Operational Excellence

Harmonizing operating models to attain M&A goals

Deploy Prodapt’s industry-leading Unified Operations Framework (UOF) as a strategic solution for a streamlined operating model transformation — Achieve a 25% OpEx reduction with Zero service disruptions

M&As in the Connectedness segment are expected to rise through 2024 as technology advancements and the release of pent-up deal appetite lead to a flurry of investments, according to this PWC report. Pursuing this route, Communications Service Providers (CSPs) may adopt shortcuts towards operating model transformation. However, a contrasting perspective from McKinsey emphasizes the importance of careful planning and execution when transitioning from two separate operating models to a newly integrated one. Even in ideal conditions, harmonizing operating models is complex, time-consuming, and challenging.

Several key challenges hinder the effective unification of operating models post-M&A. These include streamlining role redundancy, improving maturity levels across merged entities, and standardizing evaluation systems. Neglecting these aspects can lead to a sharp rise in operational expenditures, customer churn rates, and loss of competitive edge.

To address these challenges and unlock the full potential of M&A, CSPs can leverage the Unified Operations Framework (UOF). UOF offers a comprehensive approach to streamline processes, optimize resources, and integrate systems and teams effectively post-M&A. It employs a function and objective-driven approach to pinpoint areas for operational change and provides specific steps for implementation.

The three key implementation steps recommended by UOF are:

  • Redesign business teams aligning with TM Forum’s eTOM framework
  • Track automation and outsourcing maturity to enhance operational efficiency
  • Define, measure, and monitor Key Performance Indicators (KPIs) to ensure performance transparency and accountability

By adopting UOF, CSPs can achieve a significant reduction of approximately 25% in Opex, and accelerate migration by 3X. This strategic approach ensures sustainable growth and competitiveness in the rapidly evolving connectedness landscape, strengthening the merged entity.

McKinsey – Transitioning from two existing operating models to a new, combined operating model requires thoughtful transition planning and execution.”

IT Agility

Unleash the power of cloud modernization

Accelerate migration of complex data pipelines to modern cloud services using a holistic approach

Communications Service providers face several challenges in managing and processing massive amounts of data generated every day from Call Detail Records (CDRs), networks, and application logs from various sources. Big data platforms like Hadoop help manage, analyze, and derive insights from extensive data but performance limitations, scalability challenges, and high maintenance efforts make it a tough challenge.

Service providers must move towards a cloud-based Hadoop ecosystem to overcome these challenges. While there are different approaches to cloud migration, the serverless route provides several benefits when compared to the traditional cloud.

According to Forrester, more enterprises are frustrated with the complexities of Hadoop’s on-premise systems and want to shift to the public cloud. Serverless and Hadoop alternatives in the public cloud will gain more traction in the near future.

This insight sheds light on cloud modernization of service providers’ ML use cases to facilitate efficient handling of large volumes of ML data, real-time data analysis, and faster decision-making.

Fig: Cloud modernization approach to maximize the value of migration

According to Forrester, many enterprises are frustrated with the complexities of Hadoop’s on-premise systems and want to move to the public cloud. Serverless and Hadoop alternatives on public clouds will gain traction in the future.

Operational Excellence

Accelerating fibre rollouts by pre-empting order delays

Leverage AI/ML to forecast delays and reduce customer churn

Fibre to the Premises (FTTP) service delivery includes deploying high-speed fibre optic connections directly to the customer premises, which involves several complexities and unexpected delays in order fulfillment. These delays can lead to missed SLAs, high customer churn, and compensation liabilities for Communications service providers.

According to Forrester, “70% of customers are likely to churn if orders are delayed, and proactive information about orders are missed”. Hence, an intelligent FTTP service delivery becomes imperative for service providers in the Connectedness industry.

Leveraging an AI/ML-powered FTTP service delivery framework can help service providers predict and address order delays before they impact the business. With the predictions from the ML model, the operations team can gain a view of the expected delays, root causes, and ways to overcome them. This helps reduce operational overload and customer churn.


