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  • Building a Case for Advanced Analytics in the Capacity Planning process

    Building a Case for Advanced Analytics in the Capacity Planning process

    There has been significant data growth in the world of telecommunications during the modern era of mobile technologies, i.e., 4G and 5G. While data growth has often been marketed as a massive window of opportunity, in reality, the ever-expanding volumes of data are overwhelming for existing systems, thereby indirectly leading to issues concerning operational efficiency and network performance. To utilize the vast volume of structured and unstructured data, organizations must have the right innovation-driven solutions to maximize their revenue potential.

    As a part of the network capacity management team, I would like to elaborate on the above from the perspective of capacity planning. In network capacity planning, it is essential to identify potential shortcomings or, in other words, factors that can impact network efficiency and performance. Some of these factors include the need for capacity expansion due to the unavailability of adequate network resources for handling the traffic demand, or an optimization requirement in the network investment plan for better utilization of existing resources. Traditionally, most of these decisions are made manually, which we all can agree, is not practical, efficient, or even error-proof. An AI-based capacity management system is the need of the hour to exploit the full potential of resources and network data.

    How AI can enhance Capacity Management

    AI and advanced analytics are terms that get used a lot, across industries, especially by marketing teams. However, beyond the ‘buzzword,’ these technologies have an essential role to play in enabling improvements from current, traditional processes. Advanced analytics-driven capacity management solutions can help users decrypt relevant information to derive useful insights by applying data analysis, predictive analytics, forecasting, and optimization models. These insights, backed by data, and fine-tuned through Machine Learning, can help organizations make the best possible decisions by anticipating and implementing necessary network changes ahead of the demand curve.

    A significant component for providing reliable Network Investment Planning (NIP henceforth) recommendations towards Capacity Enhancement/Optimization is through the accurate forecasting of capacity KPIs. Forecasting is the foundation on which NIP recommendations are based.

    A pool of forecasting models can be used to predict network performance. These models can be broadly classified as qualitative methods and quantitative methods. Qualitative methods are used when historical data is not available or when data is noisy. Quantitative methods, on the other hand, make forecasts based on mathematical models rather than subjective judgement. Choosing the right forecasting model depends on many factors like the context of forecast, relevance, availability of historical data and the desired level of accuracy.

    Which is the right forecasting model to adopt for your business?

    Different forecasting methods have distinct advantages and disadvantages. Therefore, selecting the right forecasting method is of critical importance to all decision-makers.

    My data science team at Subex and I have built a proprietary machine-learning model for forecasting called CapMan4C. During this experience, we also collaborated with a team of domain experts to have clarity on the problem which businesses are trying to resolve and to understand data specific to the network domain.

    I bring up this experience to highlight that the thoughts involved behind building a proprietary framework for forecasting are based on two criteria:

    • What does our expertise in the telecom domain tell us?
    • What do our customers say?

    Based on these insights, it is evident that traditional forecasting time series models are observed to not perform well on network data. Using my prior data science experience in the healthcare industry, I applied different machine learning models like decision tree, random forest, etc., for forecasting on telecom network data. I observed that plugging in these existing models do not provide the desired level of accuracy. The approach of leveraging a domain-driven model eliminates the problems mentioned above. Its framework is designed so that the selection of a base model for forecasting does not solely depend on traditional time series models.

    To further enhance the accuracy of the model, this kind of framework also needs to include three key components:

    1. Recency factor – The general assumption while working with time series data is that all the historical data points have equal weightage. However, when the event ‘recency’ is accounted for, this notion does not always hold. In theory, an event that has taken place more recently is more indicative of the current situation than a distant past.
    2. Feature engineering –Feature engineering is a creative process in which we experiment and extract features that can be useful for enhancing the model. Several features, if extracted at a granular level from the data (including data from external sources), can help identify hidden trends and seasonality in data and make more insightful forecasts from the machine learning model.
    3. Auto-hyperparameter tuning– Hyperparameters are parameters of ML-models specified by “hand” based on expertise and domain experience. These parameters can be used to control the learning process, which can significantly impact the performance of the trained model. The more flexible and powerful an algorithm is, the more design decisions and adjustable hyper-parameters it will have. Traditionally, Grid Search or Random search is used to perform hyperparameter optimization. Grid Search works by searching exhaustively through a specified subset of hyperparameters. This can be very time consuming and computationally expensive. Random search is slightly better but is similar to Grid Search because it pays no attention to past evaluation. As a result, network teams often spend a significant amount of time evaluating “bad” hyperparameters.

    In summary, operating a network is a complicated endeavor, and regardless of how much planning takes place, problems do occur. Identifying the right kind of AI-driven approach toward capacity management provides network planners the ability to analyze “what-if scenarios” based on forecasts from ML models. It helps them take calculated decisions on Network Investment Planning based on AI-based recommendations. This will help them to mitigate risks proactively and maximize their revenue potential.

