10 Creative Ways Telcos Can Price Mobile Data Plans in 2014

There’s a lot happening in developed telecommunications markets when it comes to monetisation – communications service providers (CSPs) are no longer charging customers per minute, they’re charging per megabyte. Talk and text are usually unlimited, while data has become limited.

The business of charging customers for data can be complex. Many major mobile operators offer capped data packages, but in certain cases, this may impact customer experience. Consumers understand that their phones use data, but it’s not always clear how that data is consumed. Does Facebook use more data than WhatsApp? How many songs on Pandora can be played before the cap gets hit? How many YouTube videos can be watched?

The data usage of different apps and services can be confusing. When customers hit a data cap without expecting it, their experience may suffer if there isn’t an easy, flexible and affordable way to top up for the rest of the month. Otherwise, customers will use WiFi instead of mobile when they’ve reached their monthly data allowance.

Future revenue growth will depend on flexible, personalised service packages, and how fast CSPs are able to launch them to the market. Through a combination of tried-and-tested monetisation methods – and new ones – CSPs will be able to build a better customer experience while introducing new sources of revenue.

Comptel recently worked with tefficient to create a whitepaper that highlights 10 more methods mobile operators should consider implementing to monetise data. Here’s a preview of three methods that are covered in the whitepaper:

1. Ad-funded mobile

Last year, an ad-funded MVNO called Wifog launched in Sweden with a unique proposition: users could consume as much data as they wanted, but they had to be open to advertising and data collection. For every 100Mb of data used, a Wifog subscriber watches a 45-second video ad. Wifog gained 120,000 users in five months.

Established CSPs may not want to replicate this exact model, but it’s possible for them to host these kinds of ad-funded mobile virtual network operators (MVNOs). In that regard, this could be a way to generate revenue from advertisers while hosting customers that traditionally use Wi-Fi or Over-the-Top (OTT) services.

Analytics could come into play here, too – CSPs could monitor user habits and customise the customer experience, targeting them with the most relevant ads.

2. App time-based charging

With the right approach to policy and charging, CSPs could potentially create a service plan that monetises how often customers use an app, with pricing plans based on hourly, daily, weekly or monthly rates.

In mature markets, this data plan could solve the “end-of-the-month” problem, when customers have run out of their data allowance and don’t want to pay for another whole-month package if they only have a few days left. Consequently, there are a lot of customers who would like to pay for a bundle at a lower price point.

If CSPs can leverage analytics to see which kinds of apps these customers use, a more affordable, Facebook-only package could be more effective for the last few days of the month. The harmony of policy, charging and analytics play an essential role here – the agility and flexibility offered by the right solution can help monitor app usage to guarantee that customised, time-based packages are delivered when a data cap is hit.

Indian operator Uninor shifted from a Mbyte-based offering to time-based app pricing – for example, users can now pay one rupee for a day of unlimited access to WhatsApp or Facebook.

3. Apps for flat fees

CSPs can also offer a set of apps for a flat fee. Japan’s second largest mobile operator, KDDI, offers this model with a “Smart Pass” package, which allows members to get unlimited access to a set of apps for a flat fee. In addition, members are given a few other benefits, such as discounts and photo storage services. “Smart Pass” launched in 2012, and in March 2014, 10 million customers were on the plan.

The really exciting part about offering apps for a flat fee is the potential for predictive analytics. By monitoring how customers use apps and what apps are used, CSPs will be able to personalise offers and predict which apps are going to be used across their networks, creating custom packages for different audience segments.

Possibilities, Analytics, Case Studies

In this blog entry, we only covered three ways CSPs can monetise mobile data, but our white paper has ten. In the future, a diversity of offerings – and the policy management product that allows for responsive and flexible delivery and pricing – will be key.

All subscribers have different habits when it comes to using their mobile phones.  With the help of policy charging and predictive analytics, CSPs can create personalised service packages that are delivered at exactly the right time.

Want to learn all of the ways CSPs can monetise mobile data? Download “10 more methods to monetise mobile data,” written by consulting firm tefficient, an international efficiency specialist for telecom operators and suppliers.
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Comptel and Big Data at Mobile World Congress 2014

You probably have not been able to avoid hearing the term, “Big Data”, nor about the expectations of its limitless possibilities for communications service providers (CSPs). CSPs have a unique opportunity to delve into the spectrum of network, customer, service and other information at their fingertips and flowing through their OSS/BSS, eventually using it to improve both internal operations and customer-facing processes.

But Big Data sadly often means Big Projects. It is not just management of the three core “Vs” – volume, velocity and variety – that contributes to this, so can the setup of the technology to store and collect the data. But often, the biggest challenge stems from the fourth “V”, or value. What do operators need to do in order to drive true value from Big Data? I believe that there are some key requirements for being successful here:

• Have a strategic business objective to focus on. Do not just collect data for the sake of collecting data, but have a goal in mind and a roadmap of what to do to drive more value when you reach that goal. (Buy-in from the boardroom, of course, helps, too, especially with issues like breaking down organisational silos.)
• Don’t start with a blank slate. It’s important to have a set of proven, productised applications to address your business pains, whether it be customer experience-driven like smart throttling or network-focused for proactive service management, for instance.
• Collect experience and learning in your organisation if you see information as your key asset, but don’t wait until you have built an experienced team to do so – have that as your plan, but start generating value from operational applications from the get-go.

And that’s where Comptel comes in. We’ve been developing our Big Data offering to help CSPs give their initiatives a running start, and also  supply them with the tools to support information-based decision-making and derive the true value they’ve been looking for – quickly and in a future-proof and extendable way – to solve acute business pains and build on that success.

Comptel provides a true Big Data solution, addressing all key components of the Big Data process:

1)     Data Ingestion: Integration, importing and formatting of historical and real-time data from CSPs’ own data sources, combined with external data for a truly holistic view of the business. This is powered by Comptel’s proven technology used in our mediation solutions.

2)     Data Management: Transformation, correlation, enrichment and manipulation of data to ensure optimal usability, and using the most appropriate methods to store data—whether it be Hadoop for unstructured data, massively parallel processing databases or in-memory data grids.

3)     Data Analysis: Highly accurate, real-time predictive analysis, modelling and reporting, powered by machine learning.

4)     Business Analytics Applications: Productised solutions to solve acute business pains, utilising the whole Big Data solution to drive immediate value.

One important aspect of Comptel approach’s is the utilisation of both historical and real-time data to drive true value—we do not see these as separate discrete steps of a process, i.e. building a predictive model and then applying triggers based on the model’s predictions, but instead having the predictive model applied in the real-time data stream to reap and benefit from contextual intelligence.

Clearly, Big Data analytics is reshaping the telco landscape. According to our recent research supported by Vanson Bourne, about two-thirds of telco executives (64 percent) say they are already in the process of leveraging Big Data to improve customer service, for instance. This is the time for Big Data to show that it is not just a hyped concept but a true generator of value—and we believe that the way to do that is through scalable, Big Data solutions designed to achieve and build on CSPs’ business objectives from day one.

To discuss how contextual and operational intelligence can augment CSPs’ efforts, come to our booth (Hall 5, Stand 5F41) at Mobile World Congress 2014. Look forward to seeing you in Barcelona this week!

In this eBook, we share exclusive, global executive research that highlights:

- Executive strategies for 2014

- Barriers to integration

- Technology priorities

- Attitudes toward data & planning

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Around the World

As usual, there’s been a lot going on in the telco industry these days. Comptel wants to bring you the best, most interesting stories and studies we’ve found, so communications service providers (CSPs) can stay on track.

Here are three in particular that caught our eye:

Billing & OSS World…

Big Opportunities From Big Data, But Barriers Remain

Fifty-eight percent of those surveyed think that the main, long-term driver is generating new business models. Informa Telecoms & Media has released a new survey of telecom operators which shows that Big Data has the potential to create great opportunities for businesses in the future. The respondents also agreed that Big Data’s short-term driver is solving internal challenges.

Forty-eight percent of operators said that they have Big Data solutions implemented already. On average, they spend ten percent of their IT budgets on Big Data, and this is expected to increase to twenty-three percent within the next five years. However, a major barrier that continues to be an issue is that operators still lack a business proposition and a trained team to handle the implementation.

Comptel’s own Matti Aksela recently spoke to Telecom Asia on this latter issue: “Vendors operating in this space can have a very skilled team behind their analytics solutions, and knowledge on integration and decision-making based on the analytics, and can quickly achieve benefits for CSPs. It may be even easier to ‘tear down the silos’ coming from the outside than just working internally.” Read the full article on whether telecom operators should manage analytics in-house or outsource it here.

Analysys Mason…

Analytics Applications Provide Rich Functionality and Low-Risk Deployment to Help Drive New Use Cases

CSPs have been using traditional analytics tools to help review data, analyse it and report it. However, new analytics applications can be deployed and configured specifically for a CSP’s use case, automating the best practices learned throughout the industry while eliminating the need for scarce data scientists to help segment and manage the data.

The real challenge in deploying a new analytics solution is infrastructure. CSPs must be flexible about changes in operations, so the business can accommodate new tools into the workflow Infrastructures are usually created with a specific use case in mind, but when an application is on top of the old infrastructure, there’s always the risk of compatibility. Lastly, CSPs will have to depend on vendors for updates to the application. Analytics applications are beneficial for CSPs, but operators must have the means to implement a new system of data analytics for the analytics applications to be successful.

Matti Aksela spoke with Big Data Republic in June about Big Data’s ability to reduce churn through advanced, predictive analytics tools. Robi Axiata experienced the results first-hand – once the factors of churn were identified, strategies could be taken to predict and eliminate them in the future.

BillingViews…

Should experience come before the engagement?

With Big Data helping CSPs identify the granular aspects of the customer experience and customer engagement, it’s easier to see where in the organisation improvement is needed. While customer experience represents the sum of what a customer has experienced at a given time, engagement represents the sum of the customer’s experiences over time. For the customer experience to be improved, specific departments can be targeted and strategies can be recalibrated. Customer engagement, however, needs an enterprise-wise approach to be improved.

A customer’s lifetime value must be determined and closely nurtured by the entire organisation to ensure that engagement is positive and, as Ulla Koivukoski wrote, CSPs can uncover new revenue streams and grow businesses by focusing on engagement. Predictive analytics can play a major role in fostering departmental collaboration and, in turn, delivering high-quality customer experiences.

Is Big Data Actually Relevant for Your Business Strategy?

You can’t go far in the telecommunications industry – or nearly any other industry, for that matter – without hearing about the importance of big data. As we’ve mentioned before, with voice and text revenues declining, the art of applying analytics to customer data is moving into the spotlight. By monitoring how people are using data, communications service providers (CSPs) are hoping to discover new revenue streams and create more personailised marketing offers.

Paolo Sidoti, global managing director of Accenture’s Network Business Services group, recently explained to Light Reading that operators need to “reconnect with customers,” and big data analytics is the tool they’re using to do it. All of that sounds good in theory, but the reality isn’t as clear cut.

Accenture reported that just 22 percent of operators are “very satisfied” with their analytics tools. More than a third said they are “dissatisfied.” Another recent finding from the company indicated why that may be: CSPs are gathering a lot of data, but 61 percent said that it isn’t relevant for their business strategies.

Big Data Can Be a Big Mess

The emphasis on big data has inspired a lot of CSPs to pay closer to attention to what their customers are actually doing, but without a way to automate interactions and create an operational analytics program that automatically learns and adapts to use cases over time, big data can create more work than it’s worth. No CMO wants to sit in front of the computer sorting through 100,000 different customer profiles to try and manually predict who would top-up to watch a video on their phone, and then try to figure out what campaign would work best.

