Granularity is key to Quality of Service and Data Monetisation

Data monetisation is still essentially in its infancy. Unlike voice, where the whole industry was built before sophisticated monetisation came into play, operators today are looking for a concept of monetisation that is aligned with today’s consumption.

This means that it has to be personalised and contextualised without being segmented to broad demographics – it requires an understanding of each specific subscriber’s needs.

“Voice and text is yesterday’s business – we’re looking forward to the real business of data. The question is, how do you monetise it?” says Udi Ziv of contextual marketing solutions provider Pontis. “It needs to be broken down into specific value packages – if there is a real fit between the need and the product, then you can charge a premium. A generic need will just be commoditised.”

In terms of service, there’s not much differentiation between operators, and it’s not in the interest of established operators to differentiate on price. There are disruptors in the market who may try to shake things up with aggressive price plans on voice, which does cause shifts in market share as customers jump between operators, but there’s a limit to this.

Once everything is bundled, differentiation is essentially over – packages provide ‘all you can eat’ voice and the baseline price can only drop so much. Since data consumption is exponentially growing and gaining more importance, Ziv argues that investment needs to go towards data.

“Smartphones are now used as ‘data devices’ rather than phones. In many developing countries, investment in data infrastructure is either already underway, or on the verge of happening”, he says. “Once an operator has the capability of providing 3G or 4G, the next question is how to differentiate themselves from the competition.”

Understanding the context of the subscriber in real time and applying engagement schemes around them with this in mind is a basic concept – but when it is applied to data, there are a lot more dimensions to play with. For example, if a customer reaches the limit of their data package, their usage isn’t stopped but it is throttled, giving them a far worse experience.

There are a number of options for the provider in this scenario. Firstly, they can simply advertise a more expensive package that offers more data, allowing the customer to choose between a higher price and insufficient data. However, if a customer has a propensity towards using a lot of data, it may be preferable to offer them a ‘bridge bundle’, which essentially tops up their package. If the customer runs out of data a week before their monthly contract refreshes, they can pay a small fee and continue to use data in this period without being throttled – this is advantageous to the operator, particularly if a customer then decides to upgrade to a larger data package.

The regularity of data usage is of course affected by the penetration of smartphones, which means that currently emerging market consumers use less data. However, another factor behind this is the quality of service offered by the network. Building a customer profile is often a secondary consideration in terms of encouraging data usage, but operators need the same level of granularity to gain a full view of their network’s functionality.

Francois DeRepentigny of big data analytics company Guavus notes that knowing how a customer interacts with the network is not as important as knowing how they are able to interact with the network. “Particularly in emerging markets such as SE Asia, the focus is on network performance and quality of service. The issues might be basic, but if customers can’t download a web page to use an application, that’s level zero satisfaction.”

The priority is building better metrics and obtaining a finer-grain understanding of network issues. While it’s easy for operators to see how much data a consumer has used, it’s more important to know how it was consumed. While the amount may be the same, a customer’s satisfaction will be very different if they experienced a very quick download or a painfully slow one.

To establish this, operators need to keep track of how customers use the network – whether on a daily, hourly, or even minute-by-minute basis. There may be variations according to the device or the location, or the website or application visited, but more timely updates on data usage can be accomplished with self-learning intelligence that provides information either upon request, or automatically at a specific frequency. These granular elements of information build a better picture for the operator of how the customer experiences their network at a very specific level.

It is essentially the same idea as segmentation for marketing, but at a network level – rather than establishing the user’s preferences, operators can find out what kind of service each user on their network receives. In highly competitive markets such as Indonesia – which has over ten major operators – price is not an option for differentiation; it has to be about quality of service. This isn’t helped by the growth rates in developing markets. As thousands of people become mobile phone users, the network is under increasing strain and even something as basic as getting a signal, let alone placing a successful call, can be impossible.

Nishi Verma Nangia, senior analyst at Informa, notes that quality of service is an area that operators are strongly focused on. “They are trying to improve the quality of their calls – primarily on their 2G and 3G networks, but also on 4G/LTE networks as they begin offering new digital services. Offering a good experience for the customer is crucial – if customers are unhappy with the service they will churn out, which represents a substantial loss for the operator in the long term.”

A clearer view of the network – what it can provide to customers, and how they take advantage of this – is evidently key to understanding customer requirements. This in turn is key in monetising data – if customers are presented with options that will appeal to them then they are likely to buy. Building a fine-grain picture of both customer habits and network quality is therefore instrumental in getting the most out of a network.

 

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