NEOS SNA for Telco

Telecom companies today face the challenge of ever-increasing amounts of business and regulatory requirements. These challenges make goals such as operation optimization, cost control and revenue elevation a top priority for the management, requiring deep business knowledge that depends on new techniques of data acquisition and analysis. So far, telecom companies have for the most part relied on relational technologies coupled with business intelligence tools to handle this growing data and analytics burden.

However, in turbulent environments where data is not only abundant, but is also gathered by businesses at a great speed, new challenges have arisen for companies.

Companies that are able to gain insight from both structured and unstructured data have a strategic competitive edge over their competition. Companies are now extending their solutions and services with Social Network Analysis (SNA), leveraging Big Data technologies to deliver insight in context, on demand and during the interaction. In addition to the insight provided, the key competitive advantage of this service is that management is able to make faster and more accurate judgments in its decision-making process.

SNA: New tools for extracting smart data and deep customer insight

Telco providers are well aware of the social impact in economy, including relationships among our subscriber base. Recent research shows that as much as 50 to 90% of our decisions are made under social influence (for example, a group of student friends are twice more likely to have the same telco provider than a group of randomly chosen students).

As a result of the exponential growth of various digital services, highly popular social media networks, and a plethora of applications and devices, many symptoms of poor customer experience are often too subtle for traditional standard network tools to detect. That leaves both customers and operators frustrated.

At NEOS, we believe that the ethical application of Social Network Analysis techniques can help telecom companies to better manage their businesses and to provide more value to customers as well as employees and shareholders.

What is Social Network Analysis?

Social Network Aanalysis is the study of social relations among a set of actors (in our case – telco customers).

In the traditional approach to analysis, each individual subject is described by a set of attributes, such as age, sex, income or location. Individuals are then grouped and placed in context according to those attributes, and analytic conclusions (behavior etc.) are drawn from such contexts.

SNA analysis techniques are different, as they focus on relations between actors (i.e. customers) and their attributes, describing the connections between them:

  • Kinship: brother, father, etc.;
  • Social Roles: employer, friend, etc. ;
  • Affective: likes, respects, hates, etc.;
  • Actions: talks to, meets with, etc.;
  • Flows: number of people moving by/between;
  • Distance: number of kilometers, etc.;
  • Co-occurrence: lives with, is in the same club as, has the same phone model as, etc.;
    • Mathematical: is two links distant from, etc.

In social networks, connection is power.

Why consider the SNA approach?

  • Customers are skeptical: if you want to sell them your products, convince their friends.
  • If you want to sell more products to your customers, go viral (target the “right” customers).
  • Use Social Network Analysis to understand more about your customers and their communities.
  • Enhance existing reports, modeling tools, and methodologies with social metrics.

Furthermore, traditional marketing practices are becoming obsolete:

  • Test and control group methodologies no longer work as intended.
  • Information exchange between individuals on online social networks is extremely high.
  • It is difficult to keep control groups “pure”.
  • Behavior needs to be understood across and within communities rather than just focusing on individuals.
  • It is necessary to leverage (and protect yoursef against) high-velocity information exchange within online social networks.

Replacing the traditional approach focused on individual characteristics with SNA is constantly gaining importance. Analyzing relationships instead of individuals can help clarify the process whereby a person’s decisions are influenced by others’ opinions. This is achieved by using communication info to identify groups and segments and determine their connections, which is followed by the implementation of SNA methods and metrics to detect specific roles. One example of these roles would be the influencers:

  • An influential user adopts a product or behavior.
  • An influential user informs (and influences) his or her immediate contacts within the community.
  • These immediate contacts tell their contacts, and the viral behavior spreads from there.

The SNA approach therefore offers important possibilities:

  • To identify these people.
  • To influence them.
  • To monitor their behavior.

In the telecom industry, this methodology will enable the development of new behavioral analysis possibilities and enrich existing methods of analysis, enhancing customer attributes in the CRM system. The joint usage of SNA data with traffic and billing history can be used to improve:

  • Marketing and sales strategies;
  • Campaign management;
  • Churn prevention.

In conclusion:

By tracking the customer experience in real-time, and being able to adjust network KPIs that matter the most, operators can achieve significant commercial advantages. In addition, the SNA approach enables them to identify and address network issues before the customer has even noticed a change in the quality of the service, as well as optimizing the network performance offered by the new view of their networks.

Access to in-depth data analysis is not only helpful for customer service continuity; it reveals extremely valuable insights that can become a platform for focused innovation and service differentiation.

To sum up, with the SNA approach, your company can easily:

  • Move from ‘doing’ customer centricity to ‘being’ customer centric.
  • Become truly customer focused on a sustainable basis.
  • Orientate the business towards a great customer experience.
  • Track the real-time experience of the most valuable customer segments and adjust the network KPIs according to them.
  • Meet the ongoing challenge to remain relevant to customers.
  • Have long-term relevance, which implies a continuous pursuit of differentiation: providing something of unique value to customers.
  • Identify and address network issues before the customer has even noticed a change in the quality of their service.

 

arhitecture

NEOS SNA for Telco v1.0 works on and supports following technology components:

  • Oracle Endeca Information Discovery 3
  • Oracle Business Intelligence Enterprise Edition 11g
  • Oracle R Enterprise 1.4
  • Oracle Data Integrator 12c R1
  • Oracle Database 12c R1
  • Oracle Linux 6
  • Oracle Virtual Machine
  • Oracle Exadata Database Machine
  • Oracle Big Data Appliance
  • Oracle Virtual Compute Appliance
  • Oracle Database Appliance
  • Oracle Exalytics In-Memory Machine

Big Data analytics market in 2015

In December 2014, IDC predicted that Big Data and the analytics market would reach $125 billion worldwide in 2015. In addition, according to a survey conducted by Accenture and General Electric in October 2014, companies are well aware of the impact of Big Data. As many as 73 percent of companies are already investing more than 20 percent of their overall technology budget on Big Data analytics, and more than 2 in 10 are investing more than 30 percent. Moreover, three-fourths of executives expect this spending level to increase in the next year. Across the industries surveyed, 80 to 90 percent of companies indicated that Big Data analytics is either the top priority, or in the top three for the company. In addition, 87 percent of enterprises believe that Big Data analytics will redefine the competitive landscape of their industries within the next three years, while 89 percent believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum.