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artificial intelligence

For today’s operators, the buck doesn’t stop at merely ensuring profitability and retaining customers. The advent of over-the-top players, rapidly shrinking average revenues per user, et all, have changed long-standing priority lists. Now, the order of the day is to tread the path from communications service providers to digitally-driven entities.

However, here’s the tricky part-this process isn’t confined to building up future-ready networks. A rather vital part is to ensure existing business support systems (BSS) are equally up to speed. For good reason too-these systems manage information pertaining to customers and products, not to mention, collecting revenue.

In this context, Ovum has predicted that annual revenues for the global BSS market will grow from $17 billion in 2017 to $22.5 billion in 2022, a CAGR of 5.8 per cent. Drilling down further, overall growth in the market will be driven by the uptake of more vendor services, which will grow at a CAGR of 6.8 per cent, to $17.1 billion in 2022. Within the services domain, the need for managed services and accelerated growth within SaaS will drive revenue growth for BSS.

The bottom-line is this-operators ought to gear up for the digital telecom space of tomorrow. An important part of this process is investing in digital BSS stacks that are efficient, scalable, flexible and collaborative. However, operators would do well to remember that digitally transforming existing systems and infrastructure isn’t an overnight process. A piecemeal, gradual approach is best suited to ensure maximum success.


As per industry analysts, today’s BSS stacks have miles to go, before being able to support any plans pertaining to digital transformation. Here’s why-these stacks have been around since operators were setting up their networks. In fact, over the years, these players have added a complex maze of legacy systems and processes. Needless to say, all entailed extremely limited functionality, which, of course, compounded the challenge.

Adding to the chaos is the fact that the sector itself is undergoing such turbulent times. As per Ovum, to maintain a competitive edge, operators are now thinking and planning beyond traditional key performance indicators such as revenue, profit margins, et all. Instead, criteria such as customer experience, efficiency, seamless product delivery, quicker time-to-market and agility have come to the fore. However, given the extent of legacy systems and processes, operators are unable to meet these parameters effectively. In fact, this is expected have a trickle-down impact on their other lines of business as well. For instance, managing scattered systems usually results in duplication of information, which in turn adversely impacts the time-to-market of several products. In a nutshell, revenue leakage, inaccurate billing and absence of an omni-channel customer experience are just a few of the challenges an operator will face with legacy systems.


Doubtlessly, digital transformation presents an ideal opportunity for operators to rebuild lost (or crumbling) market positions, innovate and prepare for the future. However, this is easier said than done. Merely planning to monetize next generation technologies isn’t enough. Operators ought to have a plan to leverage these to best suit all aspects of their business-including BSS.

For example, as per Ovum, artificial intelligence (AI) will enable an operator to improve customer experience and enhance the efficiency of various business processes. However, in order to leverage this to the fullest, these players ought to invest in making the IT systems within the BSS domain lean and agile. This can be achieved by consolidating systems and automating multiple processes throughout the revenue and customer management stacks.


By modernizing BSS systems, four key aspects of digital transformation come to the fore:

Going forward, telecom operators and vendors alike will continue to scramble to compete in an increasingly digital world. As a result, the former’s expenditure on IT and the latter’s overall revenues are likely to see an upward trend. To illustrate, as per Ovum’s most recent ICT Enterprise Insights survey, over 70 per cent of operators plan to increase IT spend over the next year, with 30 per cent planning to increase spends by 6 per cent or more.

January 14, 2020 0 comment
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Artificial Intelligence (AI) is certainly a popular buzzword in the industry today and according to surveys by TM Forum, many have now “deployed” AI and are looking to scale. But deploying AI means different things and it depends on the strategy adopted by the organisation for implementing AI.

For some, a proof-of-concept (POC) has been successfully executed and a model has been created that uses machine learning (ML) algorithms. For example, in the context of telecom customer value management (CVM), this is often around churn or decay prediction, and the date and value of the next recharge.