    To understand these key pillars, and unlock the full potential of your network

    Read This Whitepaper

  • Anomaly Analytics: It’s Role in 5G and Assurance

    Anomaly Analytics: It’s Role in 5G and Assurance

    Introduction

    According to the latest trends and reports published by industry forums, by mid-2021 5G adoption would have reached around 250 million active subscribers from this year’s 80 million accounting for an increase of almost 200%. Though this increase is substantial, in the larger scheme of things, 250 million subscribers only account for 3% of the total mobile subscription globally. Furthermore, revenue predictions for 5G also state that 5G technology will not boost revenues but give a telecommunication service provider far more flexibility in the business models that can be deployed within the larger eco-system.

    This fact also means from an assurance perspective; the telecommunication service provider will need to invest in far more hardware and the associated services to onboard 5G services into the revenue assurance practice. While this may not be a significant investment initially considering the low adoption of 5G. However, this trend takes a significant turn as we go into the year 2022 to 2025, wherein the 5G service is expected to account for 30% adoption, thereby exponentially increasing your data volume, the hardware footprint and the OpEx required to perform daily revenue assurance activities for 5G services. At the end of the day with falling ARPUs, the discrepancies quantified will not justify the investment made towards onboarding 5G services into the traditional revenue assurance practice.

    You will need to look for a newer approach that provides a better cost vs benefit.

    Pressure on ROI from RA investments Traditionally revenue assurance involves the process of taking two or more datasets between the switch and billing and reconciling them to identify usage that was not billed due to issues in the data sent downstream. During 2G services, this volume for a tier 1 operator was anywhere between 100 million to 500 million xDRs, which required to be reconciled daily. However, with 3G and 4G service, this volume exponentially increased due to the larger mobile service adoptions and the associated services being provided. Today for a tier 1 operator with 100 million subscribers, the revenue assurance tool is required to reconcile anywhere from 5 billion to 10 billion xDRs daily. With ARPUs falling year on year, there is a significant pressure on the revenue assurance practice to justify the return on the investment made by the organization to plug leakages, a goal that will become significantly harder to reach.

    5G will only carry forward this trend at an even faster pace.

    Role of Anomaly Analytics

    As an example, let us look at a hypothetical revenue assurance setup. For an operator with 100 million subscribers, you will have anywhere from 10 billion to 5 billion xDRs generated daily. This data is then either stored in your data warehouse or your big data lake. Here the revenue assurance practice will require data to be processed individually, loaded in the tool, reconciled daily, and retained for 30 days with the goal being to identify revenue leakages. This setup would require significant investment on hardware which can run into millions of Dollars on its own, excluding the cost of license, deployment, support and manpower.

    Here we employ an Anomaly Analytics methodology, which significantly simplifies the TCO and the complexity of reaching the end goal. Taking the same hypothetical revenue assurance setup as above. The data stored in the data warehouse or big data lake can be aggregated accordingly to specific dimensions such as type of subscribers, cell site, rate plans, type of services, and measured against various metrics. In this hypothetical approach, only the aggregated data is processed using anomaly detection to monitor millions of KPIs, identify discrepancies quicker and correlate with other associated datasets. The approach here significantly uses lesser hardware and provides near real-time insights to only KPIs wherein the solution derives as a genuine discrepancy. More importantly, we achieve the same goal of identifying discrepancies.

    Traditional Reconciliation vs Anomaly Analytics Approach

    The point of concern between the traditional reconciliation and the anomaly analytics approach is that with the latter, the missing data identified by the prior is not readily available. In the anomaly analytics approach, one must dive into the data in the data warehouse or the data lake to identify the granular data. So we arrive at the question if it is worth investing is an anomaly analytics approach even if one does not have ready access to the granular data?

    As mentioned earlier, as a trend,we see a drop in ARPU; regardless of the service, the value of a single unit of measure, in this case, xDR, is trending downwards. As of last year, the ARPU dropped by 2% in the developed market (America and Europe) and by 18% in the developing market (South East Asia). This translates to the fact that if one identifies say 10,000 xDRs missing at a value of $1000, this value drops by a percentage every year due to the growing service usage and the drop in prices. Ultimately the question of going with an anomaly analytics approach for revenue assurance reconciliation is a question of “cost vs benefit.”

    Conclusion

    With newer 5G services, we see  a significant shift in our approach to revenue assurance and especially reconciliations. The term “single source reconciliation” has taken birth due to anomaly detection allowing the business to analyze the data and obtain insights on service, quality, billing, and service orchestration from a single source of data processed over a short period. While we are in the early stages of this approach, we are certainly expecting anomaly analytics to take the initial steps within a revenue assurance function and grow to become the mainstay within a couple of years.

    Want to know how Anomaly Analytics can play a role in 5G Assurance?

    Schedule a demo!

  • Cómo Next Best Action puede ofrecer un mayor valor para el cliente – Subex Limited

    Cómo Next Best Action puede ofrecer un mayor valor para el cliente – Subex Limited

    El motor de recomendación o Next Best Action juega un papel esencial en la influencia de las preferencias humanas. Ya sea Amazon, Netflix, YouTube o cualquier otra empresa importante, utilizamos sus recomendaciones en nuestra vida cotidiana. La siguiente mejor acción o el motor de recomendación tiene un impacto significativo en el rendimiento del negocio tanto en términos de generación de ingresos como de satisfacción del cliente.

