Testing

Tag: Analytics

  • How to save millions in Network Capex?

    How to save millions in Network Capex?

    Effective capital expenditure and network asset management is rapidly becoming a big boardroom issue for telecoms operators in recent times. With decreasing EBIDTA and ever increasing pressure on margins, operators can no longer afford to keep on spending heavily on capital assets and network projects with no questions asked. Yet telcos are not managing their assets adeptly, and this is something that senior management needs to address with some urgency in order to control their capital expenditure more effectively and efficiently.

    At the crux of the problem is the unfortunate reality that operators don’t have an accurate picture of what assets and network inventory they already own, let alone how these assets are being used. These problems have prompted Subex to launch ROC Asset Assurance, a service that combines inventory management with workflow and analytics so that operators can gain visibility into the complete asset lifecycle through dashboards, KPIs and reports. The solution is different from asset management services because of its workflow and analytics elements. It can initiate workflow to ensure that all the applicable assets are procured and deployed when needed. All of which means that operators should be able to and reduce their capital expenditure and manage the capacity needs of their networks with greater precision

    Here is an interesting and exciting short video on ROC Asset Assurance which provides you better understanding of the Asset Assurance space, the problems and challenges faced by global operators and how ROC Asset Assurance can help them manage and reduce network capex.

  • Managing Product Performance Using Advanced Analytics

    Managing Product Performance Using Advanced Analytics

    It seems there is an endless array of products being offered to mobile customers every day.  Prepaid plans, postpaid plans, new bundles, new handsets, movement to services on tablets, increase bandwidth offers for data streaming, etc.  As operators load the consumers up with more and more choices, understanding what is being bought, who is buying it, is the product making money, when does it start making money, will a new plan rob my bottom line accidentally, etc, are all questions operators must be able to answer, and on an almost daily basis.

    As an operator, you should be asking a series of daily questions that need to be answered to protect your business and identify significant revenue opportunities and risks:

    1.  What are your product margins today?  Which products are generating positive returns?  Which programs are missing their business plan?  By how much?

    2.  When a new product is proposed, do we understand what the impact will be from our existing customers that migrate from their current product to this new product?

    3.  When a competitor responds to your new offer with a comparable offer, how fast can you see that impact to your market?

    4.  What is the right product offer, the right price, for the right customer segment, in the right section of the network that we should be focusing our marketing resources around?

    Many of these questions require much, much more than simple metrics and reports generated by mining activities.  They require advanced analytics that allow operators to predict behaviors and successes (and failures) before those events come to pass, and most importantly, before budget and resources are focused in the wrong areas!  Advanced analytics offer predictive, detailed analysis about what an operator should do, why they should do it, when they should do it, and what the expected outcome should be.  Unlike simple forecasting, analytics is based on science, and offers a proven methodolgy for establishing successful strageties in the business in a near real time environment.

    ROCware Product Performance Management (PPM) addresses these (and many other) issues, in a near-real time, high performance analytics environment.  Using ROCware’s advanced analytics technology, PPM is able to provide operators with a full view of their products, margins, and a highly robust future-view into margins, customer and product behaviors, and many other factors that are crucial to properly track and manage your product portfolio.

    It is very common today for an operator to have access to limited product data.  Looking more closely at what the operator actually does have access to, it is really often based on excel spreadsheets, with limited information around sales metrics, with some segmentation.  This is not set up as a repeatable process, and thus the requests are completed manually, and most commonly with 4-6 week latency.  This doesn’t help an organization understand margin and profitability; it doesn’t help an organization understand competitive impacts, or the impact of a proposed plan on your existing market.  ROCware PPM does all of this automatically, every day.

    There was a time when a monthly performance report was adequate.  That was before products became far more complex, and competitive pressures were measured in days (sometimes hours).  An operator without daily views into their product health and threats is vulnerable to risks that could cost the business millions of dollars in just a single month.  ROCware PPM, along with the other ROCware suite of advanced analytics solutions, takes this worry off the table, allowing operators to quickly and confidently mange changes in their business.

