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The following Big Data Telecom industry use cases focus on improving the customer experience, reducing costs, and improving sales:
The following IBM Streams and TEDA use cases focus on improving the customers experience, reducing costs, and improving sales:
Most telecom companies have similar big data scenarios and problems with high volumes of data from a variety of sources that need to be analyzed in real-time. For example, telecommunications providers are analyzing a staggering 7 billion call data records (CDRs) per day. In this article, we present several big data analytics use cases in the Telecom industry that illustrate the complexity of this industry’s big data processing needs and Vates’ proven capabilities.
TEDA and streams provide the structure and processing capabilities to process terabytes of data to improve user experiences, diagnose and, improve networks, increase call center productivity, detect fraudulent activity, and drive marketing campaigns adjusted on the fly. Vates system integrators, engineers, and project managers using iterative agile processes to reduce risks and go-to-market timelines work to unify data sources, produce meaningful near real-time analytics, and scale with increasing network traffic.
The following Telecom industry big data analytics use cases cover implemented solutions for one of the largest telco providers in Latin America, a subsidiary of a Mexican telecom group, headquartered in Brazil. The Telecom company serves clients in Argentina, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, and Uruguay.
The Telecom company needed to exponentially improve the speed of processing its data to be able to show its customers critical information in real-time. By meeting this information need for their customers in real-time, the Telecom could improve the overall customer experience and stimulate demand to increase its customer base by improving their marketing campaigns using the same data.
The Telecom knew what it wanted to achieve, but did not know how to solve the system engineering and related architectural and processing problems. The business cases presented below illustrate how Vates and the Telecom’s technical and business teams utilized a concept known as TEDA or Telecommunications Event Data Analytics to solve the daunting big data analytics challenges.
The project required these to work together:
Vates focused on two main types of challenges:
Project “Prepaid customer locations”:
Problem: Customers prepay mobile phone service. The Telecom company needed to know in real-time where and how prepayments were made nationwide. This information had a 48-hour delay. Having this information sooner would help it understand prepaid mobile phone customers better including their preferences when it came to making payments.
Outcome: Data is now available in real-time making it possible to determine the actions required to rectify network connection problems. And it now has more insights about their customers payment preferences.
Customers have the option of paying for the cell phone service from
The main data source contained the customers’ “credits” based on the prepayment made, but was missing where and how the payment was made. The goal of the big data project with Vates was to determine where the customer prepaid for the cell phone service and if the total online payments exceeded the total of payments made at physical locations.
The new information was for use by the marketing team to better understand the buyer and how to market to them better.
Technology: To solve the Telecom company’s problem, Vates recommended using the following technologies and architectures.
Project “Online Benefits”:
Problem: The Telecom’s marketing department creates and implements marketing campaigns for specific groups of customers within the “Prepaid Cell Phone” service. These campaigns are communicated via SMS messages and are designed to give the customer incentives to buy more prepaid credits. For example: “Buy 10 credits within the next hour to get 20 free credits.”
The problem was that when the customer took advantage of the offers, it took 24-36 hours for the free credits to appear in their accounts when they checked their balance. The lack of access to information in real-time created a bad experience for customers and skyrocketed calls to the call center.
The Telecom company learned quickly with this experience that customers assume that the information they see is in real-time. The Telecom company had 2 choices 1) constantly educate customers about the reasons for delayed prepaid credits or 2) give customers what they expected by improving their technology.
The Telecom company’s marketing team also wanted to measure the results of their marketing efforts. Due to the lack of detailed data about transactions and the latency of receiving the data, it was impossible to see the value from campaigns.
Outcome: Data was available in real-time when customer viewed their account balances. Calls to The Telecom company’s call center dropped dramatically. Costs associated with call center usage dropped and customer experience improved.
The Telecom company’s marketing team is able to measure the impact of campaigns with more precision and realize revenue growth.
Technology: To solve The Telecom company’s problem, Vates recommended using the following technologies and architectures.
Project “Cell Phone Data Packet Visualization in Real Time”:
Problem: The Telecom company’s customers would check to see if they had data packet credits left in their account and, finding they did, would then try to access the internet. When they could not access the internet, they were confused...and annoyed. The problem was that though the customers’ account showed a balance with credits, because of a 36+ hour delay between accounting and data visualization for the customer, the customers really did not have any credits. This resulted in significant volumes of calls to the call center to complain and the call center agent would spend a lot of time explaining to the customer that they did not have any credits. This was not only expensive in terms of staffing call centers, but also very damaging to the Telecom’s reputation when the customer experience was not as expected.
Outcome: Customers now see their account balance information in real-time. Customer experience improved and call center costs dropped. Loss of customers to competitors, customer churn, was reduced.
Project “NQI -Cell Phone Signal Lost”:
Problem: The Telecom company’s customers reported loss of cell phone signal to the call centers. As the call center agents checked network system information, the antenna position information was 48 hours old. Call Center agents would register the problem so that IT could fix the problem but this delay caused many operational problems and additional costs.
Outcome: Information in the NQI application became real-time was achieved by Vate’s big data analytics team and operational costs were reduced significantly.
The big data analytics use cases above are evidence that using real-time stream solutions enables Telecom companies to improve their customer experiences and reduce operational costs significantly while making direct contributions that help marketing increase market share.
Working with Vates, the telecom was able to completely change key success factors with its operations, accounting, marketing, and most importantly, its customers. The applications from the Vates projects addressed impressive volumes of high-velocity data:
IBM Streams is highly efficient, using 14.2 times fewer hardware resources and delivering 12.3 times more throughput compared to alternative open source offerings. Streams is the next generation of analytic processing methods. Telecom companies and other industries with high volumes of disparate data sources that arrive with great speed should and will migrate from traditional “batch processing” to big data and real-time analytics.
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