    Entonces, ¿por qué los operadores necesitan un motor de recomendación? La mayoría de los operadores de telecomunicaciones tienen una base de clientes en millones y cientos de productos. Con tantos productos para combinar con los del cliente, ¿cómo identifica el mejor producto para cada cliente? Y no olvidar las múltiples variables que definen el comportamiento y las necesidades del cliente.

    Las empresas pueden usar el resultado del motor de recomendaciones de múltiples maneras para impulsar su estrategia. Si el plan es aumentar las ventas o mejorar los ingresos, el usuario comercial puede poner una condición adicional en el modelo de que el precio debe ser más alto que el precio actual suscrito. Si el objetivo es la retención, un usuario comercial puede establecer condiciones para que el producto recomendado tenga el mismo precio o menos que el producto que el cliente actual está utilizando. En el caso de la venta cruzada, podemos seguir la misma estrategia que la empresa quiere que los clientes se expongan a nuevos productos.

    Aparte de los beneficios tangibles que un motor de recomendación puede ofrecer a las empresas; Puede proporcionar muchos beneficios intangibles a los clientes, como una mejor experiencia, simplicidad para elegir y satisfacción, lo que lleva a una mayor lealtad del cliente.

    Diferentes enfoques para la próxima mejor acción

    Las técnicas más populares utilizadas son el filtrado colaborativo y el filtrado basado en contenido. El filtrado colaborativo significa filtrar los productos que un usuario podría esperar en función de las acciones de usuarios similares y el filtrado basado en contenido, también conocido como filtrado cognitivo, donde las recomendaciones de productos se realizan en base a una comparación entre el contenido de los elementos y un perfil de usuario. El enfoque de filtrado colaborativo supone que los consumidores con un perfil similar pueden tener las mismas necesidades y tomar decisiones similares, mientras que, en función del contenido, los datos históricos del cliente forman la base de la recomendación.

    Existen muchos otros métodos no tradicionales y mucho más avanzados, como el aprendizaje profundo, el aprendizaje social y la factorización de tensor que se basan en el aprendizaje automático y las redes neuronales para impulsar el proceso de recomendación y llevarlo al siguiente nivel. Creará un proceso mucho más eficiente para CSAT (puntuación de satisfacción del cliente) y retención.

    Construir el motor de recomendaciones no es suficiente

    Los operadores necesitan un objetivo claro al implementar la siguiente mejor estrategia de acción. El motor de recomendación podría recomendar un producto de menor precio como la mejor opción para los clientes que conduce a la pérdida de ingresos para el operador. Por lo tanto, es necesario identificar que el producto que mejor se ajusta cumple con los objetivos del cliente y del operador.

    Además, los productos de telecomunicaciones tienen una validez diferente, lo que significa que un cliente que haya comprado un producto con una validez de 60 días comprará solo después de 60 días. Por lo tanto, el modelo debe tener en cuenta esta brecha de compra al recomendar nuevos productos.

    Un desafío que enfrentamos con uno de nuestros clientes fue que los clientes estaban comprando múltiples productos similares en un mes determinado, lo que dificultaba las recomendaciones. Para superar esto, decidimos que bajo cada tipo de producto: datos, voz, SMS y VAS, recomendaríamos los mejores productos 3-4. Logramos esto al proporcionar clasificaciones para los productos en la canasta de recomendaciones. La clasificación también ofrece flexibilidad al usuario comercial para que pueda agregar más condiciones a los productos en función de su estrategia, como ventas adicionales, ventas agresivas o ventas cruzadas.

    Actualmente estamos logrando un 70% -80% de precisión en los 5 mejores productos recomendados para diferentes segmentos de valor para el cliente.

    ¿Usted necesita La Siguiente Mejor Estrategia de Acción?

    Usar las interacciones o ideas de los clientes es la mejor manera de identificar los patrones de comportamiento de sus clientes y usarlos para proporcionar las mejores recomendaciones a sus clientes. Un motor de recomendaciones puede ayudarlo a identificar los comportamientos de los clientes, los patrones de compra y traducir los conocimientos en un enfoque hiperpersonalizado para sus campañas de marketing. El resultado es una segmentación de usuarios más profunda con claridad, mayor LTV y mayor retención de clientes.

    Aquí hay un punto de vista sobre la importancia de la próxima mejor oferta o motor de recomendación para su negocio.

    Descarga el PoV

  • How Next Best Action can deliver higher customer value

    How Next Best Action can deliver higher customer value

    The recommendation engine or Next Best Action plays an essential part in influencing human preferences. Whether Amazon, Netflix, YouTube, or any other major company, we use their recommendations in our day to day lives. The next best action or the recommendation engine has a significant impact on the business performance both in terms of revenue generation and customer satisfaction.

    So why do operators need Recommendation Engine? Most of the telecom operators have a customer base in millions and hundreds of products. With so many products to match with customer’s, how do you identify the best product fit for each customer? And not to forget the multiple variables that are defining the customer behavior and needs.

    Businesses can use the output from the recommendation engine in multiple ways to drive their strategy. If the plan is to upsell or revenue enhancement, the business user can put an extra condition in the model that the price should be higher than the current subscribed price. If retention is the objective, a business user can set conditions for the recommended product to have the same price or less than the existing product customer is using. In the cross-sell case, we can follow the same strategy as the business wants customer exposure to new products.

    Other than tangible benefits that a recommendation engine can offer to business; it can provide a lot of intangible benefits to customers, like better experience, simplicity to choose, and satisfaction, leading to higher customer loyalty.

    Different approaches for Next Best Action

    The most popular techniques used are collaborative filtering and content-based filtering. Collaborative filtering means filtering out products that a user might expect based on actions by similar users and content-based filtering, also known as cognitive filtering, where product recommendations are made based on a comparison between the content of the items and a user profile. The collaborative filtering approach assumes that consumers with a similar profile may have the same needs and make similar choices while in content-based, the historical customer data form the basis for the recommendation.

    There are many other non-traditional and much more advanced methods such as deep learning, social learning, and tensor factorization that are based on machine learning and neural networks to power the recommendation process and take it to the next level. It will create a much efficient process for CSAT and retention.

    Building the recommendation engine is not enough

    Operators need a clear objective when implementing the next best action strategy. The recommendation engine might recommend a lower price product as the best fit for customers leading to revenue loss for the operator. So, it’s necessary to identify that the best-fit product meets both customer and operator objectives.

    Also, telecom products have different validity, which means a customer who had bought a product with 60 days validity will buy only after 60 days. So, the model must account for this purchase gap while recommending new products.

    One challenge that we faced with one of our clients was that customers were buying multiple similar products in a given month, making recommendations difficult. To overcome this, we decided that under each product type – Data, Voice, SMS & VAS, we would recommend the top 3-4 products. We accomplished this by providing rankings for products in recommendation basket. Ranking also offers flexibility to the business user that they can further add more conditions on products based on their strategy, such as up-sell, aggressive -sell, or cross-sell.

    We are currently achieving 70%-80% accuracy in the top 5 recommended products for different customer value segments.

    Do you need the Next Best Action strategy?

    Using customer interactions or insights is the best way to identify your customer behavior patterns and use them to provide the best recommendations to your customers. A recommendation engine can help you identify customer behaviors, buying patterns, and translate the insights into a hyper-personalized approach for your marketing campaigns. The result is a more in-depth user segmentation with clarity, higher LTV, and higher customer retention.

    Here is a Point of View on the importance of the Next Best Offer or Recommendation Engine for your business.

    Download the PoV

  •  Two ingredients to successful telecom partnerships 

     Two ingredients to successful telecom partnerships 

    Over the past few years, analysts have been touting the benefits of cross-industry partnerships between incumbents as well as challengers. Partnerships provide a way to leapfrog business, penetrate new markets, and grab a more significant share of the pie in a synergistic manner.

    Communication service providers (CSPs), the nerve-center of perhaps the most complex partner ecosystems, are no stranger to the value of partnerships. However, with digital transformation quickly replacing legacy systems and processes, these players struggle with low digital elasticity. This hinders their ability to be competitive and develop alliances quickly. Without digital tools and the right platforms, navigating the complex web of multi-partite telco partnerships can be challenging, yielding lesser value than expected.

    What are these critical platform capabilities and digital tools, and how do they deliver benefits? Let us look at some of the partnerships forged by two leading telecom players to understand these must-have features:

    Airtel’s vibrant expansion

    Over the past few years, Bharti Airtel has been aggressively innovating to extend its reach into different verticals and expand its product portfolio for its 300+ million customers. The Airtel Payments Bank, a comprehensive payments portal, saw partnerships with many players like MasterCard and HPCL in addition to products like Bharti AXA Life insurance. In September 2019, Airtel entered the OTT segment with the launch of Airtel Xstream, a digital entertainment platform that provides movies, apps, games, and TV channels. Since its release, Xstream has been forging partnerships with various media content providers, notably Netflix, Amazon Prime, and Lionsgate Play, to deliver content in India’s diverse regional languages. In keeping with its brand identity of youthfulness, Airtel is set to promote health and fitness content via Spectacom, a local upstart, to all its users. More recently, Airtel has tied up with Apollo Hospitals in a telco-healthcare deal that aims to use Airtel Thanks app to provide an AI-based COVID-19 risk assessment.

    Lessons: Identify the right partners to amplify the value

    Telcos can encourage either rigid or fluid partnerships based on internal dexterity. To foster future-ready alliances, they need to identify agile upstarts as well as incumbent players, rapidly grab and onboard them, and launch services quickly. Digital partner platforms can simplify the process of partner recruitment, clarify what tools, networks and channels are available, how to leverage existing platforms when pushing new services and products, and educate partners on how to realize value through deep customer engagement jointly.

    Verizon and Google symbiosis

    In 2013, PwC predicted that telcos could become aggregators of cloud-based enterprise services because of inherent advantages of vast networks, data, and transmission infrastructure. However, it is only in the last couple of years that we are witnessing such partnerships bloom. Take the case of Google and Verizon. In 2017, Verizon moved 150,000 of its own employees to G-Suite. After a year of trialing, Verizon migrated another 100,000 workers to the platform. Today, Verizon is an active reseller of G-Suite, using its own 4G LTE network to deliver workplace productivity and collaborative tools to enterprise customers. Verizon and Google are taking it one step further now through the introduction of YouTube-TV to Verizon individual customers in a platform-agnostic manner.

    Lessons: Deepen partnerships for sustained revenue

    It is not just about on-boarding new partners but discovering ways to keep the partnership profitable. As telcos innovate, so do their partners. Thus, any partner management solution platform must have transparent and precise ways to score partner performance. It should also simplify partner access to multiple verticals through reusable partner profiles and partner data. This is crucial to deepen relationships by visualizing how new partner products can integrate with the telco value chain, create synchrony between pricing and business models, and co-innovate for specific use cases.

    Why digital partner management solutions are important

    Having worked closely with telcos, Subex knows that partnership is the way forward, especially as CSP revenues from traditional services dip. However, on their own, telcos do not have a reputation of digital mastery and are often limited to national markets instead of global ones. Thus, while smart and long-lasting alliances is the way ahead, the right partner management system is how CSPs can get their foot in the door to unlock unprecedented value. In fact, TMforum reports that “industrial automation, remote monitoring, digital health, smart grid, smart city, and connected vehicles collectively represent a $1 trillion market by 2023.” Clearly, CSPs must evolve their digital partner ecosystems – quickly – to exploit this opportunity.

    Digital partner onboarding is the first step to successful collaboration.

    Find out how Subex can help

  • What is a Microservice Architecture? Why is it important now?

    What is a Microservice Architecture? Why is it important now?

    We have been building software applications for many years using various tools, technologies, architectural patterns and best practices. It is evident that many software applications become large complex monolith over a period for various reasons. A monolith software application is like a large ball of spaghetti with criss-cross dependencies among its constituent modules. It becomes more complex to develop, deploy and maintain monoliths, constraining the agility and competitive advantages of development teams. Also, let us not undermine the challenge of clearing any sort of technical debt monoliths accumulate, as changing part of monolith code may have cascading impact of destabilizing a working software in production.

    Over the years, architectural patterns such as Service Oriented Architecture (SOA) and Microservices have emerged as alternatives to Monoliths.

    SOA was arguably the first architectural pattern aimed at solving the typical monolith issues by breaking down a large complex software application to sub-systems or “services”. All these services communicate over a common enterprise service bus (ESB). However, these sub-systems or services are actually mid-sized monoliths, as they share the same database. Also, more and more service-aware logic gets added to ESB and it becomes the single point of failure.

    Microservice as an architectural pattern has gathered steam due to large scale adoption by companies like Amazon, Netflix, SoundCloud, Spotify etc. It breaks downs a large software application to a number of loosely coupled microservices. Each microservice is responsible for doing specific discrete tasks, can have its own database and can communicate with other microservices through Application Programming Interfaces (APIs) to solve a large complex business problem. Each microservice can be developed, deployed and maintained independently as long as it operates without breaching a well-defined set of APIs called contract to communicate with other microservices.

    What are the advantages of Microservices architecture?

    1.Development Agility: As large complex software applications are decomposed to a number of small microservices, each microservice can be developed by small “one-pizza” or “two-pizza” teams independently. The teams can plan, build, deploy and maintain their microservices independently as long as the service boundaries are well-defined, and no breach of contract is ensured. The teams become more responsive to changes.

    2.Technology Stack Independence: In a typical monolith, the constituent modules have “compilation or build dependency” and share capabilities as “libraries“. Any changes in one module triggers compilation and/or linking of the whole software application. So, it basically implies and mandates to standardize the technology stack (such as programming language, database etc) to develop a monolith software application.

    In contrast, a microservice can be developed, deployed and maintained as a small independent application without any compilation or build dependency with other microservices. Its dependency with other microservices is handled through well-defined APIs or contract. It means that each microservice can be developed by choosing any technology stack (programming language, database, etc) best suited to realize its functionality instead of being required to take a more standardized, one-size-fits-all approach.

    3.Managing Technical Debt is easier: Let us say we find that one microservice is not vertically scaling in production. We can localize and fix the issue by modifying specific implementation part of that microservice code. Alternatively, we can choose to rewrite the microservice with a different technology stack best suited for meeting the vertical scaling goals. In either approach, as long as the consumer contracts the microservice shares are upheld, the development team is free to change the implementation without impacting the larger system.

