Trying to find an edge over your telecom competitors? At PNA, our Data Analytics services can help you find the patterns unseen to human eyes. Stop trying to find edges and start getting ahead of your competition.

1. Traversing Telecom
with Data Analytics
www.positivenaick.com
Navigating the complex landscape of the telecom
industry to discover new value streams for our
clients.

2. Need for Analytics
In this age of hyper-connectivity, the telecom industry is one of the most pervasive industries on
the planet. A vast majority of the people in the world rely on the telecom industry to communicate
with people as well as access the vast resources on the Internet.
With such a large sphere of influence, plus millions of customers paying into a company, and
hundreds of competitors all around the globe, the telecom industry has its own share of problems
as well. With so many customers and so many players, there’s no shortage of competition. To make
things even more difficult, the telecom industry is also the place where some of the fastest
technological advances take place, such as fiber optics, 4G and 5G technologies. If a telecom
provider does not keep up with the trends and install expensive new infrastructure to enable and
support these new technologies, they’ll find themselves overtaken and decimated by their other
competitors.
Using the insights derived from data analytics, telecom companies can stay ahead of the curve
when it comes to providing customers service and keeping revenue costs down. By anticipating
demand from customers and supply from technological providers, telecom providers can
effectively plan for new infrastructure upgrades at the time when those upgrades are the cheapest
as well as demanded by customers.
1
Prospecting
Support/Service
Acquistion
Cross-sell/upsell
Reporting
Retention
Payment/
collections
Order
processing
Potential area of focus in Telecom

3. 2
Next Best Offer
Making customers purchase
services higher up the ladder
helps improve revenue. Next
Best Offer would need to
dynamically upsell and
cross-sell products, packages,
and services to the customer,
by intelligently analyzing their
purchasing habits.
Business Need
With Next Best Offer, telecom
companies can provide
targeted and personalized
product recommendations to
customers. Additionally, this
can be used to improve
customer loyalty and
satisfaction, by providing and
improving services customers
use and love.
OutcomeApproach
Using a collaborative
filtering model, automatic
predictions about a
customer’s interests are
made. This is done by
collecting individual customer
preferences from customers
who have similar behavior.
Next Best Offer is a form of predictive analytics-based marketing strategy that deals with
improving customer experience while helping telecom providers close a deal. This method is used
to help companies and their marketers accurately understand customer purchase habits and
streamline marketing towards efforts that have a higher chance of ending in a successful financial
transaction.
Next
Best
Offer
Offer
Unlimited
Internet
Recommend
Movie
Tickets
Netflix
Subscription
change plan
long lasting
customer
new customer

4. 3
Affinity Analysis
Relationships between different
products are analyzed to help
two major goals; To develop
suitable marketing strategies to
cross-sell and upsell different
products to customers, and to
improve customer experience
by identifying and offering
services that are truly relevant
to individual customers.
Business Need
Once the products and services
are identified and classified into
proper buckets, they can be
incorporated into different
loyalty programmes and
discounts. This can be used to
create optimized promotional
campaigns tailored for each
customer. This has the added
advantage of of improving
customer experience and
reducing churn.
OutcomeApproach
An Association Algorithm is
used to identify services that
are often purchased together
by customers. This algorithm
is then also used to find out
the various purchases made
by the entire customer
population.
Affinity analysis is a data analysis method that finds relationships between different service
offerings, such as how if they’re sold together, and in what specific bundles different services are
sold together.
learning the affinity of each product based on the purchase patterns

5. 4
Customer Lifetime Value
Customer Lifetime Value (CLV), or the amount of monetary value a customer brings to the table
throughout the lifetime of their relationship with the telecom provider, is a number of paramount
importance to the customer.
CLV is used to make internal
revenue predictions, based on
the value from each customer.
Additionally, CLV is used to
allocate marketing budgets on
the basis of the relative value
of each customer.
Business Need
In addition to calculating the
customer’s past and current
value to the company, the
customer’s expected future
value can also be predicted.
The data can be used to
optimize how marketing
expenditure is managed.
OutcomeApproach
A survival model is used to
predict the overall lifespan of
a customer, ie, how long a
customer will continue to do
business with the telecom
provider. Using a regression
model, the customer’s
lifetime value score is
calculated.
20%
CLV1 CLV2 CLV3
60%
20%Inactive
nonprofitable
customers
Active
profitable
customers
Very active
very profitable
customers
Lifetime Value = (Average order value) * (Number of Repeat sales) * (Average Retention Time)
identifying profitable vs. non-profitable customers through clv