Fig: Leveraging an AI-powered FTTP service delivery framework for on-time provisioning and improved customer experience

“70% of customers are likely to churn if orders get delayed, and proactive information about orders is missed”. – Forrester

Software Intensive Networks

Predicting network faults with ultimate precision using AI

Service providers ditch rule-based firefighting and embrace proactive AI to anticipate and prevent outages, saving millions and boosting customer satisfaction.

Network Operations Centers (NOCs) are pivotal for service providers in ensuring seamless connectivity and optimal performance. However, a surge in 5G, IoT, and virtualization technologies has brought unprecedented challenges. NOCs grapple with the overwhelming influx of alarms, struggling to differentiate critical issues from irrelevant ones. Manual reduction methods and rule-based approaches lead to delays and false alarms, inflating costs and hindering response efficiency.

To overcome these challenges, service providers must embrace a proactive approach, harnessing machine learning (ML) for precise prediction and resolution of network faults. Service providers can leverage ML to analyze extensive and diverse data sets, extract crucial insights and promptly implement preventative measures in real time.

Hence, adopting a network event prediction model becomes imperative to anticipate and proactively mitigate potential network failures and outages. It also enables service providers to ensure precise predictions, reduce network downtime, and cut operational expenses.


Fig: Key steps to leverage ML model for ticket prediction
and prioritization

Leveraging ML to analyze extensive and diverse data sets, service providers can extract crucial insights and promptly implement preventative measures in real time.

Operational Excellence

Redefining contact center experiences with Generative AI

Adopt the GenCARE Framework to raise efficiency, streamline processes, and reduce Opex in contact centers

Contact centers deploying AI tools for customer engagement continue to report low satisfaction scores and delayed resolution. A lack of deeper understanding of human languages and inability to comprehend nuances in text and audio messages conveying the need for support are the key reasons. This deficiency results in customer dissatisfaction and, eventually, tremendous damage to the CSP’s brand value. Key challenges faced by contact centers include:

  • Natural Language Understanding (NLU): Inability to accurately understand and interpret human languages. For example, misinterpreting customer inquiries (billing, service complaints) containing colloquial expressions leads to inaccurate responses
  • Context Retention: Struggle to retain context, leading to disjointed and frustrating exchanges, especially in longer or more complex conversations
  • Multilingual Support: Requires additional resources, training, and coordination, especially for languages with limited training data
  • Emotional Intelligence: Empathy and emotional understanding are challenging to replicate in AI systems

These challenges significantly raise multilingual support costs (20%-30%) for contact centers, with low chat containment (<20%) necessitating more live agents. This contributes to customer dissatisfaction and increased churn risk, mainly due to prolonged wait times.

As per McKinsey, generative AI can reduce the volume of human-serviced contacts by up to 50%, depending on a company’s existing level of automation. Use our GenCARE framework to enhance contact centers through Generative AI and achieve a 40% cost optimization while boosting customer satisfaction. The key components of the framework are:

  • NLU-based intent identification-Integrate the chat platforms with domain customized Generative AI models for quick and accurate query handling
  • Context-enhanced agent support-Leverage context retention capabilities to quickly identify customer issues and generate automated notes to boost agent productivity
  • Real-time sentiment analysis -Classify and score sentiments using the sentiment analysis module. Respond as per the customer’s emotional status
  • Multilingual query resolution-Use language translation to achieve zero wait time with a language-independent unified team

As per an McKinsey estimates that generative AI can reduce the volume of human-serviced contacts by up to 50 percent, depending on a company’s existing level of automation.

Operational Excellence

Cultivating Analytics-driven Excellence in Service Provisioning

Utilize the FibrePro Analytics Maturity (FAM) Model for improved decision-making, enhanced customer satisfaction, and cost efficiency

While organizations have made substantial investments in data and analytics, an HRB report reveals that only 23.9% of companies identify as data-driven, and merely 20.6% have successfully cultivated a data-centric culture. The level of data analytics maturity is a critical element for fibre operators in transitioning from intuition-based decision-making to an insight-driven organization.

