The UK Revenue Assurance Group is meeting tomorrow, and there is a full agenda for those attending, including talks and workshops on:

  • how one international telco implemented its own in-house Decision Support System for RAFM;
  • the adaptation of assurance techniques to optimize network capex;
  • how to involve the RA team with new projects;
  • a case study on how Enterprise Risk Management contributes to assurance in one major British telco; and
  • a comparison of how to manage revenue risks in telecoms, financial services and retail.

And if that was not enough, the group will be celebrating 10 years of its regular triannual meeting! With cake!

The UK RAG gets is name because it holds its meetings in London, though anyone with a relevant professional interest is welcome to attend. International members of RAG may not be able to fly into London to help us blow out the candles on the birthday cake, but they can still take advantage of the exclusive content in the member’s only section of the RAG website. The latest addition is the audio recording of a presentation on how Tesco Mobile, an MVNO, manages its revenue assurance.

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The relationship between people and machines has been a recurring theme on talkRA. I discuss the tension between people and machines a lot. But I think the tension is not really between people and machines. The tension is between people who treat other people like human beings, and people who treat other people like machines. People are varied, difficult, unpredictable, individual, and demanding. It would be convenient for a business to have 500 employees who all behave the same way, and 5 million customers who all behave the same way. But people are not like that. Whether we talk about software, controls, or processes, there is a danger that somebody with little empathy for real people, and divorced from the consequences of their decisions, will make terrible choices that then become hard coded into the ‘rules’ of the business. These decisions may look good from the cold, abstract perspective of a spreadsheet, but can be terrible for the human beings affected by them. The end result is the kind of customer experience shared below.

American technology journalist Ryan Block called Comcast to cancel his internet service. After ten minutes of arguing with Comcast’s ‘retention specialist’, he decided to record the remainder of the call, capturing the final eight minutes. Afterwards, he shared the recording on SoundCloud, where it took just two days to reach 4 million people. Why did 4 million people take an interest in Block speaking to Comcast about cancelling his service? Because Comcast’s representative repeatedly demanded an explanation for why Block wanted to cancel his service – in the obvious hope that Block would simply give up and remain a customer. Listen for yourself…

I wanted to talk about this incident because these examples must be balanced against any data-centric analysis of how to boost revenues, reduce churn, and so on. This recording is also data. Unfortunately, it is the kind of data that is hard to compress into numbers and spreadsheets. But it is still vitally important data if we want to understand how well the business is performing. And this data says: “avoid Comcast as your service provider, because they treat customers badly.”

The recording also says that Comcast treats its staff like machines. Whilst the customer thinks they are talking to a human being, who has some discretion over how they behave, the customer might as well be speaking to an IVR. Comcast’s representative behaves like a slave to the rigid rules he is expected to follow. That means the employee is required to ‘save’ the customer by any means possible, even if the customer is absolutely determined to leave.

A statement issued by Comcast puts the blame solely on their representative, saying:

The way in which our representative communicated with them is unacceptable and not consistent with how we train our customer service representatives.

However, others have questioned Comcast’s corporate attitude. When Comcast tweeted to say they would take ‘quick action’, Block tweeted back:

I hope the quick action you take is a thorough evaluation of your culture and policies, and not the termination of the rep.

And somebody claiming to be a former employee of Comcast used Reddit to share a much more comprehensive analysis of why Comcast’s representatives would behave like this:

If I was reviewing this guys calls I’d agree that this is an example of going a little too hard at it, but here’s the deal (and this is not saying they’re doing the right thing, this is just how it works). First of all these guys have a low hourly rate. In the states I’ve worked in they start at about 10.50-12$/hr. The actual money that they make comes from their metrics for the month which depends on the department they’re in. In sales this is obvious, the more sales you make the better you do.

In retention, the more products you save per customer the better you do, and the more products you disconect the worst you do (if a customer with a triple play disconnects, you get hit as losing every one of those lines of business, not just losing one customer.) These guys fight tooth and nail to keep every customer because if they don’t meet their numbers they don’t get paid.