6. 5
Being one of the most
competitive industries in the
world, the telecom industry has
no shortage of competition.
Without proper service and
customer experience,
customers are easily motivated
to take their business to the
competition. It’s also much
more expensive to acquire new
customers compared to
retaining current customers.
Business Need
With the churn data on hand,
retention policies and
incentives can be prepared
beforehand to retain the
customer. If there are any
recent business process
changes from the company
that have prompted
customers to opt out, they can
be modified or removed
altogether.
OutcomeApproach
Using a survival analysis
model, we can find out when
a particular customer might
retire their relationship with
the company. Additionally,
using data like billing records,
customer service records,
usage patterns, we can
identify the probability of
churn for each customer.
Customer Churn
The challenges with churn prediction and identification lies with identifying which customer
behaviors trigger churn and to predict the attrition of customers. Once the reasons for customer
churn are identified, steps can be taken to address issues.
Age < 60
Usual call
duration< 2 Min Placed Calls > 10
Churner Non Churner
no
no
noyes
yes
yes
Non Churner Churner
a simple churn rule for older customer segment

7. 6
With profit-based customer
segmentation, we can identify
loyal customers and provide
appropriate offers to maintain
their loyalty to the company.
We can also identify high-value
customers who aren’t loyal, but
have the potential to be
high-value to the company.
Business Need
The biggest outcome expected
here is a direct increase in CLV
(Customer Lifetime Value) for
the telecom company.
Additionally, rewards and
loyalty programmes can be
rolled out to loyal customers
to ensure they are
incentivized to stay with the
telecom provider.
OutcomeApproach
Using segmentation
techniques, customers are
classified into different
segments based on various
factors, such as purchase
behavior, media consumption
habits, etc. With this
segmentation, customers
from high-value segments can
be identified to help improve
revenue targets, improve CLV
of existing customers, and
finally, try new methods to
find customers.
Profit-based
Customer Segmentation
Different customers have different purchasing habits. Customer segmentation classifies customers
based on how much value they bring to the company. Some customers are highly profitable and
they should be rewarded loyalty incentives, as well as developing new marketing strategies to point
them towards products they’d also be interested in.
Email address
Age group
Attributes for segmentation
Purchasing history
Profile infomation
Internet usage
Usage patterns
Calling type
Payment history
18-24
65%
77%77%
73%
72%
78%
71%
59%
26-34
85%
72%72%
70%
75%
74%
68%
70%
35-44
87%
73%
73%
73%
63%
73%
73%
45-54
60%60%
60%
63%
63%
57%
62%
64%
55-64
65%
51%
66%
63%
44%
50%
69%
65+
53%
68%
63%
43%
45%
53%
43%

8. 7
Adopting Analytics
into Telecom
There are four major steps to be followed when adopting analytics into a telecom provider’s
company.
• Identifying Internal Use Cases
• Measuring Analytics
• Finding Required Talent
• Technical Requirements
Some companies already have certain
resources and skills in hand to open up an
internal data analytics team. Other companies
might need to build one from scratch. Some others
are better suited to using a third party data analytics
firm, like PNA, to get their work done. Different
companies have different requirements, and without
the necessary research beforehand, companies can
run the risk of choosing an option that isn’t suited to
their needs, causing further inefficiency.
Finding Required Talent
When it comes to telecom, there is already
a vast array of implements in place to
collect and organize data. But in this
phase, we can find out what other technical
requirements are needed, considering the particular
goals the telecom provider has. This often varies
depending on the data the company already has, and
the infrastructure they already have in place to collect,
organize, and integrate that data into their processes.
Technical Requirements
Before we can implement data analytics, there needs
to be an identifiable use case within the company.
These are issues and problems that are clearly defined
and data analytics can offer measurable results in.
Once we identify these use cases, we have the
foundation with which we can build analytics solutions
tailored to each company’s needs.
Identifying Internal Use Cases
The next step is to measure the impact of those
changes. This means having a conversation with all the
stakeholders and asking a few key questions.
What are our performance goals after deploying the
solution? How are these goals going to be measured?
Does our organization have the tools necessary to
measure them?
This will provide valuable insight into finding out
whether or not the deployed solution is performing as
intended or if it has any unintended consequences.
Measuring Analytics

9. We hope this gave you better insight into how Data Analytics can help your company reach new
business goals. If you have any questions, please contact us using the details below.
Thank You
PositiveNaick Analytics Ltd.
No177,1st floor,LM Tech Park,
1st Main Rd, Nehru Nagar, Kottivakkam,
Chennai, Tamil Nadu 600041.
Email: customercare@positivenaick.com
Website: www.positivenaick.com
Phone: +91-44 4857 6162