Below are the primary challenges faced by fibre operators in achieving data analytics maturity despite huge investments.

  • Lack of data and analytics strategy aligning with business
  • Cultivating a data culture that binds data talent, tools, and decisions
  • Creating a robust data architecture that enables controlled, secured data access and utilization
  • Building a skilled team with both domain and data analytics expertise

Employ the FibrePro Analytics Maturity (FAM) Model, a holistic framework for fibre operators to overcome these hurdles and build a fully integrated data-driven organization. FAM synchronizes data capability and adoption maturity to enhance data analytics maturity across the fibre journey. This model comprises 4 key stages: Descriptive, Diagnostic, Predictive, and Prescriptive & Cognitive.


This Insight delves into the journey of data analytics maturity for service provisioning use cases, underscoring its pivotal role in boosting revenue generation, competitiveness, and customer satisfaction for fibre operators.

As per an HRB report only 23.9% of companies are data-driven, and 20.6% have successfully cultivated a data-centric culture.

IT Agility

How can service providers elevate customer churn prediction by leveraging Quantum Machine Learning?

According to Nielsen, “The right solutions could save up to $1.6 billion of revenue lost to customer churn in a year.”


The Connectedness industry has encountered various challenges throughout its existence, with one major challenge being customer churn. Customer churn refers to the percentage of customers who terminate their subscriptions or switch to a different telecom provider within a specified time limit.

The Connectedness industry experiences the highest customer churn rate compared to other sectors. According to a study by the Aberdeen Group, the average telecom company loses $100 per month for every customer that churns. This means that a company with a 10% churn rate could be losing $120 million per year.

According to Forrester, a bad experience is enough to prompt customers to consider switching providers. This makes continuous investment in customer churn prediction necessary, given that customer retention is more cost-effective than customer acquisition. High customer churn has detrimental effects on the business, including revenue loss, missed cross-selling and upselling opportunities, and difficulties in forecasting and planning for future growth.

The emergence of data collection techniques and the need to generate deep insights have led to the expansion of analytics applications across several domains. However, Communications Service Providers generate vast amounts of data every day, making it a significant challenge to draw meaning out of such complex, multi-faceted data.

Customer churn analysis is resource-intensive and requires extensive computational power

As the volume and type of data captured in the Connectedness industry increase exponentially, the number of metrics and evaluations that require processing also increases. Customer churn prediction and analysis are usually carried out using ML modeling. Numerous attributes are used for research, such as the billing data, Call Detail Records (CDRs), and Contract/Subscription data. Hence, customer churn prediction requires significant computational power.

Furthermore, customer churn is a complex process that involves multiple interdependent parameters. For instance, network quality, which directly affects customer satisfaction, can be impacted by several other factors, such as network congestion, signal strength, and coverage area. A recent Gartner report predicts that enterprise-generated data processed in data centers, or the cloud, will increase to 75% from the current 10% by 2025. In other words, more than 180 zettabytes of data will be generated globally from over 41 billion connected devices. As more parameters are added to make precise predictions, the current predictive methods will become ineffective in processing and analyzing the multi-faceted and intricate data, which require extensive time, energy, and resources.

Major challenges with the Classical Machine Learning1approach of churn prediction

  • Examining large and diverse datasets to provide personalized solutions is a challenging task that often results in resource wastage
  • Allocation of resources is challenging due to the shift from a centralized to a
    hyper-distributed subscriber environment
  • Analyzing and deriving insights from multidimensional data is cumbersome, leading to difficulties in identifying and extracting complex churn patterns

Service providers must embrace Quantum Machine Learning to overcome the shortcomings of Classical ML, such as slower processing times, inability to process large amounts of data in parallel, and low accuracy.