Comcast uses “gates” for their incentive pays, which means that if you fall below a certain threshold (which tend to be stretch goals in the first place) then instead of getting a reduced amount, you get 0$. Let’s say that if you retain 85% of your customers or more (this means 85% of the lines of businesses that customers have when they talk to you, they still have after they talk to you), you get 100% of your payout – which might be 5-10$ per line of business. At 80% you might only get 75% of your payout, and at 75% you get nothing.

The CAEs (customer service reps) watch these numbers daily, and will fight tooth and nail to stay above the “I get nothing” number. This guy went too far, you’re not supposed to flat out argue with them. But comcast literally provides an incentive for this kind of behavior. It’s the same reason peoples bills are always fucked up, people stuffing them with things they don’t need or in some cases don’t even agree to.

I find this account of Comcast’s rules to be credible. Comcast may have a rule saying their reps should not argue with customers. However, nobody is this overzealous unless they are motivated to be like this. In other words, something in Comcast’s rules, procedures and incentives is motivating this human being to be so dogged at retaining customers. Without a financial incentive, it would be normal for the rep to just do as Block asked, cancelling the service and ending the call as quickly as possible. Arguing for nearly 20 minutes shows that the rep has something personally at stake. In this case, the rep has too much at stake.

Whilst Comcast’s motivational techniques might deliver good results on their spreadsheet – there is no doubt this kind of high-energy ‘retention’ strategy will influence some customers – there are also downside consequences for real people which may not be shown by the data that management looks at. No matter how much data we think we have, when it comes it comes to marketing analysis, customer service, satisfaction and loyalty, we need to remember how difficult it is to reduce people’s attitudes and behaviour to numbers which computers can calculate. Decision-makers who ignore human consequences do not deserve respect, whether they intend to disconnect a batch of old services, and wait to see if any customers complain that they have been affected, or whether they give a salesman a big bonus for results, then plead ignorance of the salesman’s unethical tactics.

Data can be clean and straightforward, making it pleasant to work with. Much of business assurance is rightly oriented around data. Manipulating and managing data contrasts with the messy business of how people think and act, which is difficult to record, measure and describe using rules and formulae. But telcos exist to serve people, and business assurance professionals should always keep that in mind.

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Imagine this advertising campaign…

Good news for all PhoneyTel customers!!!!

Previously your bills were only 70% accurate. We’re very sorry about that. We let ourselves down, and we let you down. Presenting mistakes in 3 bills out of every 10 is simply unacceptable.

We listened when you told us we needed to improve. So that’s what we did. We’ve improved. We took a hard look at ourselves, and we’ve changed the way we work. We’ve invested in our people, in our process, and in our technology. Now we can promise you the standard of performance that you expect from us, day in and day out. With thanks from our technology partners, we’ve just completed a project to resolve our accuracy problems. And that’s why we can proudly boast that PhoneyTel bills are now…

(drum roll)

90% accurate!!!!

That’s right. Your bill is only going to be 10% wrong, when previously it was 30% wrong. That’s the quality of service you deserve, and PhoneyTel is glad to give you what we think you deserve.

That would be a ridiculous campaign! Or maybe not. Consider the following excerpt from the website of a leading global supplier of ICT solutions…

Loss of revenue due to billing & charging complexity, inaccurate data input, invalid correction & discount control, ineffective payment collection, and bad debt management remain a major challenge for operators. The primary causes of revenue leakage include network configuration changes, tariff configurations, and poor system integration during the CDR processing cycle…

…Through [our] understanding of the revenue cycle, [we] can help operators reduce the potential impact and risk of revenue leakage through its revenue assurance processes, tools, and expertise…

…[We were] able to improve the billing accuracy for a certain African operator from approximately 70% to 90%, leading to fewer customer complaints.

I may not be a customer of a certain African operator, but excuse me as I still feel entitled to complain about such extraordinarily low expectations. Who, in all seriousness, thinks 90% accuracy is something praiseworthy, even if it is an improvement on what went before? Customers complain more about overcharges than undercharges, so it is hard to know what “70%” accuracy really means in this context. But whilst 90% accuracy sounds better, it still sounds lousy. Nobody should be seeking credit for delivering such inaccurate bills. That would be like a thief expecting you to thank him, because he took your wallet, instead of stealing your car.