  • Traditional Capacity Management is doomed to fail and cost CSPs millions in unnecessary CapEx!

    Traditional Capacity Management is doomed to fail and cost CSPs millions in unnecessary CapEx!

    Most CSPs today adopt a traditional capacity management approach that consists of planning their network resource requirements over the next 12 months based on past consumer trends.

    Reality check! In today’s fast paced end-user consumption and service demand, trying to predict resource needs 12 months out based on past end-user behavior is like playing a lottery based on past outcomes in hopes to hit it big – More often than not you will lose big time.

    The reality is that the past doesn’t predict the future anymore and that end-users are causing unpredictable shifts in resource consumption in the network as they tune into major events and build their life around real-time communications. Oh sure, many CSPs will read this and think, we have the latest probes in the network giving us loads of real-time data and complex flows where we know if packets are traveling left or right in the network. And yet with all this information CSP still can’t keep ahead of today’s data tsunami, without being concurrently choked by escalating CapEx. Having loads of low-level information more often than not causes data overload: You have so much raw data that you don’t know what it means from an overall congestion perspective without weeks or months of analysis.  Or even worse, you may interpret trends differently depending on the data sample you examine, making it virtually impossible to project congestion and business impacts. Many CSPs with whom I have spoken face the same problem: When in doubt, pour more CapEx into the network, in the hope of adding the right resources to alleviate congestion.

    What if there is a way to more precisely target the CapEx spend needed in the network, to deliver the services the CSPs need to thrive?  In fact, there is, and it’s called “Real-time Capacity Analytics”!

    Real-time Capacity Analytics is about understanding all capacity-related data rather than looking at it on a per attribute or device perspective – which provides little more clarity than just a blip in an ocean of traffic – instead looking at capacity consumption as it relates to end-to-end path and services to end-users. It is amazing how CSPs are concerned about how capacity congestion affects their subscribers and yet most solutions today fail to look at capacity from an end-to-end end-user perspective. Without an end-to-end view and understanding of how different segments of the network path and services affect congestion, CSPs may be spending CapEx in portions of the network that may temporarily relieve the symptoms of congestion rather than resolving the root cause.

    So as a CSP, the next time you face customer impact based on congestion, ask yourself: Did I see it coming? Did I get the right warning signs that congestion was building up over time? Did I get a read of time to exhaustion that could have helped me plan added capacity before impacting my customers? Is my solution pinpointing where to target my Cap Ex? And finally, do I have a solution that can tell me if my network can accommodate new subscribers or services, and, if not, where will the congestion hot-spots occur and how much Cap Ex is needed?

    If your current solution isn’t helping you answer any of the above questions, it is time to consider Real-Time Analytics for Capacity Management before your business gets swept away by the tides of capacity congestion.

  • Understanding customers is really a two way street…

    Understanding customers is really a two way street…

    Organizations are racing to understand customers for a variety of reasons, but the most prevalent is that their markets are increasingly saturated, and they need to protect their base.  Concurrently, there is a big push to go “steal” customers from their competitors, but let’s not focus on that for the moment…let’s talk about understanding customers.

    As an organization that provides both services and products, a typical telecom operator has really keyed in on “understanding their customers” and “customer experience management” as buzz words and phrases that have led to initiatives that include developing 360 degree views of those customers.  This has created focus on things like demographics, segmentation, billing history, purchase history, contact history, service usage analysis, etc.  These are certainly important factors, and they do help paint a picture of that customer to the operator.  But the perspective is often skewed, in that the operator is looking at the customer from the operator’s point of view.  Instead, to better understand the customer and their experience, the operator needs to adapt their approach to the customer’s point of view.

    While this customer point of view strikes many as common sense, it still remains an elusive paradigm.  Quite simply put, the operator needs to understand how the customer perceives the operator has treated them.  How has the network quality been for that customer?  How many unfavorable events has that customer had to experience (dropped calls, failed downloads, etc.)?  On top of the number of times the customer has contacted, how many times did they contact before an issue was resolved (not to mention hold times in the queue with each episode)?  These and other factors contribute to truly understanding how the operator has treated the customer, which then helps better explain why a customer may be considered a promoter or detractor.