For others, it means the technical capability has been put in place to build, evaluate and execute ML-driven models across multiple use cases – our own AI at Scale platform is a case in point. It can be used for building, evaluating and running in production both CVM and non-CVM models.

Typically, the AI platform will sit on top an existing big data lake or incorporate a data fusion layer to capture data from multiple sources (covering real-time and batch, structured and unstructured data).

A fully functioning AI platform provides a graphic user interface (GUI)- based workbench for data scientists to rapidly build models using ML algorithms. It also supports the full lifecycle of model creation and execution; data ingestion, data exploration, feature engineering, model development and evaluation, and deployment and execution in production.

But, along with the technology challenges associated with scaling for AI, there are larger implications for the “people” and “processes” that go along with it.  Often, our clients will point out that one of the biggest challenges they face is the hiring and retention of key talent in data science. Understandable, really, as data scientists are in a “hot” market. This is why companies will often turn to partners to assist.

In fact, this people-centric factor is not only limited to the data science team. In the context of CVM, marketers need to adapt. It is one thing to be designing and implementing campaigns that are based on business rules created by a marketer; the criteria for the segment, and the offer to make to that segment. It is quite a different thing to be prepared to leave this to a “black box” solution.  This is exactly what a ML model is. It is next to impossible with a ML-driven model to determine exactly why a particular offer is made to a particular customer.

Therefore, a scaling strategy is required, that allows confidence to be built. For example, allowing the model to apply to a proportion of the base while the traditional business rules approach is applied to the remainder. When positive results are seen, then the proportion of the base that receives offers based on the model can be increased.

An ancillary consequence of this is that the robustness of the methodology for measuring performance is critical. There must be confidence that when an improved result is seen, it is trusted. This is why effort is required to ensure the universal control group (UCG) is highly representative of the base. Performance is measured as the difference between the UCG and the universal target group.

The “process” side is also extremely critical. This covers the governance and practices in place to ensure data integrity is maintained, and that data is updated when expected. Critically, it also covers those processes associated with putting a new model into production. This DevOps side is particularly challenging for many organisations because there are new practices to be developed. A ML driven model is not constant. By its very nature it changes while in production, which is very different from what we see as “normal” software deployment. In fact, this is so different that the TM Forum has a Catalyst stream in progress for “AIOps” to develop frameworks for supporting the operations of AI. Engage with this Catalyst programme to keep abreast with the thinking as it develops. And perhaps become a contributor.

November 28, 2019 0 comment
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Penning blogs on customer experience management (CXM) can get tricky after a while. The bottom-line of each piece is simple-a sound CXM strategy can make or break a business.Here’s the interesting bit, though. Equally important factors in the CXM game are the tools of the trade one opts for. There are, without a doubt, a plethora of options to choose from. But which strikes (or is likely to strike) the right note?

Permit me to point out, though, CXM isn’t a “one size fits all” solution. What may work for one customer may not apply universally. In the context of this blog, though, let’s focus on artificial intelligence (AI) and how it ensures customer experience monetization.

Permit me to start by restating-AI is not a generic solution. One simply cannot just implement AI-there are larger implications. While there is, indeed, a large amount of hoopla around AI, let’s not forget, there’s no field tested and proven solution for AI. Every solution, every use case that’s been built so far can only be improved, not replicated. If one chooses the latter over the former, well, they’ve merely limited the possibilities. Therein lies the nub of the argument-the field is yours to prove and implement.

AI and CXM: A Multi-Faceted Equation

There may be a million ways to address this point (and why not, don’t forget, all data provides some outcome!). A very straightforward approach would be thus-AI enables companies to ensure real-time decisioning. How? Well, the data is on hand. Customers haven’t really changed their patterns, except every decision is usually made “in the moment”. And apart from the fact, of course, that the sheer number of decisions has increased dramatically.

