Telecom operators around the world are all facing the same issue: voice and SMS revenues are shrinking while data consumption does not compensate for that loss. This results in a decline in average revenue per user (ARPU) that is amplified by fierce competition and strong regulation.
As fighting against data is not an option, telcos need to find new ways to leverage data. In theory, this shouldn’t be too difficult, as data covers much more than just voice or SMS services. 
In hopes of boosting data revenues, some telecom operators have tried to introduce their own messaging applications but with limited success. This leaves many telecom operators scratching their heads, looking for ways to improve monetization of mobile data.
In order to do that, they need to understand that it is not only about making the clients pay for their data usage; it’s about taking them onto a data journey. Concretely, this means optimizing his/her data experience and creating the need for data.

The power of Deep Packet Inspection

At Real Impact Analytics, we found that Deep Packet Inspection (DPI) is a great way to stimulate this data journey. In short, DPI is a way for telcos to analyze data packets that are exchanged between smartphones and the network. 
Telcos typically limit DPI to technical purposes like cyber-security, network management and billing. Some also use it for specific promotions like “Free Facebook” but not to set up end-to-end marketing strategies. In fact, DPI can be leveraged to :

  • tailor price plans
  • promote the right content to the right users
  • accelerate the customer data journey 
  • control cannibalization effects

We have listed three major takeaways from testing this in emerging markets.

1. Recommend the right apps to avoid cannibalization

Analyzing the subscriber base according to average monthly data consumption helps to identify different consumption clusters, from low data usage to high data usage. DPI proved that promoting the right apps in the right clusters is key to boost ARPU. Since DPI allows you to understand the customers’ interests, you can target them better.
For example, the data journey could start by encouraging non-data users to browse the internet and show them the benefits of going from just browsers to slightly more consuming apps, such as navigation services, email or social media. Once the client sees the benefits in having access to parts of their lives on-the-go, they will be more inclined to move on to use it more frequently, typically using Whatsapp, Viber, Skype and other VOIP services to stay connected at all times. The most advanced stage within the customer journey is to use stream content on-the-go, e.g. Whatsapp, Netflix, Spotify, YouTube, etc.

2. Pay-per-byte is bad for ARPU

One of the first key insights taken from that case study was about pay-per-byte pricing model. Even if this is supposed to be the most profitable model, it turns out that:
• Churn is much higher in Pay-per-byte model (e.g. 50 cents per MB) than in established data plans (e.g. 5 euro for 200 MB). Given the high acquisition costs, this erodes value over the long term.
• Migrating a pay-per-byte user to a data plan increases data consumption as it engages the client into the data journey, creates the need for data and eventually tends to boost ARPU. This insight can be leveraged in Western geographies, where pay-per-byte is still used when a client exceeds his monthly data limit. In this case, it is smart to suggest the client to upgrade to the next package rather than risk losing him because of a bad pricing experience.

3. Limit smartphone subsidies to very few targeted users

Smartphone subsidies (e.g. subscribe to a one-year data plan and get a smartphone for one euro) are a traditional lever to customer loyalty programs. 
However, our DPI analysis revealed that getting a smartphone does not correlate with increased data usage (it only happens for 20% of smartphone discoverers). Furthermore, half of the low data users already have a smartphone. 
In summary, our research has indicated that understanding your subscribers’ interactions with mobile apps are key to planning campaigns aimed at increasing ARPU. They give a clear understanding of how subscribers’ usage changes over time and how it is influenced by plan changes, targeted offers or phone upgrades.

By Jérôme Urbain, Technical Leader Data for Partners at Real Impact Analytics