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October 2012

Margin Analysis: Bolting Profit Assurance onto a Revenue Assurance Platform

Margin Analysis: Bolting Profit Assurance onto a Revenue Assurance Platform

Margin analysis is one of the hottest trends in business assurance and telecom analytics.

As a product manager for cVidya’s soon to be released margin assurance module for our MoneyMap platform, I’d like to share some perspective and explain what the excitement is all about.

To be honest, while margin analysis is hot, it’s not exactly new.  For years people have been performing margin analysis using Excel spreadsheets.  And in the early 90s, margin analysis was also a key benefit of owning a data warehouse.

So why are these approaches less than optimal?  I think the answer is they either take too much time or the results are not granular enough.  In a data warehouse approach it generally takes a good 15, 30 or 60 days to fully collect and analyze the data.  That’s pretty slow if your objective to manage your business in real-time.

Excel spreadsheets are great for producing periodic monthly, quarterly, and annual financial reports, especially where the inputs are aggregate (“coarse grain”) numbers.  But as soon as product categories are mixed and you need to segment results across “fine grain” customer groups and geographic regions, the Excel model becomes clumsy.

The Problem with Spreadsheets for Margin Analysis

I’d like to delve into the spreadsheet issue a little more to better explain why the Excel approach breaks down with added complexity.

Pretend you’re CFO at a mobile operator.  The overall costs of supplying an on-net service like SMS are very well known to you.  But a problem arises when you consider a bundled service where SMS is mixed, say, with the services you are buying from third party partners.

For example, let’s say I have a plan of 100 minutes of voice calls, 500 SMSs, 10 movies, and 20 ringtones.  The first two services are internal.  I know my SMS cost and the average cost of a voice call being dialed to various regions of the world.  But let’s suppose the subscriber makes a call to Russia and the call is routed through several international carriers who bill you for usage.  A case like that is much harder to model in a spreadsheet.

The same goes for content services like movies and ringtones that I buy from another provider.  First of all, the cost of a one particular movie could vary across different content providers.  Not only that, the way the movie is sold may vary.  One provider may charge $2 a movie; another provider charges you $500 for an unlimited number of downloads of the same movie.

So through this example, you begin to see how hard it is model such complexity in a spreadsheet.

But in our forthcoming margin analysis product in MoneyMap, we’ve taken into account the fact that many service providers want to enter costs derived from another system.  So we include places in the system where you can post external costs, say a computed average SMS cost.  That’s fine.

But in calculating the margin of service bundles that are mixture of on-net and off-net costs — that’s where the value add of a commercial software solution comes in.

Why is Margin Analysis Important?

Let’s step back and ask a more fundamental question: why are operators eager to implement this newer and better approach to margin analysis?  Well, here I would point you to two industry trends: increased competition and service complexity.  Consider these issues:

  • Multiple operators have been licensed to compete in any given geographic region, so tweaking services to achieve maximum profitability has never been more important;
  • Over the Top Players in mobile broadband are not only eating scarce bandwidth, but are offering profit killing alternatives to mobile voice and SMS, such as VoIP and proprietary text messaging.
  • Revenue per Subscriber is decreasing, so to remain profitable, protect the business, and exploit opportunities, every carrier needs to have a margin analysis capability.
  • Bundling of services has exploded.  Indeed, many operators have become triple- or quad-play providers, making it harder to gain a consolidated view of margins; and,
  • Finally, Interconnect and Content Partners have expanded, underscoring the need to monitor these partners from a service profitability view.  Even a cable or satellite company need margin analysis because they buy external carrier services and content and need to better understand what they are buying.

The Benefits of Margin Analysis

Many departments at a telco will find margin analysis useful, but marketing and finance probably get the most benefit because it’s all about analyzing the profitability of different products and services.

With usage data collected from the network plus invoices to subscribers and from third party partners, we can analyze each and every subscriber and service.

Here are the most important uses of margin analysis:

  • Pricing Plan Analysis — Marketing  can analyze whether a particular pricing plan has a high or a low margin.  They can also analyze the usage pattern of customers using a particular plan.  Incidentally, the popularity of flat rate billing makes margin analysis doubly important because the rates you charge for the service are less subject to change.  If the usage of certain elements in the pricing plan go up unexpectedly, the service could quickly become unprofitable or even turn into a loss.
  • Service Hierarchies — Margin analysis can reveal the margins on different hierarchies of a service.  For example, if the service is international calls to Germany, you can also examine the margins on a sub-service — such as international calls to mobile phones in Germany.
  • Market Segmentation — Margin analysis allows you to look at customer profitability in great detail.  You can see the margins for subscribers living in a specific region or business customers in a particular industry.  And tracking service over a full year permits comparing services in different times of the year.
  • LTE Service Monitoring — LTE  basically changes how we think of a service.  For instance, LTE services have hooks to allow you to base pricing on the quality of service or bandwidth consumed.  Services can be enhanced during an actual session.  All of these factors will make it harder to analyze the LTE service margins.
  • Friends and Family Plans in Mobile Data — Verizon’s Share Data Plan collects and combines usage from multiple family members across 10 devices -iPhones and iPads for example.  The idea is the customer pays a flat rate such as $200 for 10 devices.  Well, a service like that requires constant vigilance to ensure it remains profitable because there are many variables in the service that could spike up costs.
  • Experimentation and Testing — Finally, since the uptake of particular services is getting tougher and tougher to forecast, being able to assess the margins of a product 24 hours after launch becomes critical to making adjustments to the product, particularly as it’s been tested in a small market and full-market roll-out.
Margin Analysis