That’s why, in its raw form, it’s hard to make big data relevant for any business strategy. The information is simply too diverse and disorganised to use, and not every solution fits the bill. Accenture’s findings showed that 93 percent of the operators surveyed said they needed new or improved products to help with analytics. With so many CSPs saying they need better tools, it’s no wonder they’re disappointed with big data. If you can’t properly sort and operationalise the data, then it will never be useful for building better relationships with customers.

One key aspect to utilising the data is to make sure it is available in a timely manner, which is something Comptel has been doing for our customers by online mediation solutions. While data does not really have an expiration data, waiting months and expecting to get the same value is just not very realistic. But purely focusing on the event at hand isn’t the solution, either.

We believe that creating an individual profile from customer data and combining it  with the true context of the subscriber is the key. We also work in-depth with our customers on slower moving data and can provide excellent results there, but if I were sitting on the other side of the table, I would want a solution that takes advantage of streaming data.

Comptel’s predictive and automated analytics solves the problems of big data with powerful, machine-learning capabilities that ensure automated actions are taken at the right time to the right audience, with the right context. It’s in this way that CSPs can leverage big data analytics to customise their campaigns to each individual customer’s preferences and unique needs. More importantly, the insights that are delivered are helpful to the overall business strategy, too, making it easier to integrate analytics into customer loyalty and marketing programmes.

The use of big data for CPSs is not limited to marketing functions, either. Comptel provides predictive analytics solutions to operationalise use cases that will help show the value of data across networks and the impact and cost of different solutions on the technical environment. Additionally, predictive analytics can be linked to the subscriber level and help optimise policy-throttling activities.

Big data is also about breaking down the siloes. Rather than treating your network as one pillar of your business and the customer as the other, analytics is applied to data across the company. As we’ve said before, the future of marketing is networks and the future of networks is marketing. By bridging silos across the organisation, CSPs create a better end-to-end user experience – and with the vast volumes and huge dimensionality of this data, the way to do this efficiently is through machine learning and predictive modeling instead of trying to work within the scope of humanly manageable data.

Overcoming the Second Hurdle

In a lot of ways, the disappointment CSPs are feeling is a good sign, because it shows there’s an awareness of big data’s potential. While solutions are in use to address some parts of the problem, most operators are still experimenting with what works best for their businesses. But it’s reassuring that they’re so aggressively trying to figure that out and appreciate the value that big data can bring.

Comptel strives to stay beside CSPs every step of the way. A business strategy needs the right tools to work, and our array of solutions for big data analytics has helped businesses achieve an accuracy rate of 80 percent and outperform the competition 90 percent of the time. We’re determined to help transform big data across all of an operator’s units and siloes into actionable insights, and augment it with additional data sources from outside of the operator environment, such as social media. That way, data can become an organised and operational asset that can be used to build business and better customer relationships.

LTE Networks Expand in the Philippines, But Do They Address the Real Issues?

LTE use has been following an almost frighteningly fast growth curve. Global LTE traffic is expected to increase by 207% this year, and LTE customers are supposed to double in 2013, surpassing 100 million. Around the world, communications service providers (CSPs) are building new infrastructure to keep up with consumers’ demand for faster data speeds.

The Philippines is no exception – mobile subscribers grew from 6 million in 2000 to 92 million in 2011. By 2016, mobile subscription is expected to reach 117 million people, with a penetration rate of 114 percent.

Since August 2012, LTE has been slowly rolled out across the country, too, covering major cities like Metro Manila, Cebu, Davao, and Boracay. Major CSPs are spearheading the trend. An operator in the Philippines recently announced that it built LTE cell sites to service regions across Luzon, Visayas and Mindanao. And other operators have made similar moves into the LTE space.

Yet offering LTE service and having the right strategy in place to monetise it are sometimes two very different things.

