Big Data In Telecom

Telecom companies are sitting on a gold mine, as they have plenty of data. But what they require is a proper digging and analysis of both structured and unstructured data to get deeper insights into customer behaviour, their service usage patterns, preferences, and interests real-time.
Bigdata offers telecom operators opportunity to have a more complete picture of their operations and their customers, and to further their innovation efforts. The operators that can incorporate new agile strategies into their organizational DNA fastest will gain a real competitive advantage over their slower rivals.

Bigdata Experts at Sentienz understand the game of scale, telcos easily hit 30 to 100 petabyte storage. Also we respect the existing IT Infrastructure and allow smooth adoption. Our platform enables you to fasten the road to advanced analytics.

Bigdata the “Opportunity”

Big data promises to promote growth and increase efficiency and profitability across the entire telecom value chain. Big data can even open up new sources of revenue, such as selling insights about customers to third parties.
Many existing business capabilities can be enhanced when more and varied data becomes readily available for analysis, expanding the scope of opportunities and the breadth of optimizations. Big data offers benefits across the entire telecom value chain.

Bigdata “What” Data

The eventual goal of big data is to combine and correlate every information source to generate a holistic, transparent, end-to-end view of all the interactions every individual customer or household has with the operator. But to really leverage big data, operators must radically modify how they gather, verify, learn from, and make use of the information at their disposal. That means completely rethinking the purpose of the traditional corporate pilot program, long dependent on uncovering incremental opportunities by setting rigid, predetermined goals and hoping to attain them through laborious and time-consuming stage-gate and approval processes. Instead, operators must learn from companies such as Google and Facebook, where data is king and virtually every product decision flows from what the available data says about customers and how it can be used.

Bigdata Telecom Usecase

Proactive Network Upgrades Management and Maintenance

Timely upgrades of infrastructure and services are key to sustainability and growth in today arena, it is only possible with correlating the vast amounts of diverse data using bigdata technologies, which is otherwise not feasible with conventional ways.
Running regular reporting on the network infrastructure data can give subtle hints on those nodes that are responsible for the majority of the negative customer experience, and could therefore be prioritized for upgrades. These reports also highlight the dynamics of how network congestion affects churn, and where exactly network upgrades produce the most incremental revenue.

Data Driven Capacity Planning

The consumption of services and resulting bandwidth in a particular location may be out of sync with a telco’s plans to build new towers or transmission lines in that location. This leads to a mismatch between expensive infrastructure investments and the actual revenue from those investments.
A Data driven approach could potentially optimize the rollout of 4G coverage in time and space to match the likely pick-up in service revenue, based on detailed cell tower traffic data of the last few years. With prior legacy approach, there would have been an estimated 10% more capex for the same outcome.

Dynamic Network Traffic Shaping

Certain mobile apps and user activities can hog bandwidth and erode service quality for all other customers, perhaps because they contain malware from non-trusted app stores. Network operators need to respond to such bandwidth spikes quickly to reallocate virtualized resources and maintain service level agreements (SLAs).
Bigdata technologies helps with real-time bandwidth allocation. Network operators can allocate bandwidth more nimbly and manage risk to their SLAs. They can also investigate issues more quickly and take action such as eliminating devices that degrade the customer experience.

360 Degree View of Customer Value

Telcos and cable companies interact with customers across many channels and points in time, which land up in silos, making it very difficult to correlate data about customer purchases, marketing campaign results, and online browsing behaviour. Problem is exacerbated by recent acquisitions and a proliferation in the volume and type of customer data.
Merging that data in a relational database structure is slow, expensive and technically difficult.

Unlock an enterprise-wide data lake of several Petabytes with our Data Platform and get seamless access to 360-degree unified view of the customer life time value based on usages patterns across time, products and channels.

Personalized Marketing Campaigns

Marketers have long sought ways to tailor their marketing campaigns to the needs of each individual customer. Telcos are uniquely positioned to deliver on that goal because mobile phones not only follow their owners everywhere, but also reveal a lot about their owners’ interests through browsing behaviour and the applications present on the phone.
Example case “Telesales revenue increase by 50% by tracking competitors web-sites visited and counter offers to products searched +20% conversion rate increase by optimizing and personalizing the path-to-transaction $1.65 ARPU increase for 1 million customers boosts topline by $20 million per year”.