Quantum Machine Learning (QML): A strategic imperative to predict customer churn and maintain the competitive edge

Quantum Machine Learning (QML) offers a new approach to analyzing large datasets and extracting valuable insights for faster estimation of customer churn. It can efficiently model high-dimensional feature space using quantum parallelism. Quantum parallelism is a feature of quantum computers that allows them to perform multiple calculations simultaneously, exploiting the superposition of quantum states to explore multiple solutions at once. By leveraging the power of quantum computing, it can perform brisk calculations, enabling businesses to analyze customer data efficiently and effectively.

Using QML, service providers can develop faster predictive models to identify customers likely to churn. These models can process factors such as customer demographics, purchase history, and browsing behavior, thereby increasing the efficiency of finding at-risk customers.

Here are three scenarios where QML can excel:

  • Complex pattern recognition: Churn prediction requires identifying intricate patterns and dependencies in the data, which is difficult for Classical ML. Quantum ML can be leveraged to handle complex computations and analyze high-dimensional data, including call frequency, data usage, location, and customer demographics, to uncover hidden correlations contributing to churn.
  • Real-time churn Prediction: Preventing churn requires timely action, and Classical ML, due to its slower processing time, proves ineffective in this regard. Quantum ML enables real-time churn prediction by providing faster computations and optimizations. It can process data quickly, allowing service providers to identify potential at-risk customers and take proactive measures to retain customers promptly.
  • Handling Big Data: Churn prediction often deals with large datasets that can be computationally intensive to process using Classical ML methods. QML can provide computational advantages for analyzing big data by leveraging the inherent parallelism and quantum algorithms designed for data-intensive tasks. Telecom marketing leaders can use QML to fine-tune models for predicting churn and optimizing parameters like learning rates and regularization factors for improved accuracy without extensive trial-and-error experimentation.

Furthermore, QML could help service providers explore new data analysis and predictive modeling possibilities, potentially leading to accurate insights from the available data. It will allow data teams to explore intricate data relationships, enhance security measures, and handle significant data challenges.

The following table talks about a sample customer churn prediction model, highlighting the advantages of Quantum ML over Classical ML in computing highly complex and interdependent data.

Table 1: Comparison of Classical ML and Quantum ML implementation of customer churn prediction

A notable enhancement in the overall speed, by 152 times, indicates a significant advancement in both computational efficiency and analysis of extensive datasets. This progress highlights the rapid evolution of QML capabilities and underscores the potential to tackle complex problems and derive insights faster.

Graph 1: Scalability7 potential of Classical ML & Quantum ML

As the number of parameters within a dataset expands, implementing QML significantly reduces the overall number of evaluations required for data processing. In the graph, as the number of features/parameters increases, the number of assessments increases exponentially for Classical ML, signifying that more computational power and resources are required to solve a highly complex problem. In contrast, the graph is relatively flat for Quantum ML, indicating that it will be highly efficient in solving complex problems.

Given the widespread anticipation of a big data surge across industries, the escalation in the number of prediction parameters is inevitable. This makes QML’s efficiency imperative for service providers to enable them to save time and drive decision-making capabilities.


Quantum ML is close to a breakthrough in its journey. It can completely reform the machine learning process and models, drastically reducing processing time and significantly improving performance. Leveraging QML can enable service providers compute complex use cases like customer churn instantly and accurately, providing considerable benefits to the service providers.

Software Intensive Networks

Unleashing high-speed Fiber connectivity

Accelerate Fiber connectivity and reliability with enhanced network orchestration and assurance solutions

The demand for faster and reliable connectivity in the digital era has led to the rise of Fiber optics, transforming our digital experiences. Gartner‘s recent report highlights a growing preference for gigabit Fiber to The Home (FTTH) services among consumers, emphasizing the importance of modern connectivity. By 2025, approximately 60% of Tier-1 service providers are expected to adopt the 10 Gigabit Symmetrical-PON (XGS-PON) technology. Despite this growth, Fiber broadband encounters obstacles like slow setup, connectivity issues, and network fragmentation from diverse technologies. McKinsey notes that 40% of potential users’ decisions and churn rates are influenced by Fiber network experiences.