Mistakes will always happen, but accuracy is not so hard to deliver that anyone should think a 10% error rate is tolerable. In a way, I am pleased that this supplier has been so transparent. However, I am more upset that they feel no embarrassment at being associated with such a low level of delivery.

Who is the supplier? (Drum roll…) Huawei. They may be fringe providers of RA services, but sloppiness at the fringe hardly suggests a robust core. Huawei employees, including the people who wrote this promotional spiel, should ask themselves a question. How happy would they be, if they learned that their payroll was 90% accurate?

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Daniel Peter of Mara-Ison Connectiva contributes today’s guest post, which revolves around a common challenge for revenue assurance and fraud management: measuring the benefits that are delivered. However, Daniel steps back from the usual headlong rush into calculations and equations, and first poses a much more fundamental question. What kind of value should revenue assurance and fraud management seek to deliver?

I have been thinking lately about the fundamental attitude of telcos towards Revenue Assurance and Fraud Management (RAFM). My recent interaction with RAFM Managers and System Integrators suggests that every dollar that’s being spent on RAFM is questioned; while cost consciousness is good for a telco’s short term profitability it might lead to loss of strategic advantage when unfavorable decisions are made on RAFM spending.

RA is not a mere hygiene factor but provides strategic advantage to the telco. I have also seen discussions in certain RA forums where they discuss, “whether cost of RA is higher than the leakage detection”, “what is the ideal payback period for RA System”, “what is the breakeven point for RA System”… This made me wonder whether the tools and techniques for breakeven analysis and payback period are the right approach for the investment/expenditure decision on RAFM.

Competitive forces for telcos are on the rise with regulators making concepts such as MNP mandatory; while consumers enjoy better features and services it puts tremendous cost pressure on the telco (margin has become very thin). This margin pressure has also affected the investment or budget allocation decision on Revenue Assurance and Fraud Management – for both people and technology. RAFM is a cost center and it’s being targeted by the management as a potential area to cut down cost just as they do to areas that are not core to the business; but RAFM is core to telco. Another interesting observation is that investment in RAFM is very different from an investment in a new software system or marketing campaign where short term return-on-investment calculation should be the driving force for decision making.

When a telco invests in an OSS, there’s a decision making process in place where the business and IT jointly participate in selecting the vendor. This approach helps the management in ensuring that the allocated budget has served the purpose, certainty on increasing profitability and securing the return on investment.

There are various tools and techniques to calculate RoI. For example, a telco planning to launch 4G LTE will perform breakeven analysis to determine the Break Even Point (BEP) for the incremental revenue generated from 4G LTE. Cost of cannibalization is factored in for this example as the subscriber would unsubscribe from the GPRS plan (Cost of cannibalization is the decrease in profits as a result of reduced sale of the existing product; customers are moving to the new product. From GPRS to 4G LTE in our example) BEP provides the number of incremental units the telco has to sell to cover the expenditure which means if the firm sells less than the BEP, they lose money. BEP is the point where the telco generates zero profits from that investment which means revenue is equal to the total expenditure at that point.

Payback period can also be assessed using breakeven analysis as we can forecast how long it will take to get to the breakeven point. When the payback period is very short, there is a risk that the return on investment is lower; in other words the return on investment is assured and the rate of return can also be quantified. It’s mandatory that the decision maker hit that number otherwise that expenditure will be classified as a bad decision. When the units sold exceed the BEP, it is fetching profits from the investment. Breakeven analysis is a good tool to assess whether an investment should be made or not and whether it’s feasible to achieve the BEP within an acceptable time frame. Tools like these are very helpful for investments/expenditures that yield direct results within a short period and the revenue stream is straight forward.

Now the question to answer is, can we consider tools such as these to make decisions on investment in RAFM? According to TMF, the objective of the Revenue Assurance Management processes is to establish an enterprise-wide revenue assurance policy framework, and an associated operational capability to resolve any detected revenue assurance degradations and violations. While all these can be quantified and measured, the question we have to consider is whether the telco should use a short-term RoI analysis such as Breakeven for RAFM?