    4.Improved Fault Isolation: In case of an error, a whole monolith application can crash and disrupt business as usual (BAU). However, microservices architecture offers improved fault isolation whereby in the case of an error in one service the whole application doesn’t necessarily stop functioning. When the error is fixed, it can be deployed only for the respective service instead of redeploying an entire application.

    For example, in a typical eCommerce application, let us say, the “product review service” is down; as a result, users will not able to read product reviews or write new reviews. But it has not impact on other services such as “product catalog management service”, “order management service” etc; as a result, users can still search, add products to cart and check-out.

    5.Scaling of Hot Services: Once developed, microservices can be deployed independently of each other. It helps in identifying “hot services” and design to scale them independent of the whole application. Similarly, microservices architecture enables auto scaling of different services resulting in optimal usage of precious computing resources. In contrast, a monolith hogs computing resources, as the whole application keeps running even though some modules are dormant.

    What are the limitations of Microservices architecture?

    If not designed carefully, microservices architecture also can become complex. Hence, certain design principles and practices must be considered.

    1.Boundary of Microservices: We need to be careful while defining boundaries of microservices. They should neither become monoliths nor be so small that it results in exchange of large volume of API calls, choking the network and degrading the performance of whole application. Domain driven design helps in defining the boundaries. Also extend Robert Martic C’s “Single Responsibility Principle” which states – “Gather things together that change for the same reason and separate things that change for different reasons”.

    2.Build and Deploy: Once boundaries are defined, a microservice should be built by a single team that can decide the best technology stack to realize its functionality. The dependencies among microservices are defined through well-defined APIs or contract. Ideally, they should run perfectly as long as the contract is upheld. However, the ideal scenarios are far off from ground reality. APIs or contracts evolve continuously in the form of different API versions and it may lead to break down of the application. Hence it is essential to setup CI/CD pipelines with any of the available CI servers (like Jenkins) to run the automated test cases and deploy these services independently to different environments (Integration, QA, Staging, Production, etc)

    3.Monitoring and Logging: Microservices are distributed by nature and monitoring and logging of individual services can be a challenge. It’s difficult to go through and correlate logs of each service instance and figure out individual errors. So, it makes a lot of sense to implement a common “log & statistics aggregation service” to monitor and control all the microservices centrally from a control panel.

    Microservices architecture and Agile software development have a lot in common.

    Just as microservice architecture is frequently defined in contrast to monolithic architecture, the agile software development approach removes the overhead and risk of large-scale software development by using smaller work increments, frequent iterations, and prototyping as a means of collaboration with users.

    Agile software development with continuous delivery, devops culture and microservice architecture are all bound by a common set of goals – to be as responsive as possible to customer needs while maintaining high levels of software quality and system availability; in other words – to be agile.

    Have you built software applications using microservice architecture? Have you decomposed a large monolith to a number of microservices? If so, please share your challenges and best practices in the comments.

    Looking to leverage technology effectively to thrive in the digital era? Our CTO focused set of solutions is what you need.

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  • How to build trust in your wholesale business and move beyond plugging leakages

    How to build trust in your wholesale business and move beyond plugging leakages

    Telecom fraud continues to be a major problem for telcos across the globe. With technological advances, fraudsters are rapidly changing their patterns and finding new ways of committing fraud. According to the 2019 CFCA survey, telcos are losing approximately USD 14 billion in total through IRSF, Arbitrage, Bypass, Traffic Pumping, and Wholesale SIP trunk frauds. With the advent of OTT services, we have seen a steep decrease in international voice traffic. The annual TeleGeography reports show that international OTT voice traffic reached 1 Trillion minutes in 2019 as compared to 432 Billion minutes of international carrier traffic. The research shows that international voice revenue has declined from $99 billion in 2012 to $60 billion in 2019 and could fall more up to $50 billion by 2024. There is almost a 50% decrease in wholesale revenues in the last decade.

    The need of the hour for a wholesaler is to take the lead and protect their telco partners for any fraudulent activity and reduce losses due to fraud. Wholesalers need to build their business strategies around risk focusing on trust other than leakages.  These leakages may not have a direct impact on them as fraudulent traffic is not necessarily a revenue loss for a wholesale service provider but is a direct loss to their telco partners and the call originator. Fraud majorly impacts the retail service providers.

    Here are some of the best practices that wholesalers can follow and have a strong partnership with the telcos:

    1. Be equally responsible for fraud: The wholesale service providers should monitor the traffic keeping in mind their telco partners and treat fraud as common problems to build trust in this partnership.
    2. Maintain a risk matrix: Maintain a risk control matrix, including traditional and non-traditional risks, and have a mitigation plan to reduce the impact of fraud on telco revenues.
    3. Dispute management: Though telcos remain liable for the traffic sent but to handle the disputes, wholesalers need to keep track of the deadlines and timeframes for the commercial terms of the agreement to avoid conflicts and ensure timely payments.
    4. Share information with telco partners: Fraudulent data can be interchanged between Wholesalers and Telcos. This information can help to bar B party fraudulent ranges and also highlight the A numbers dialing the fraudulent traffic to telco partners. Telcos can either block the services or handle blocking requests from retail customers and apply a monetary threshold for barring.
    5. Use advanced data analytics:  In a few cases, individual call detail records (CDRs) may look normal. However, in aggregate with complex statistical analysis and leveraging machine learning on training data, wholesalers can detect anomalies and uncover both known knowns and Unknown unknowns.
    6. Risk scoring: Define a risk score for supplier validation, global routing plan, and commercial rate sheets to build a trustful network.