So, AI, in a nutshell, enables companies to inject predictability with a fair degree of accuracy whilst dealing with customers. The idea is to see if the likely short term future outcomes of a customer’s actions come to the fore.

AI-Based Use Cases That May Turn the Tide for Operators

As I mentioned before, countless use cases for AI (and indeed, any technology) exist. And are only becoming more intelligent, with the domain shifting constantly. Within the scope of the CXM domain, though, two primary use cases must be focused on, if one is serious about retaining customers, of course.


Leveraging AI Intelligently

This is, to be honest, a bit misleading. Here’s how-the very idea of introducing AI in the CXM domain is to enable operators to compliment the customer’s expectations. To be where the customer is. And so on, of course.

Now, that a clear set of priorities has been defined, the next step is to create a roadmap of how to intelligently leverage AI. Perhaps something on these lines..?


What’s crucial to remember is that AI is directional. Don’t mistake it for “artificial execution”-it can only do so much. It cannot address a challenge. It may offer a leaner, meaner structure for problem solving but one’s still got to execute the same, for best results!

On a parting note, permit me to put it simply, yet succinctly. Focus on breaking the clutter. Focus on customer retention and making the brand. Focus on AI as a tool in your arsenal, not the arsenal itself. For, isn’t the bottom-line providing an unforgettable customer experience?

May 3, 2019 0 comment
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If you look back at the history of technology you find that what we know is not often what will be.  What we forecast is likely though not often the result.  Only one can guess how many trillions of messages of all types will be sent and shared by 2020.  Forecasters say close to 3 trillion messages will be sent annually but if you look around the room and around the globe you only see the tip of the iceberg in user growth and use cases for messaging.  When I grew up we had one rotary telephone and one TV.  Now everyone has a TV in their hand and messaging types beyond your wildest imagination bombard you like hurricane.  Sometimes SMS, chat apps, videos, email, and realtime meetings are all occurring at the same time.  Looking forward even a little and beyond our traditional thinking, if digital transformation takes place in one industry can we not expect it to have a ripple or tsunami effect in many others. For example, take a look at Mahindar Comviva’s white paper on Digital Transformation and you will see an interesting view of the changing world of messaging.  In another sector, IoT-internet of things devices will also be sending messages to me and everyone else on their chat lists to let people know what is going on.  Are you beginning to see the compound or exponential growth in the world of messaging?  Looking at this from another angle that of a provider or operator there has been the long “race to zero” where once high fees were charged for SMS, long distance and other services are now free.  Now fee-based services are bundled together so the customer sees value in the overall benefit rather than any one feature or app.  New fee-based features may arise though the business models may vary whether subscription or advertising derived revenues can level the playing field or even give rise to sustained revenues.  Linkages with other business all aiming for the customer will be the new “middleman” in the mix.  That is, companies who link with other companies to message you about things you need or want.  Some are obvious like Amazon buying Whole Foods to bring all their other business to you whether you walk in the door, home delivery or even by drone in the future. At the same time there are tens of thousands of startups and others that are building unique and valuable solutions to solving specific problems with innovative technologies such as artificial intelligence, machine learning and wireless tech. I like to think that there is a new wave coming quickly of technology embedded literally in everything we buy including food, medicines, wearable and anything else that we do that will improve our lives in many ways we haven’t even thought about yet.  For example, we really don’t know much about what we eat and its impact on our health.  Yes, we know there are a lot of bad things but we don’t know conclusively if we eat one thing for twenty years that it will give us cancer or that it will help us live a lot longer.  There are so many medical issues that via IoMT-internet of medical things that may be able to improve our lives, give us more energy and less live-threatening diseases later.  I like the idea that my IoMT device will give me a warning or even a nudge when I eat something I shouldn’t or say that my weight-loss goals could be achieved if I did more of one kind of exercise than another.  You can now see the world of messaging goes far beyond us chatting with each other which opens up even more possibilities than I have time to discuss today.

September 11, 2018 0 comment
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