: could we measure and predict the
quality of a customer’s experience on a telco network in real time?
Telcos need to deepen their
customer knowledge through smarter capabilities such as predictive analytics.
And we may have found the answer in
predictive analytics. Our recent ArXiv
paper, Towards Real-time Customer Experience Prediction for Telecommunication Operators, tells an interesting story of telcos’ inability to
anticipate customer problems. If telcos could predict issues in real time, they
could then serve their customers more efficiently by anticipating problems and
implementing fixes before a customer calls with an issue. The good news is that
our study found that telcos have the data necessary to help – and keep – their
So, we devised a way for a telco’s system
to understand a bad customer experience so it could examine, and anticipate
customer usage. This, in turn, would give the telco a chance to intervene in
real time, in what the system considered a potentially bad experience in an
automated way, such as a text to customers, or to send operators out to fix a
technical issue. For telcos, this predictive analysis could not only mean
improved customer satisfaction and reduced help calls, but less customer churn,
and a better Net
Promoter Score – a telco’s customer experience grade.
through a forest of data
We partnered with a major South African
telco to test our ideas on some real, but historical and anonymized, data. They
provided a data set of app usage, which we plugged into a big data architecture
of network analysis, and machine learning software. We used the IBM Now Factory network
analytics “feed probe” to collect the data and push it into Big
Insights’ machine learning
algorithms. This allowed us to generate models of contextual relationships
within the data. And then we used InfoSphere
Streams analytics platform to score such models on millions of events from
the network and make predictions of customer experience. During the pilot, our
application processed billions of records that included mobile app usage and performance,
and network location. It then evaluated and visualized how these factors
affected churn rate, customer care and Net Promoter Score.
example, we uncovered an issue with a mobile application that was linked to a
high percentage of retransmitted data, which resulted in many customer care
calls during the monitored days, as well as for two subsequent weeks. If the
telco had deployed our predictive solution in real time, it would have been
able to identify the issue well in advance, and proactively communicate it to
the customers, thus reducing the number of customer care calls.
The system discovered the issue by training what we
call a Restrictive Random Forest model. It observed customer transactions in
the telco’s data feed. It then generated an ensemble of decision trees that
labeled each daily experience as either positive or negative, based on if a
help call was made. This approach could allow the system to help prepare call
center agents to manage a problem before a call, better manage a call that does
come through, or tell technicians of a (potential) mechanical failure – before
anyone experiences an outage.
My team, and our system, will continue to
study the individual aspects of user behavior and the reasons for their calls.
Our goal is to build personalized models for segments of users, an approach we
expect to more-accurately predict the user’s mobile experience. So, maybe in
the future, customers will make a call on their telco’s network for something
other than reporting a complaint.
more, read: Towards Real-time Customer Experience Prediction for Telecommunication Operators, by Ernesto
Diaz-Aviles, Fabio Pinelli, Karol Lynch, Zubair Nabi, Yiannis Gkoufas, Eric Bouillet, Francesco
Calabrese, Eoin Coughlan, Peter Holland,
Labels: big data, ibm research - Ireland, machine learning, telco