In today’s guest post, Daniel Peter of Mara-Ison Connectiva highlights a risk addressed by few revenue assurance teams. It is common practice for telcos to keep up-front customer charges low, with the intention of generating a return from subsequent service fees. Daniel questions if RA can, and should, do more to monitor this financial exposure.
Recently I came across a challenge faced by a telco in the MEA region during my discussion with their RA team. Typically RA teams are well versed in the traditional switch-to-bill assurance like provisioning failure, missing calls/events and rating/billing error etc, but they are not equipped to handle risks that fall outside of this path. They lack the tools and the skillset to handle new risks. This issue was highlighted in a recent blog by Eric. He says that RA managers possess very powerful tools but do not have the wide range of tools to manage the full range of risks implied in by the title ‘risk manager’. I agree. Techniques like Monte Carlo simulation should be in the toolbox of every risk manager.
A telco’s financial risk increases with the addition of complex and dynamic service offerings. One new area of increased risk is selling devices bundled with postpaid/prepaid plan. As technology evolved from 2G to UMTS to LTE, the corresponding advancement on the device side has been fascinating. I still remember affluent people in India carrying imported Motorola DynaTACs in early 1990s. Few could afford to buy them, and there was hardly any network to support them. Mobile phones have since become the talk of the town, and we have seen the rise and fall of several mass manufacturers of handsets. In mid-1990s, Cellular (GSM) network was launched in India and started becoming popular with Nokia 3310 series taking the lead in the market. Then we have seen the beginning of the smart phone era with the rapid addition of new features and services like a camera by Nokia, mail services from Blackberry, mobile operating systems, powerful mobile processors and bigger storage and the whole mobile internet. Then came Apples’s iPhone that changed the landscape completely, reinventing the smartphone model. Google’s Android platform was another game changer which basically made life impossible without mobile phones. While this revolution is playing out for the subscribers, behind the scenes we find that the telcos operations have not kept up with the times. For majority of operators in the emerging markets, the RA departments are still functioning within the boundary of conventional switch to bill assurance mindset.
Whilst devices have become more expensive over time, service costs have steadily gone down. For example, the call charges in India are less than a cent (US$0.01) per minute whereas a decent smart phone ranges from US$200 to US$1000. With this evolution, the risk profile for the operator has changed. As devices are bundled and subsidized with service, the operator now has a significantly larger exposure. That means the focus of RA departments has to move beyond conventional switch to bill assurance.
Telcos take huge financial risk when they bundle devices with their talk/data plans. In effect they are entering into a finance/lease arrangement with the subscribers. The RA team I had interacted is still struggling to measure the cost, risk and return associated with the financing leasing and the handset upgrade options provided to the customers. This issue is beyond switch to bill revenue leakage scenario and the risk drastically affects the bottom-line. A different methodology and approach are required to address the risk.
I strongly believe the RA team has to adopt a proactive and predictive approach to assess risk and prevent revenue leakage using data science techniques. Data science typically gets a detailed view of the problem by collecting attributes that are ignored in the conventional analysis. It also looks at the problem from different dimension. This provides an edge to the business as preconceived notions are eliminated and behavioral aspects of the customers are understood using data. This aids prediction. Data science has been around for very long time and is on the rise owing to the rapid growth of big data technology and analytics profession. Techniques such as decision tree analysis, goal programming, Monte Carlo simulation, and Naive Bayes classifier are widely used in data science practices and could be helpful for the RA team.
While a finance company will have standard models of risk calculation for leasing and financing purchases, a service provider like a telco has a different goal and path to profits. Some telcos in America and Europe already have RA controls that address risks that go beyond the conventional scope of RA. But all telcos, whether in developed or emerging markets, should assure the bundles they offer to customers. This is a challenge but the good news is that the tools are available and the skill sets can be acquired. But first, we need to realize the challenges we face.