Today, one of my friend who works
as an Analyst for a major telecommunication company asked me, how Big Data can help
create value for their company. I thought this makes a good topic to make an
entry in my blog.
Majoring in telecommunication systems,
I have a fair idea about the problems and limitations of telecom companies. The
mobile customer base across Europe and North American are already saturated. India
and China once fastest growing markets for new customers are also in the stage
of maturing. The fierce competition between various telecom players has forced
the tariff rates to remain as low as possible. With no avenues to increase the
tariff rates or customer base, compounded with the increasing operating cost
has already taken a toll on the balance sheets of the telecom companies across
the globe. The future doesn’t look bright for this sector.
Other than the financial owes the
other common factor uniting these companies are the user and caller logs – tons
and tons of it. Majority of these companies store these unstructured
data and most do nothing with it.
So how can big data
help process these huge unused logs to create value and new source of revenue
for the telecommunication industry?
Big Data helps you achieve it in
the following areas:
- Targeted Advertising
- Location Based Advertising
- Infrastructure Planning
- Customer Service Center Optimization
Know your Customer:
For adding value to the any
industry, it’s important to know about the customers it is serving. Big Data
helps us understand more about who the customer really is, what are his likes
and dislikes, his behavior pattern and recommends/predicts products and services
which suits his needs better on real time.
From the given data in hand we can see how big data can help
Telecom Company to understand its customer. The caller logs generally contain
information given in the table below. It’s usually who called whom, at what
time, the duration of the call and from which location (usually the mobile tower
number or GPS coordinates)
Caller Number
|
Caller Location
|
Receiver Number
|
Receiver Location
|
Time of Call
|
Duration of Call
|
The customer database has the
information about the caller such as the age, sex, profession, income etc.
With just these two datasets and applying the right data science techniques it’s
possible to extract the detailed customers profile of each of the caller.
If you know who your customers are
and what they want, the possibilities are limitless.
Targeted Advertising
Mobile based advertisements can
be better targeted to the right person at the right time if we know the
customer need and preference
Let’s consider this example:
Peter, a 20 year old
guy calls Dominos pizza and orders a pizza for home delivery through his mobile
phone on a Sunday afternoon.
The caller logs will have this
data. Since the Dominos pizza telephone number is publicly available, we know
whom he called and at what time the call was placed.
From this single log we can
deduce the following information about the customer.
o
Male Customer aged 20
o
Likes pizza
o
Lives in Area X
o
Prefers pizza on a Sunday afternoon
o
Prefers Dominos Pizza
Consider a million such call logs
of all consumers who ordered for pizzas through various outlets across the city
which can be processed with the power of Big Data. The customers can effectively
be found, categorized, grouped and profiled based on their age, locality, likes
and preferred brands. This data can be used with advertisers to market their
product in a targeted manner.
Now, consider if Dominos Pizza in
Area X wants to run an SMS advertisement campaign through the telecom provider to
advertise a new pizza it is introducing in that month or a promotional discount.
The SMS/Mobile App based advertisement campaign is bound
to be successful
·
If it is viewed by the 20 year olds than the 60
year olds.
·
If it is viewed by the consumers who are in Area
X
·
If it is viewed by the consumers who likes pizza
·
If it is viewed by the consumers who likes Dominions
pizza
·
If it is viewed by the consumers who like pizza
in their preferred day and time.
Similarly its possible to
categorize users based on their financial needs, shopping needs, travel needs
and recommend products and services just in time. This has already been done by
social media companies like Facebook and Google to track the users online
digital usage and do targeted advertisement. Effective big data integration
into the telecom industry can replicate the success of social media in telecom
too.
Real-time Location Based
Advertising
Telecom providers are better
connected to their customers than any one else at the moment. Even social media
cant match the access to their customers like the telecommunication companies.
With the fixed mobile towers the telecom provider can effectively know their
customers by location and target advertisements based on their current
location.
Lets consider the same Dominos
pizza example:
A user who likes pizza is
travelling in his car near location X. The Dominios pizza in location X will
have better chance of the user visiting the restaurant if the advertisement
reaches him when he is near by the location.
But since the user is on the move
it becomes important the message reaches him on time. Unlike the previous case
when profiling is done in bulk and messages are sent in bulk but in a targeted
manner, in real time location based advertising timing is critical. The
information has to be fed to the system and decision has to taken and acted on at
real time.
Real time big data solution can
help us realize such a system in a fast, scalable, fault tolerant and
really smart way. The ecosystems of Hadoop and Storm give us the perfect tools to
build such a System.
Infrastructure
Planning
Big Data can help the companies
plan their infrastructure better. From the user logs, it’s easy to identify the
general usage of the network i.e. when the network load is at maximum and minimum.
The network capacity can also be determined for a particular location and during
a given time of a day.
This can help the operations to
plan their infrastructure better. With proper machine learning algorithms we
can predict network overloads and possible cases of crashes in advance. This in
turn translates a stable
infrastructure with less downtime and better customer satisfaction.
Customer Service
Center Optimization
When you know your customers, its
easy to determine the problem they might face. With the predictive data modeling
it’s possible to determine cause of problem of the customer before the customer
service executive can pick up the call.
This can help company to resolve
the issue to the earliest by connecting to the right person without much delay.
With some good analytics on the customers issue, its possible to foresee the
problem in advance and rectify them even before they occur.
Conclusion
To conclude, real time big data
systems can optimize and improve the existing process in the telecom industry
and add more value the sector. Making use of the existing data can give a huge
boost to the performance, revenue and open more avenues for new business.
More to continue..
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