Sorting out Revenue, Direct Costs & Indirect Costs

The whole objective of margin analysis is to understand the financial profit or loss status of individual telecoms products, services, and packages.  To do that we subtract the total revenues of a particular product with its costs?

Sounds easy, right?  Well, it would be easier if we didn‘t have to deal with two entirely different kinds of costs.  There are the direct costs associated with the particular service you are trying to get the margin of.  Then, there are also hundreds of indirect costs such as the salaries of people, marketing costs, etc.

Luckily data on indirect costs is relatively easy to obtain.  That data lives in a telco’s financial system and is computed by adding up payroll, purchases and the like.  Now, there are many different ways to combine indirect costs with direct cost to arrive at a full-loaded margin analysis.

cVidya’s approach is to leave the method of combining direct and indirect costs up to the individual operator.  Instead, we focus on the hard part — sorting out the direct costs of the individual services, products, and bundles.

The difficulty here comes from the need to gather direct costs from data sources scattered across the telecom enterprise.  For instance, interconnect contracts are managed by the business.  Usage collection is done by engineering.  Pricing plans are done by marketing.  In short, many pieces of the organization jigsaw puzzle need to come together.

Merely collecting information from all these sources is challenging.  Then there’s the added task of managing the many algorithms or formulas used to calculate profitability.

Billing cycles add another huge level of complexity: since telecoms bill on many days of the month, so you have to figure out how to align those billing cycles to get an accurate margin.

Volume commitments to partners are yet another complicating factor.  For instance, in content settlement, the rate may be $100,000 per month for a certain level of subscriber purchases, but $150,000 per month if that volume commitment is not met.

So sorting out all these complexities — and fitting them in with the unique needs of individual telcos -- is where the real value of margin analysis software comes in.

Easy to Implement — Based on Revenue Assurance Data

As I mentioned at the outset, the great news for financial and revenue assurance professionals is that margin analysis isn‘t one of those costly applications that only large carriers can afford.  On the contrary, at cVidya, we are implementing it as a “bolt-on” solution to RA.

RA software essentially paves the way for margin analysis because every department who has an RA solution already has most of the source data already collected, normalized, and augmented with added data such as the demographic, regional, and network information associated with customers.

Adding margin analysis to RA brings some nice advantages.  The analytics information is online and users can interact with it dynamically to drill down to specific segments or even create new alerts.  And because the platform is based on xDR-level data as opposed to just aggregate numbers, you can view the various dimensions data in highly granular segmentations.

Here are some examples of the flexibility this new style of margin analysis solution delivers:

  • High segmentation — In most cases today, margin analysis today is per plan, but with a revenue assurance-based solution, you can drill down, say, to international calls made on a mobile phone from the financial district of Milan, Italy on the second week of November.
  • Alarm when Margins Deviate from Normal — The user can decide what types of margin changes trigger alarms.  For example, you can set an alarm threshold to go off when the margin on traffic with an international carrier goes down by 25%.
  • Dynamic segmentation — The data dimensions are fully under the user’s control, enabling highly customized analysis.  For instance, you can compare the margins of specific media vendors serving the youth market by youth category, time, and region.

Conclusion

Margin analysis, catalyzed by big data platforms and leveraged by revenue assurance systems already in place, brings some exciting new business-optimization power.

It will allow a telecom organization to deliver not just revenue assurance, but real-time and fine-grained “profit assurance” as well.

Copyright 2012 Black Swan Telecom Journal

 

About the Expert

Efrat Nissimov

Efrat Nissimov

Efrat is the Director of Product Management in the Marketing department in cVidya, responsible for the Revenue Assurance, Fraud Management and Risk Management products and solutions.  Efrat has 17 years of experience in the communications and telecom industry.  Prior to joining cVidya, Efrat worked 10 years in TTI Telecom (now part of TEOCO) as a Product Manager and Solution Engineer of the CDRs products.  Prior to this position, Efrat worked in the Engineering and Operations Departments in two leading Communication Service Providers in Israel.   Contact Efrat via

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