    Wholesalers have access to massive volumes of traffic. Being the nervous system of interconnect, connecting operators across the globe, they are in the best position to scale up and improve the customer experience for the telco partners.  We have observed that customer dissatisfaction, which leads to churn, ultimately leads to a reduction in traffic. It is time for wholesalers and telcos to work as a team and build a strong partnership on trust.

    To understand the need of building trust in your wholesale business.

    Schedule a demo

    harmeet

    Harmeet is a Director- Business Solution and Consulting Group at Subex. He has over 14 years of  experience in Business Development, Solution Design, Consulting, IT Operations, OSS/BSS product implementation, and delivering customized as well as in-house client solutions to large and small to medium scale Telcos in the APAC and Europe region. He is responsible for Pre-Sales for Subex Portfolio around Digital trust.

  • How CFOs in Telecommunications can increase their productivity and lead the change

    How CFOs in Telecommunications can increase their productivity and lead the change

    On reading articles about new Chief Financial Officer (CFO) appointments, we often see directorial boards or CEOs remark that they needed “a strategic finance officer” or someone who can “partner with key stakeholders to define strategy.”

    With data and risk falling under the prerogative of CFOs, these executives are emerging as key value drivers within organizations. A study conducted by TCS in September 2019 found that the top 5 areas where CFOs want to invest their energies are:

    • Technology-enabled business model evolution/transformation
    • Capital allocation for digital transformation initiatives
    • New product and service development
    • Non-traditional risk management
    • Enterprise-wide analytics, data and talent governance

    Comparing this with an earlier study on CFO goals in the telecommunications industry, it seems like not much has changed in the past 4 years. In 2015, the strategic goals of CFOs in the telecommunications industry were listed as:

    • Gain deeper insights into customer needs as well as P&L drivers
    • Reduce costs from manual business processes
    • Accelerate speed through innovative business models
    • Achieve operational excellence with timely quality business information
    • Use technology to improve service delivery and profitability

    In reality though, as a Deloitte survey reveals, CFOs spend 60% of their time on traditional finance operations including managing revenue, accounting, internal audits, and compliance.

    Clearly, transitioning from an operational controller to a leader who drives strategic vision within the enterprise calls for wide-scale alignment between people, processes, and technology.

    Here are some ways I believe CFOs can reduce operational bandwidth and shift their focus to leadership tasks:

    1. Improve data governance

    Revenue and cost reporting are critical in all organizations, and these depend largely on the existing data sets within the enterprise. Every CXO must trust their own data, which requires strong data governance. This could include unifying data within large data lakes, cleansing data, and making it easily and securely consumable in reports. Based on one of their own surveys, the Japan Association for Chief Financial Officers (JACFO) argue that there is a strong link between profitability and structure within CFO organizations. CFOs who participate actively – whether by analyzing return on investments (ROI) or product profitability and making decisions on retiring non-performing products – contribute to increasing the top line.

    2. Create a comprehensive risk catalog

    CFOs need a well-defined risk catalog for their revenue models, which must also be non-traditional, to adapt to dynamic trends. This can include auditing irregularities, inaccurate rating and billing, and measuring risk around digital transformation. Such a catalog should capture risk comprehensively and define plans for mitigation, business continuity, and continuous improvement.

    3. Infuse support across the organization

    As CFO roles evolve from revenue reporting to financial planning and analysis, (predictive as well as prescriptive), they are shifting from enabling revenue assurance to delivering business assurance. Thus, financial officers find themselves reaching into areas like customer experience, marketing, sales, and network profitability. It is no longer enough to simply update costs at high level, rather they need to track revenue and distribute the indirect and direct cost to the end consumer to define the customer’s profitability or margin. For example, access to ARPU data will help CFOs better validate customers. Tapping into revenue from a user receiving many incoming calls may generate more margin than, say, a customer making high value recharges or paying high rental but without any usage.

    4. Use technology and automation

    The adoption maturity of AI/ML technologies among CSPs remains patchy. For instance, while there is deep penetration of AI in revenue forecasting, some CFOs still grapple with Excel sheets and lack basic automation like reading invoices, performing credit and debit checks, etc. The absence of workflow automation can deeply impact productivity, especially in situations like the current COVD-19 pandemic when remote working and email communication can cause poor versioning, data duplication, etc., leading to higher inefficiencies.

    Despite having the right talent and vision, CFOs may continue to struggle to progress beyond stewardship roles as long as they lack the right tools and technologies.

