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Decision Analytics for Operational Excellence
Informed decisions employing advanced analytics
This catalyst project combines operational data from OSS/BSS, business data mainly from BSS, external market data, and the corresponding business KPIs. This data is combined by two analytical tools to provide a better understanding of the causes of a particular Communications Service Providers (CSPs) problem (e.g. reduce operational costs, use customer experience to increase customer retention vs. customer churn) and an automation of the solution. Read More
 
Business Proposition

Often Communications Service Providers (CSPs) have distributed information sources and destinations. Exploring and analyzing such distributed data does not always lead to the root cause of a business problem. At times, even predictive models that warn of upcoming problems might not identify the underlying causes of the problems due to walls between the information areas. This catalyst project combines data and capabilities of two analytical tools and provides:
  1. Integrated application of hitherto distributed data: BSS + OSS + Market Data + Corresponding KPIs.
  2. Predictive models to identify real root causes of complex business problems.
  3. Application of data and capabilities of two analytical tools to rapidly solve business problems
  4. Application of decision analytics’ tools and processes for:
    1. Customer retention analysis and preemptive actions to increase the customer retention and reduce churn.
    2. Reduce operational costs, e.g. in a call center.


TM Forum Standards in Use
or Development

  • Business Process Framework (eTOM)
  • The entities and corresponding processes for developing KPIs within the tools are drawn from the latest Information Framework (SID)
  • One of the tools, Business Analytics Accelerator for Telecom, is undergoing conformance certification of its predefined entities within Information and Business Process Frameworks.
 
 

Team Member Contributions

Champion

Participants
 

 

Demonstration Scenarios

  • Using Customer Experience for customers with lower customer satisfaction levels to determine preemptive actions to increase customer retention and to reduce churn, especially for customers with high Life Time Value (LTV)
  • Reduce operational costs: reduce handset returns to the store. Identify the underlying cause for a lot of returns and define a treatment plan. E.g. identify that customers are buying the wrong handset for their needs (too many features, too few features, wrong data plan for that phone)
Read More
 
 

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Customer Champion Quote


“Championing the operational excellence decision analytics catalyst, we see that on increasing customer experience and at the same time reducing OPEX on the operator side we create a win-win-situation for the operator and for the end customer”
Karl Wilhelm Siebert, Director, Regional Networks & Operations Branch West, Vodafone







Description
Often CSPs have distributed information sources and destinations. Exploring and analyzing such data silos does not always lead to the root cause of a business problem. At times, even predictive models that warn of upcoming problems might not identify the underlying causes of the problems due to walls between data. To be productive, the decision analytics process must consider various combinations of network information and business information along with certain additional analytical capabilities.

This catalyst project combines operational data from OSS/BSS, business data mainly from BSS, external market data, and the corresponding business KPIs. This data is combined by two analytical tools to provide a better understanding of the causes of a particular Communications Service Providers (CSPs) problem (e.g. reduce operational costs, use customer experience to increase customer retention vs. customer churn) and to enable automation of a solution.

Through this approach, the CSP achieves better performance and lower operational costs, and can make informed decisions based on hard facts and data. For example, the CSP can predict which customers are Often CSPs have distributed information sources and destinations.

The catalyst builds from the outcome of the ‘CEM Control Center’ catalyst (demonstrated at The Management World Nice 2010), which combined additional decision and knowledge fundamentals for a new product to address business questions such as, how do we increase revenue in a more controlled manner or, how do we better manage investments, etc.

The catalyst uses a predefined set of KPIs, adapted to suit individual business functions. It illustrates the process via accelerated data assimilation and TCO analysis to enable quick decisions.

Catalyst participants and their roles
  • Vodafone: The service provider champion for the catalyst, provides the problem statement and validates the suggested solution
  • Subex: The Subex ROCware platform explores domains with problems, runs the appropriate propensity analysis, identifies their root cause, and sends them to the appropriate functions for resolution.
  • Microsoft: Microsoft SQL Server Business Analytics takes the outcomes of Subex ROCware as a starting point and then expands the scope of the analysis by considering additional data to arrive at recommended actions, having considered the root of the problems.
  • N-Pulse: Define the architecture, advisory activities, Business Processes, TCO calculation methodologies, Definition, Deployment and hosting of the Microsoft demo

Use Case Description
1.1 Introduction
The Catalyst demo shows as an example scenario with:
  • Using Customer Experience: customer profile and customer Life Time Value (LTV) to a customer retention analysis and churn analysis
  • Customer churn prediction; propensity of customers to increase customer satisfaction churn, along with estimates of their Life Time Value (LTV)
  • Indications of possible actions to reduce the churn.
In the first step, Subex’ ROCware platform gathers data provided by the Business Systems (like Billing, CRM) and, as a second step, analyzes the data using dimensions such as:
  • Billing
  • Usage
  • Location/geographic place
  • Customer data
  • Time
  • Products bought by the customer
  The Subex platform can also compute the actual customer Life Time Value (LTV) and actual churn values, to validate the propensity models. It also provides comparisons between segments of customers, customers with higher LTV and regions with high regional churn rates.

The scenario in the Demo shows that in some regions there are serious churn problems. Alarms according to threshold values can be shown.

To get a better understanding of the reasons of the churn, Microsoft SQL Server Business Analytics takes over the Subex analysis data and adds to the existing metrics of Subex data from OSS and BSS like:
  • Network quality over a region/# of calls to the call center,
  • Product information (e.g. complexity of a service price plan)
  • Market data (about equipment/handset limitations)
  • Billing statistics
  • Selling parties data
The Microsoft tool then displays customer scores over Handsets and average customer profile scores over service price plan within the regions with higher LTV or churn score.

Predictions / trends of the churn can indicate the causes of the CSP’s churn problems. It shows then that the reasons for VIP customers likely to churn are these factors (ranked highest to lower): Equipment limitations, Plan complexity, Price of service, # of calls to call center.

An additional what-if scenario is included to support decisions. It shows the change of the customer’s churn score if we change to a different Service plan, Handset, or network coverage.

A similar scenario can be used for increasing customer profitability, showing what-if scenarios for customers with low profitability if we change their service price plan or the handset.

1.2 Use case steps
This use case shows how the tools can be utilized using historical and predictive information:
  • Actors:
    • McNamara – an Executive of Vodafone is using the analytics tools on his desk to understand how churn and profitability is evolving
    • Dr. Schmitt – an operations manager at Vodafone using the analytics to discover the reasons for churn and profitability reductions
    • Subex ROCware platform – provides analytics on customers, revenue, and churn.
    • Microsoft SQL Business Analytics server: accessing further data and uses the Subex Analysis to provide what if scenarios for product or network enhancements.
    • Various BSS, OSS and Market data as a source for the analytics.
  • Activities:
    • McNamara logs in the Subex ROCware
    • Subex ROCware displays the current status of churn over several regions. The churn rate is displayed and shows some very high rates in some regions. It also shows that some customers with high estimated Life Time Value are at risk of churning.
    • He wants to find more about the problems to solve them; he calls the operations manager, Dr. Schmitt, to get the reasons fixed.
    • Dr. Schmitt logs into the Microsoft systems which has always in real time the actual data from the Subex platform.
    • The Microsoft Business Analytics displays some possible reasons by probability for churn. With the predictive analytics of the Subex platform, and what-if scenarios (e.g. replacing handset, enhancing the quality of service) circles the solution and can support the decisions.