In my opinion, measuring RoI for RAFM with tools and techniques such as Breakeven Analysis should be avoided, as RoI for investment in a plant/machinery/network expansion is focused on production and sales whereas RAFM function exists to provide strategic advantage and the returns are long-term although identified leakage in short-term can justify the expenditure in RAFM. Unlike investment in network expansion, the object of the RAFM function is different. Telco should assess the key outcomes of RAFM and not calculate RoI solely based on leakage detection. RoI for RAFM based on leakage detection focuses on how much leakage the investment has found and how many dollar worth of fraudulent practices have been found, which is an indicator of loss of qualitative focus.

RAFM by nature is number focused but the return on RAFM should be qualitative focused. RA leads to increased revenues but leakage detection from dollar spent is not the right approach. Telco should assess the risk areas RA is addressing, do a what-if analysis to quantify the potential loss (in terms of revenue leakage, quality of service and fraud should the risks go unnoticed), the satisfaction the board has over the reported revenue and the confidence the customers have on the bills sent to them and deduction in their prepaid vouchers have to be considered. All these translate to strategic advantage and have to be considered while evaluating the value addition from RAFM.

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I read Eric’s post about how the TM Forum has reduced the importance of people within their new Revenue Assurance Maturity Model. As both a founder of Compwise, a software business, and as a human being who helps big businesses to analyze their data, I wanted to explain why I feel the TM Forum has made a mistake.

For the last couple of years I have switched sides, wearing a client’s hat instead of a vendor’s one. One financial institution, an issuer of various charge cards (a.k.a. debit cards, credit cards, prepaid cards et al) asked me to check their product portfolio and assess their profitability.

At some point I had found myself conducting a typical RA audit assignment where the billing is rather complicated, including about 7 or 8 bill cycles (per TRX, daily, weekly, monthly, quarterly, semi annually, annually and a sporadic one). The tool I used for this audit was Microsoft Excel – with some reliance on “a little helper” assisting me with advanced Excel functions, as my command of Excel is fairly basic. The principles I followed were same as used by any auditor or RA practitioner working in telecoms.

I had analyzed one product only, analyzing the revenue streams, of what is called a 4-party model, which in practice involves around 6 or 7 parties. My analysis revealed hundreds of thousands of dollars in incorrect charges submitted to the financial institution.

I guess if I had procured dedicated software, implementation et al, this would produce better results than my humble use of Excel. However, for zero investment in software, and within a very short time frame, it is far more effective to get 90% of the value that can be saved, rather than waiting for 99.9% of savings to be delivered after the long timeframes involved in a tender, proof of concept, procurement negotiations, purchase and implementation of a specialized solution.

This also means the incremental value added by a specialized solution is not the 99.9% of savings that are reported by the tool. The incremental value is the 9.9% it delivers above the 90% that I could deliver using Excel (minus the costs of engaging me, but plus the costs of purchasing the solution).

Down the road, the data I used for my audit was exported to a BI tool. This makes it easier to analyze the data and find the same mistakes. Today there is a new generation of BI tools, which are agile, cheap and lightweight.

But whilst tools are becoming cheaper and more powerful, it is too easy to focus on the cash costs of tools and to neglect what people need to do, but tools can never do. We often take people for granted, even though people may be part of the problem that needs to be solved.

In my project for the financial institution, the most complex component was to establish the organizational consensus and acceptance for the project. It was obvious the process and the resulting findings would radiate on various departments and some stakeholders might feel concerned with the findings. The key challenge was not the technical part but rather the internal sales process, applying sensitivity in order to create an organizational joint effort where the goal is achieving an improved level of audit and control as well as improved risk management. This obstacle has nothing to do with technology. It is all about people. The source of the challenge lies with people, and only people can overcome it.

Lastly, I recall a situation 7 years ago when I was still running Compwise, selling specialist solutions to telcos. TMN developed an internal tool for churn analysis, with the help of their local IT partner. Based on their CDRs and tariffs, TMN’s in-house tool delivered 90% accuracy compared to the 99.9% accuracy of the Compwise churn simulation tool. TMN invited me to a demonstration of the results. My response was… “well done”. For them, 90% accuracy delivered the right return for their stakeholders. Who am I to argue otherwise?

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