    Subex’s consulting and advisory services can help CFOs align people, process and technology through strong capabilities around data governance, risk mitigation, cross-functional transparency, and more.

    Looking to transform your finance organization into an agile and strategic one?

    Schedule a meeting with us

  • Telecom operators are well poised to aid in fight against COVID-19

    Telecom operators are well poised to aid in fight against COVID-19

    These are challenging times for the world as the COVID-19 pandemic has claimed 350,000+ lives and infected more than 6 million people. There is a significant amount of economic loss as the majority of the business activities were stopped. Although government authorities have implemented various measures such as enforcing lockdown, travel restrictions, and more. Despite all the reassuring measures, there are undeniable challenges in enforcing these restrictions at scale, especially in densely populated zones. It negates the purpose of these measures and increase the risk of a rise in infection. Watch the video to know how operators can support the government in their fight against COVID-19.

    Register for our on-demand webinar to know what operators can do to overcome the uncertainties unfolded by COVID-19 to enhance customer experience and profitability.

    Register webinar

    This article was originally Published at Analytics India Magazine.

  • Business Assurance is a framework aggregating traditional, new risk and assurance disciplines

    Business Assurance is a framework aggregating traditional, new risk and assurance disciplines

    Telcos have traditionally used Revenue Assurance to find leakages and track revenue streams. However, the rapidly changing telecom assurance space requires more. With digitalization greatly increasing the overall risks for telcos and, the pressing need to succeed in today’s multi-service and multi-disciplinary services world there is an imperative need for telecom assurance to shift beyond revenue-only assurance (whose primary focus is an emphasis on EBITDA) to Business Assurance.

    Business assurance integrates assurance and risk management disciplines into an overall proactive data-centric assurance framework, intending to continuously protect and improve financial integrity, while also enhancing business value and customer experience.

    Embracing Business Assurance—an urgent need for telcos

    Business Assurance growth is estimated to rise to $64.4 billion by 2025 as opposed to Revenue Assurance growth which was at $2.9 billion in 2019. In the present ecosystem—with a complex, digital economy consisting of partners and OTT players—telcos have expanded into the realm of content providers offering entertainment streaming, current affairs consumption, shopping, and many more options.

    In this context, incorporating Business Assurance can play a vital role for several reasons: increased market growth leading to increased risk factors; the mandatory need for real-time controls; key risks that arise from a lack of adequate business process controls of business processes; additional control requirements due to the increased emphasis on privacy, security, and regulatory enforcement; the scope of Revenue Assurance being restricted to small business silos; and the progressively complex multi-service products being offered by telcos.

    All of the above points an urgent need for a framework that functions proactively in the value chain. It also helps to ensure agility in digital transformation projects and encourages accountability. For telcos currently on a digital transformation journey Business Assurance ensures end-to-end monitoring systems and serves as a second line of active security for a very complicated digital environment.

    Business Assurance can transform billing and revenue management and product performance

    Business Assurance enables telcos to bring a significant difference to billing and revenue management and product performance.

    The role of billing is becoming increasingly important as Communication Service Providers (CSPs) try to build greater value and loyalty through partnerships and customer relationships. The fierce competition between CSPs and the challenge from over-the-top (OTT) digital service providers means that the opportunity to deliver multi-service bundling with cross-product promotions and rewards, all on a single invoice, is now a vital success factor.

    Similarly, optimal product performance is vital for telcos to compete effectively. Improving customer service requires better management of telecommunications efficiency through efficient network metric collection and monitoring. Also, decision-makers require effective monitoring to help successful strategic network investment decisions. Growing traffic and the introduction of advanced monitoring technologies mean that there is an immediate need to process vast quantities of data in near real-time.

    Business Assurance assures multiple benefits

    The adoption of a Business Assurance strategy would produce tangible business results for communications service providers (CSPs) in the short and long term. Telcos can gain from

    • Providing amplified customer service – by expanding assurance coverage to the whole consumer experience journey. This new approach to ‘business care’ will have a positive impact on ‘customer care’ by ensuring greater consistency in CSP interactions with their customers.
    • Reaching new levels in digital maturity – introducing Business Assurance puts physical and logical assets under one umbrella that is critical to success in the age of the Internet of Things (IoT) and digital services, where end-to-end integration is central.
    • Increasing trust – implementing Business Assurance creates faith by providing veritable results to consumers and partners. In turn, it helps CSPs manage risks and threats to their company in an expanded environment by concentrating on financial integrity.
    • Building momentum within the internal community – increasing team morale and improving success in different areas by setting higher goals.
    • Achieving real and measurable economic profit –finally, Business Assurance enables CSPs to quantify their success through profits.

    Business Assurance is the way forward

    CSPs have been at the forefront of incorporating emerging technologies in their roadmaps and are ready to deliver innovative assurance solutions as new technologies allow. While it is true that it will take time to create a rich and reliable Business Assurance system, incorporating such a system is definitely the way forward.

    On-Demand Webinar- Managing Business Risks: The Growth of Business Assurance

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