    Why is understanding customers a two-way street?  It is because two components make up that understanding:  The behavior of the customer, and the behavior of the service provider.  Two points of view that often result in entirely different outcomes than predicted by looking through a single lens.

  • Could Analytics Pose a Life Threat to Large Tier Operators?

    Could Analytics Pose a Life Threat to Large Tier Operators?

    As we dive deeper into the world of analytics, more and more information and intelligence is being made available to operators, analysts, and other interested parties. But along this same progression of “seeming” advancement in the domain, there is also a growing, critical threat to large tier operators: Access.

    Access itself is a big word. In the context above it simply means “access to analytics intelligence”. But the other meanings are where the problem exists for larger tier operators. When you think of “access” in terms of these areas, the problem becomes more clear:

    1. Access to where your data is located – where is it? All of it?

    2. Access to the group(s) who control that data – can you get to those people? Do you even know who they are?

    3. Access to the data itself – assuming you know where it is and who owns, can you even get to it?

    As history proves out again and again, these sets of questions become increasingly harder to answer as the size of the operator continues to get bigger. Taking the approach of a larger vs. a smaller operator, here’s what is all-too-often the case:

    Where is the data? In smaller operators, data tends to be located in singular systems. Put more simply, they don’t have 4 billing systems for retail customers, 3 inventory platforms, and 6 order entry instances. They tend to be closer to one of each type. Obviously the younger the operator, the more advantages they have as well, simply because infrastructure and architecture are more simplified. If they are a larger tier operator, however, they are often quite the opposite. In addition to having multiple systems that are redundant or duplicated, they often have customers scattered across these instances with no particular rhyme or reason, as several system consolidation events have spread any given customer’s account in unusual directions. One of the biggest challenges in this regard is getting to a true “single view” of an account. Some of the largest carriers in the world have tried to get there – they remain unsuccessful, and ironically, they often don’t even realize it themselves.

    Who controls the data? So let’s say you know where your data is. Can you get to it? Do you need a budget available to *pay* the internal group to get you your own data? But most critically: When you show the data owners what you need and why you need it, can you successfully escape the attempt by those owners to create the analytics for you? Many of these shops that own data also have lighter-weight analytics capabilities. They would love for you to engage them to build something for you. They would love for you to pay their department for their efforts. They would love for you to not use an outside expert, as this is often *perceived* as a direct threat to them (this is NOT the case, but they still respond like it is). They would love for you to educate them on what you want and how to do it. But most importantly, they’ll sometimes make getting your data so difficult and expensive, that it becomes more financially beneficial to just do it their way. This never gets you the result you expect, on time, anywhere close to budget.

    Contrast this with a smaller operator. The smaller the operator, the more collaboration takes place. That’s not a judgement…it’s just the unfortunate truth (for the large tiers). Of all the operators I’ve worked on project deliveries directly with over the years, this has held true each and every time. When a finance guy needs a data dump from the accounting platform, he doesn’t make a phone call to a different region, through a path of 3-10 people to get to the right person. What he does do is walk down the hall, around the corner, into the IT guy’s office, sits down and has a few minutes of “small talk”, asks for the data at the end of those few minutes, and usually has it by the next day. Also – smaller operators don’t believe they can do it all. They already wear 10 hats during the day, and welcome an expert team in analytics to help drive immediate value back into their business, almost form the day they walk in the door.

    The simple fact of the matter is this: Analytics is going to be a crucial differentiating point for operators’ speed (agility) of response to business and market changes. And when you cut to the chase, data access will make this practice virtually impossible for large tier, legacy operators to fully leverage when compared to the smaller organizations. This will cripple many large operators in some key, lucrative segments of their markets. How can we be so sure? It’s already happening…

  • A Thorn in the Side? – Handling Over-the-top Content Demands

    A Thorn in the Side? – Handling Over-the-top Content Demands

    As I interact with more and more service providers about their network capacity issues, I’ve become sure about one thing – what worked before, isn’t really working anymore.  The CapEx requirement for network equipment just to keep up with the exponential growth in data traffic (i.e., Data Tsunami) is still not getting them ahead of significant congestion issues and customer impacting events.  Why? Traditional capacity management paradigms are not working.

