The global pandemic and its associated travel restrictions have seen roaming revenues effectively wiped out. This came along on the back of the European Union (EU) RLAH pricing policy already limiting operators’ ability to generate revenue from roaming. How can operators start recovering those lost revenues? Big data can help.
Roaming revenues have been hit hard in recent times. By March 2020, a decrease of 11% in annual inbound and outbound traveller numbers gave an indication of what operators could expect to come from roaming during COVID-19. It has been difficult for operators to formulate any solid strategy to address the challenge. They already had to deal with issues like regulations imposing lower tariffs and the potential for billshock, meaning travellers weren’t necessarily roaming in places where there were no regulations.
It all amounts to a serious hit: Juniper research suggested that telecoms companies could lose revenues of up to $25 billion in 2020, around half of what the industry makes annually from roaming. Juniper has further predicted that it could take three years for the telecoms industry to bounce back. Its research estimates that mobile roaming subscriber numbers will take until 2024 to exceed 2019 levels, amounting to 918 million by 2024.
What can operators do to give themselves an edge and recover lost roaming revenues? Big Data is one powerful ally that can enable visibility into how your roamers behave, helping you formulate business strategy. Here we look at some potential opportunities that can help telcos retrieve margins from roaming.
Since mid-2017, EU citizens have been able to use their mobile data to call and message wherever they were in the EU without any extra costs, thanks to the Roam Like At Home (RLAH) policy. The EU defined fair use policy within its RLAH regulation, to ensure that permanent roaming was prohibited. If customers don’t stick to these rules, operators can levy a surcharge.
Elsewhere in the world, the thresholds for permanent roaming may be defined by each operator, with respect to local regulations. For instance, the operator may consider that a permanent roamer is a customer spending over 50% of a month roaming and racking up a data consumption above half the domestic average.
So, operators can amend “fair use” definitions and use that as a route to potential new revenues. Big data analysis can enable you to evaluate and identify those roamers who qualify as “permanent” roamers. By using data to carry out test scenarios, you can experiment with different criteria and threshold levels and see what impact that could have on roaming profitability.
Internet of Things (IoT) devices often consume low levels of data, meaning they generate lower roaming revenues. They don’t exceed fair usage policy levels, so don’t qualify as permanent roamers either. They do, though, utilize network resources, so need to be factored into your planning.
IDC has forecast that by 2023, worldwide shipments of connected cars will reach 76.3 million units, at a CAGR of 16.8%: it’s possible that many of these cars sold in certain countries could come fitted with a SIM card from a foreign operator. This presents the opportunity to use a big data analysis tool to identify those M2M SIM cards that have roamed in your country for an unusual length of time during the past six months or year for example, qualifying as an abuse of roaming policies. If you can work out how many of these SIM cards are in circulation and misusing the system, you are better placed to negotiate new wholesale roaming agreements with operator partners or to apply flat fees for these types of SIM cards.
Big Data tools can also be a means to identify “silent roamers”, those users who travel internationally but disable their phone’s mobile broadband and rely on local Wi-Fi or purchase an in-country SIM. Silent roamers typically use very little data when traveling: many go out of their way to ensure they don’t use any at all. This represents potential lost revenues that operators could be bringing in.
Silent roamers are generally so concerned about billshock on their return home from travels that they try to use local Wi-Fi as much as possible for all activity, including voice calls. They’re aware that Wi-Fi isn’t always available everywhere they go. When it is, it can be of a lower quality that won’t support video calls or even upload travel photos to their social media.
Silent roamers are notoriously difficult to identify and quantify. But big data analytics can give you insights into their behaviour and present a solution: by combining traditional DCH TAP files with signalling data, operators can pinpoint silent roamers and take action. It’s essentially a marketing exercise that utilizes big data to target customers with a value-add solution that is in their interests. You can remind customers who might already have a roaming package with you of its existence, and you can propose roaming packages to customers that don’t presently have one, in compliance with GDPR or local privacy laws. In both cases, the goal is to turn silent roamers into real roamers and potentially improve both revenues and customer satisfaction.
For their customers travelling outside of the EU, EU operators are required to cut off data roaming once a customer’s data bill reaches €50 excl. VAT. It’s to prevent billshock when the traveller returns home, and operators are obliged to warn customers in some way, typically via SMS or email. Usually, this amount is reached with only a few Megabytes.
That obligation doesn’t exist outside of the protections given to mobile customers in the EU. So for non-EU roamers, the roaming bill may be even bigger.
PAYG travellers may use roaming accidentally – or choose to use roaming but with no idea of how much it might cost them. They can be in for a surprise when they get home and find a large bill waiting for them. This creates dissatisfaction and complaint calls to the service centre, and from the perspective of the operator, potential refunds and, in the worst-case scenario, churn. Using big data, operators can identify PAYG roamers and, again, make them aware of the availability of roaming packages that could be of interest to them.
Big data analysis can help you gain a clearer picture of all your customers’ habits when roaming. So, taking a look at average data consumption is a good idea. If average consumption is getting close to the maximum level allowed in existing roaming offers – say 800Mb used when the limit is 1Gb – it’s likely that many roamers are using up all their package when traveling. This again means they either risk billshock (if PAYG is allowed beyond offer allowance) when they return home, or they have to reduce their roaming usage. It certainly doesn’t encourage peace of mind in customers when they travel.
Analysis can give you greater visibility of this type of practice and let you respond. It means you can offer higher-level roaming packages which bring benefits across the board. It drives customer satisfaction since there should be no billshock, and it also drives revenues for operators since people who were limiting their roaming usage may be happy to spend more. Furthermore, it potentially increases margin; with a higher-limit data package, more customers will not use it all up.
Furthermore, it potentially increases margin; with a higher-limit data package, more customers will not use it all up.
The GSMA found that roaming was one of the four key areas of telecom revenues that were impacted by COVID-19 in 2020 and 2021. The data is there to help you bring roaming back: by collecting data from your roaming billing traffic and also from signaling, you can detect all your roaming customers with specific behaviours and change your offering. By embracing big data and analytics, operators can open the door to potential new revenues that they’ve been missing for a while.
Big Data solutions Global connectivity award