To address the challenges, service providers need to transition towards automation and data-driven decision-making in network management. This shift facilitates efficient deployment, operation, and maintenance of networks, while also providing valuable performance insights. Achieving this requires the adoption of right technology enablers, allowing for zero-touch provisioning, nearly zero-touch operations, and comprehensive network insights to expedite service setup.


Fig : Essential enablers for network transformation success

The fiber network’s lifecycle involves various phases, from planning and design to orchestration and assurance. This insight examines essential enablers for effective orchestration, assurance, and visualization. Through these enablers, service providers can expedite service setup up to 60%, advancing their path towards “Fiber for the Future.”

To succeed, service providers must prioritize zero-touch provisioning, near-zero touch operations, and comprehensive network insights for a faster service setup

  • Dibyendu Dey, Principal Architect
  • Rohit Karthikeyan, Manager – Strategic Insights

Maximize value from cloud migration

Migrate complex online charging systems and network service order management to the cloud holistically.

Service providers across the globe are either considering or have already increased their spending on Cloud. Gartner states, “Cloud will be the centerpiece of new digital services and experiences, which is why 40% of all enterprise workloads will be deployed in the cloud over the next few years”. As Online Charging Systems (OCS) and network Service Order Management (SOM) are at the forefront, moving them to the cloud renders the advantage of coping with the evolving 5G landscape and virtualization. However, service providers are still reluctant to make this transition because:

  • Handling heavy payloads and workflows while juggling through an integration-heavy architecture with zero latency is cumbersome
  • Securing sensitive data such as invoices, Call Detail Records (CDRs), history of customers’ usage, financial transactions, and porting information is critical
  • Adhering to complex data compliance requirements for local and national data regulatory norms

In addition, unlike other CRM systems, the transition of OCS and network SOM to the cloud involves significant challenges due to the complex networks and integrations in the telco architecture. These are critical systems that go through numerous changes every day, and they can’t afford delays. Hence successful cloud migration requires a robust deployment architecture, end-to-end automation, and continuous security to quickly adapt to real-time changes in the environment and accelerate secure releases.


Fig: Key focus areas for successful cloudification of OCS and network SOM

Moving OCS and network SOM to the cloud offers phenomenal advantage with the evolving 5G landscape and virtualization. However, service providers are still reluctant to make this transition.


Transform customer experience with unified order management

Reach new levels of customer centricity with Salesforce

An Order Management System (OMS) is the backbone of the ordering and fulfillment process. A unified OMS is crucial for delivering exceptional Customer Experience (CX) – it is the deciding factor between customers’ loyalty or their willingness to switch to a competitor. A unified OMS can strengthen revenue streams, and reduce expenses, leading to improved business performance.

According to Forrester, 90% of customers believe their experience during the order journey is as significant as the product or service itself. However, a traditional OMS faces significant challenges, such as relying on manual processes, generic order journeys, duplicate leads, and inflexible systems, which negatively impact CX.

Service providers must use a unified order management system to overcome these obstacles. Salesforce OMS, a single platform that facilitates integration with various systems and data sources, delivers a comprehensive view of the customer and their orders across all channels. Although the Salesforce OMS platform is extremely powerful, service providers must take the right implementation approach to achieve maximum benefits.

Salesforce OMS for CX transformation: A strategic implementation approach


To deliver a consistent CX, service providers must employ a holistic and customer-centric implementation approach that encompasses the entire order journey, viewed through three different lenses:

  • Lens 1: Examine primary shortcomings during order capturing and address issues
  • Lens 2: Investigate major inefficiencies in the order orchestration stage and resolve issues
  • Lens 3: Analyze, identify, and resolve gaps in customer service stage and deliver exceptional CX through automation

The recommended implementation approach effectively addresses challenges and reduces OMS implementation time by up to 40%, accelerating order journey towards a modernized system and enhancing CX.

Salesforce OMS offers a unified platform that can integrate with various systems and data sources, providing a comprehensive view of the customer and their orders across all channels.