Customer Experience Analysis

Due to the cost of existing solutions, the data expires after 60 days, CDRs need to be analyzed and archived for compliance, billing and congestion monitoring. Example: forensics on dropped calls and poor sound quality.
High volume makes pattern recognition and root cause analysis difficult. Often those need to happen in real-time, with a customer waiting for answers.

Highly scalable platforms largely benefits telcos with cost advantage (storage 20x cheaper than enterprise-grade storage) and better insights (Better analysis to continuously improve call quality, customer satisfaction and servicing margins).

Up-selling and Cross-selling & Next Product to Buy

Telcos are well equipped with all possible approaches to upsell smart phones into user bases that are still largely on legacy feature phones. Operators can now convert many hundred thousand feature phone users to smart phones with associated data plans.
With access to all of the users data, Operators can now offer personalized products and plans with a higher chances of adoption rates.
As telco product portfolios grow more complex, there are ever more opportunities to sell additional services to the same customer base. With Confident NPTB
recommendations, based on data from all its customers, empower sales associates and improve their interactions with customers pre-transaction.

Improved Customer Service Productivity

Telcos today are struggling with a combination of high costs but low customer satisfaction related to customer care. Primary root cause being, contact centre agents do not have granular insights into a particular customer’s historic data, hence are unable to provide effective call resolution.
one operator detected that 25% of callers were contacting the call centre merely to have their late fees on the monthly bill waived. Bigdata platforms provides telcos with the ability to off-load these cases to online self-service and interactive voice recognition, and frees up the agents to focus on more valuable customer interactions and focus on issue resolution.


Field Service Productivity

With traditional legacy system there are all chances of misdiagnosis of customer issues due to lack of data with contact centre agents peculating down to insufficient ways of diagnosing what the actual issue faced by customers, Cases like “Agents lacked the data to triage network vs. home-based problems accurately enough.
Therefore, technicians were dispatched to the customer premises for problems that reside within the network”.

With pin-point analysis and reporting of issues with bigdata tools, there is a huge reduction in the number of “false positive” truck rolls, saving operators in terms of millions.

Data Protection and Compliance

Telcos are obliged to protect the organization's data, which is growing at an exponential rate from misuse by any of its employees. Security requirements demand that unauthorized employees not have access to, or the ability to tamper with consumers’ billing information, call records or texting history. Data volumes from various different sources pose a major challenge in storing and processing this mammoth of information.
Misdeeds older than a given window cannot be investigated, requests for information are delayed due to conventional retrieval and New regulations mandated the retention of certain data sets for multiple years.
With the highly scalable storage, analytics and reporting capability Operators can now answer all sorts of question in relation to data breaches, Detect and Prevent breaches in real-time, support regulatory mandates and legal compliances.

Real-Time Fraud and Anomaly detection and Prevention

Fraud detection systems depend on data mining algorithms to identify and alert the telco to fraudulent customers and suspicious behaviour .While data mining techniques help only in the areas of subscription fraud, it is useful to remember that there can be several methods of fraud, requiring other analytic models to aid detection.
Bigdata allows correlation of internal location, usage, and account data with external sources such as credit reports, operators could significantly increase the detection of fraudulent activity such as call forwarding on hacked PBXs, swapping of SIM cards, and improve the overall accuracy and efficiency of their efforts to recognize patterns of fraudulent behaviour.




End User Device Security

Implementing security solutions such as Digital Rights Management , Information Leakage Prevention for enforcing security at the end user level becomes great challenge as mobile phones evolve into personal data hubs, end users are facing privacy and security dangers that are escalating and multiplying, as threats converge from a range of environments, including SMS, cloud, Web 2.0 and malware threats from non-trusted applications. As a result, customers are now as concerned about data integrity as call quality.

A smart data platform equipped with retrospective tools can in real-time mitigate and ensure end user privacy, security and data integrity which becomes paramount of customer experience with the operator.

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