A Demand for Personalisation

Whenever a CSP deploys a new service, the next step is to get people to use it. In the Philippines, we need to consider four big findings among Filipino subscribers who participated in our recent Vanson Bourne survey:

• 84 percent would download more files if they had a better mobile data plan.
• 67 percent top up their phone plans at least once a week.
• 72 percent want personal service when experiencing poor connections.
• 70 percent are likely to pay for a temporary bandwidth upgrade.
• 64 percent have two or more SIM cards

This data shows that there’s not just a demand for the faster data speeds LTE offers, there’s a demand for better, more personalised interaction with CSPs.

Sure, it’s possible to offer customers the same bundled package, but as competition increases, so, too, will innovative pricing packages. In a country like the Philippines, where so many people are topping up every week, it may mean that they’d be open to a new data plan, but they can’t find one that’s suitable.

Yet we see that nearly three-quarters of customers would consider paying for a temporary upgrade. That indicates that if personalised upsells were offered, CSPs could potentially realise greater revenues, because consumers would be willing to take advantage of these special deals.

Adapting for a Country’s Changing Needs

The smartphone phenomenon will change a lot of things, too. Last year, there was a 400 percent increase in demand for smartphones in the Philippines, with penetration expected to grow from 18 percent to 50 percent in the next three years. This trend is going to enable more internet and data use than ever before.  One survey showed that more than 80 percent of Filipinos have two or more personal devices, and among that number, 85 percent bring those devices to work.

LTE deployments and a growing acceptance of personal devices at the workplace are going usher in a lot of new changes for CSPs. In short, it’s going to be more important than ever for them to find a way to use the data at their disposal to their advantage.

With predictive analytics, for example, CSPs can analyse their customers, networks and other information, to determine which sets of customers would really benefit from full LTE use and which would most likely only want to use LTE sparingly. This way, promotions can be tailored accordingly, everyone will get the package they want and need, and CSPs can improve relationships in a way that builds loyalty and business performance.

What Technologies Are Impacting Policy Management?

I was recently talking about policy management with my colleague, Ulla Koivukoski, and started thinking about how far we’ve come and how it will continue to evolve. All of the new and advanced technologies that have been introduced in the past couple of years are having a big influence on this, and will continue to shape how communications service providers (CSPs) utilise policy management capabilities.

One of the most prominent of these technologies is 4G/LTE. Because LTE enables faster data speeds, customers will inevitably want to consume more and more data. CSPs who can gain deeper insight into such data usage will have a clear advantage. For policy management specifically, this means the ability to provide different packages with different rating models that are unique to customers’ behaviours. It also means implementing bandwidth or data caps in certain instances– otherwise, we’d use all of our network capacity!

Adding to this, it’s crucial for CSPs to identify the impact of down throttling on individual customers who are likely to churn and/or cause a revenue loss. For example, if customers experience poor quality of service (QoS), CSPs need to be able to proactively offer them a higher bandwidth or data package. In this way, the risk for revenue loss and customer churn can be mitigated while simultaneously improving QoS for the right customers. Further, a predictive analytics engine can suggest which customers will be most valuable for CSPs based on pre-defined Key Performance Indicators (KPIs), and which customers desire a corrective action to keep them on-board (e.g. a dedicated bandwidth prioritisation).

CSPs also can benefit by tightly coupling policy control with real-time charging. Like our recent consumer research demonstrated, financial considerations like personalised product/service promotions can influence customer behaviour. So, if CSPs can not only dynamically control the packages that are being delivered to customers and how, but also competitively price their offerings, they can increase the amount customers are willing to spend and maximise their revenue.

Linked closely with this is big data, which is giving CSPs a huge opportunity to add value. To tap into the power of big data, CSPs must first sift through and analyse the immense data volumes, both structured and unstructured, to get complete views of their customers. With this, CSPs can offer new services and bundles to customers with both efficiency and rapid time-to-market. Adding to this, a combination of advanced analytics and mediation enables CSPs to begin use cases like proactive broadband upsell for customers based on the prediction of their changed usage pattern, premium user identification, and automatically approaching customers with the right offer, in the right context.

