The
telecommunications industry is a particularly competitive one,
hallmarked by rapid technological advances and changing customer
demand. Even 10 years ago, many of us evaluated our
telecommunications providers in terms of the cost of cell phone
minutes and network call coverage. In just a decade these priorities
shifted — now we increasingly expect high-speed video,
unlimited data and the latest devices.

Telecoms
must meet and exceed these demands for the sake of customer
retention, let alone growth. A company’s approach to data analytics
can play a crucial role in how well it’s able to build its customer
base, optimize its offerings and remain relevant in a changing
market.

Here are
four potential data analytics use cases for telecoms.

Reducing
Customer Churn

Customer
churn is downright costly. According to data
from Bell Canada Enterprises and TELUS, it costs nearly 50 times less
to retain an existing customer than it does to acquire a brand new
one. As Computer Weekly notes about these findings, it would then
seem practical for a telecom to focus primarily on giving current
customers a reason to stay, rather than focusing primarily on drawing
new ones away from the competition.

Thus, it
pays to ask what constitutes a positive experience for telecoms
customers today? Data from customer feedback and subscriber behavior
can inform targeted actions to reduce churn by alleviating customer
pain points and prioritizing what customers believe to be their
strengths.

Optimizing
Billing for Customers

Most of us
have received at least one confusing phone or internet bill in our
lives — whether it contained opaque charges, erroneous information
or hidden “surprises” on pricing. It’s a frustrating experience
all around, one requiring time and effort from both parties to
resolve.

Forward-thinking
telecoms are trying to optimize billing for customers, making it
easier to understand usage and charges without requiring the help of
a specialist.

British
Telecom (BT) is one such innovative company that decided to embed
communication analytics from ThoughtSpot directly into customer
portals — giving subscribers the ability to ask questions and pull
insights related to pricing and usage directly. This move allowed
more than 1.2 million BT customers to analyze billing data and
determine which service plans would provide the best balance between
performance and cost.

Optimizing
billing for customer understanding eliminates a common pain point and
boosts loyalty.

Minimizing
Network Outages & Downtime

It’s no
simple task maintaining the telecoms infrastructure and equipment —
and troubleshooting it quickly when something goes wrong. But even a
few minutes or hours of downtime can be very costly for telecoms both
in terms of lost business and tarnished customer perception of the
brand.

In fact,
research firm Heavy Reading
found network outages cost mobile carriers approximately $15
billion annually,
or 1.5 percent of their annual revenue on average.

Mining
data helps companies understand the underlying reasons for downtime
and predict incidents before they occur. AI-powered analytics
can detect patterns, anomalies and relationships buried deep within
data, then push these findings to human decision-makers who can take
action.

Refine
Product Offerings & Pricing

With access
to self-service data analytics, teams can quickly pull insights
related to product performance and profitability — breaking
down this information by factors like location, customer segment,
timeframe, marketing campaign and many more. This ability to refine
offerings helps telecoms stay relevant in a changing market, appeal
to potential customers and satisfy existing subscribers.

Data
analytics has many potential use cases in the telecoms industry,
including but not limited to cutting down customer churn, optimizing
the billing process, minimizing network downtime and continually
refining the array of products and plans offered to customers.