    Essentially, feedback from carriers of all sizes and types has exposed one of the most significant shifts in thinking regarding how to go about managing and planning for network capacity.  They know that the rules are all changing and today’s content demands are outpacing the CSPs ability to keep pace.  The first key question is how to get back in front of the capacity demand (we’ll talk about monetization next…stay tuned).  So, why aren’t today’s processes scaling?

    • CSPs use a multitude of human resources and manual processes to manage network capacity.   This may have scaled under slower and more predictable capacity growth curves, but thanks to services like You-Tube & Netflix, entire network capacity is shifting in quantum leaps.
    • Solutions provided by equipment vendors are often platform specific, and reinforce a silo approach to Capacity Management when a holistic view is needed.  Service demand congestion is a network phenomenon which doesn’t care about individual equipment vendors or devices.
    • CSP planning groups leverage data and make decisions based on systems which have 20 – 40% inaccuracy in comparison to the actual capacity availability in the network.
    • Today’s CSP solution approach is often homegrown where 90% of the time is spent on acquiring and understanding raw data.  As a whole, everyone is trying to answer the question of how to proactively eliminate the possibility of congestion, but most are still focused on addressing the symptoms and not preventing the problem

    It is surprising to note that even top tier/technology leaders cannot accurately predict where and when capacity issues will impact their networks.  This lack of visibility hurts CSPs considerably because as per our own studies, network events are behind can account for up to 50% of customer churn in high value mobile data services.

    And the Capacity Management problem doesn’t really end there; in many ways it’s like a supply chain process. Marketing owns the function of forecasting where service uptake will drive capacity needs across the network. When Marketing underestimates service uptake, there is a real and significant impact to potential revenue: On average, it can take about 3 months from when capacity is fully tapped in a Central Office (CO) to when new capacity can be added to your network.  During that time, customers expecting service availability become hugely frustrated and begin to churn.  Engineering groups are pushed into panic-mode, trying to react as fast as possible – often putting capacity in the wrong places due to inaccurate data – resulting in further congestion, service degradation, an inefficient use of capital.

    The message from CXO’s is crystal clear – there is an urgent and dire need to find new ways of monetizing the data crossing their networks. This need is exacerbated with OTT content and net-neutrality. SLA and authentication based revenue models are absolutely dependent on knowing what types of content/services are traversing your network, how much capacity they consume, and how utilization is driven by your consumer’s interests and activities.  This type of analysis requires a critical and intelligent binding of network and services data with business data to truly assess the financial impact to the CSP. Many Business Intelligence (BI) solution leaders will lay claim to abilities here, but actually fall very short of the mark.  Instead, real experience suggests that solutions in the marketplace today either:

    • Can handle the financial aspects of your business but have no understanding of today’s network dynamics in terms of capacity issues and services;
    • Can handle parts of your network very deeply, but do not correlate or provide a holistic view at the service level; or,
    • Can collect some network and service level information, but have no ability to incorporate business data to understand the impact to the business – i.e,. cost, subscriber behavior, propensities

    All the above challenges bring us to the inevitable question – what kind of approach does one take in order to tackle capacity management issues? How does one stop chasing traffic and focus on flattening the CapEx curve instead? In order to attain ‘Capacity Management Nirvana‘, a proactive and scalable approach needs to be adopted by CSPs. An approach which not only intelligently binds network and business strategies based on the Data Tsunami realities but also brings proactive and predictive capacity management to the table. At the end of the day, a CSP should have access to all their capacity, the ability to leverage real and immediate feedback on the change in capacity as service uptake increases, and finally, the right tools and intelligence to get in front of what’s coming.

    To know more about how a Capacity Management solution can help you address the above issues, download the whitepaper “Energizing Smart Growth with Network Intelligence“