Another technology making an impact on policy management—and one that goes hand in hand with big data—is the cloud. More and more, the cloud is one of the best options for storing and processing data. It allows for offline processing and the ability to trigger information online, to achieve real-time, personalised campaigns. Latency and security threats remain a concern, but if these can be managed properly, then I see policy making a big shift to the cloud.

Of course, this is just the tip of the iceberg – there are many more advancements being made every day. As our world and the technologies in it continue to evolve, I look forward to seeing how policy management will grow and change to drive a better, more efficient customer experience.

The True Value of Big Data

There’s no denying that one of the biggest trends in IT right now is Big Data. While there are many different ways to describe it, perhaps the most commonly agreed upon, and my personal favorite, is that it must encompass the three “Vs”: volume, velocity and variety. How organisations understand and embrace these concepts varies—but I think we can all agree on one thing – there is a lot of data being generated quickly from various sources. I’ve found that one of the biggest questions organisations are asking though (which adds a fourth “V” to the equation) is: How do we derive value from Big Data?

Real-time (or near real-time) predictive analytics are gaining in popularity, and may hold the key to realising Big Data’s true value. In his keynote presentation at OpenWorld, Joe Tucci, CEO of EMC Corporation, stated that: “Real-time predictive analytics will be the killer app for this cloud era.” Personally, I could not agree more and think that this points in precisely the right direction, not just for cloud but for all businesses dealing with data.

One of the main benefits is gaining a strategic understanding of customers and the overall business ecosystem. But the key is going beyond simply collecting information, or even the ability to store and process it. The way organisations can truly realise Big Data’s potential is by leveraging it to predict behaviours and market changes, and make smarter business decisions based on that knowledge.

What exactly those actions are will depends a bit on the case—for communications service providers, it may be policy management activities or real-time, location-based marketing campaigns. And as many are already noticing, decision-making with predictive modeling can have huge benefits.

In short, I believe we must indeed look at Big Data not as a thing that happens, but as a process we act upon – through contextual predictive analytics-driven actions. Enabling these insights is important to Comptel and something we’re continually working towards by combining analytics with our high-performance mediation and fulfillment platform. If I must confess, I am really excited about what we are seeing and doing here, and the benefits we can offer to our customers!

The Future of Mobile Business Intelligence

There’s an interesting intersection between the popularity of mobile devices and the appetite for business intelligence (BI). Inevitably, the demand to display and interact with BI on mobile devices is growing and will continue to do so as more mobile technology supports this function. Already, we have tablets and smartphones with high-quality displays and interactive capabilities – but this is just the beginning, if you take into account the full potential for mobile BI.

Mobile BI is mostly relevant in the consumption of information, which is reflected in the need for simpler interactions in BI infrastructures. After all, nobody wants to be writing complex code, like SQL, on their smartphones. Rather, one of the key benefits of a successful BI system is the ability to show the same information to all users. For instance, dashboard reporting with drill down functionality and reports that scale easily across devices will be vital for success. And the more access points there are, the more important this standardization is.

Let me also say, however, that mobile BI will not – and should not – replace existing BI systems. Instead, mobile BI should complement existing systems by providing organisations with added speed and flexibility for consuming the available information. I also believe the move to mobile will give even more importance to more advanced analytical methods. For example, the ability to easily, effectively and accurately segment data based on certain attributes or to combine relevant information, such as churn predictions, into the revenue forecasts, as well as data visualisation approaches will come to the forefront. This allows for the information to be accessed – in a relatively refined form – across the entire organisation.

In other words, I see the advances in mobile BI being very much complementary to the other movements we are seeing in BI and analytics as a whole – bringing easier and more operational access, through complementary methods, such as predictive and advanced analytics. This, in turn, provides more refined data in a form that is easy to utilise across the organisation to maximise effectiveness of — not only the BI and analytical tools — but the people using the information generated. The latter point, being able to flexibly but securely access the information when and where it is needed to minimise “information lag”, is certainly a strong value proposition and will help mobile BI gain its foothold.