How do you visualise terabytes of data into a simple user interface? Deloitte Digital was tasked with doing exactly this for a large Telco in South Africa and the following is the results of this challenge.


Deloitte Digital have developed the ".comm Telco Data Visualiser" app which takes big data of telcos, utilities and large corporate customer segments (in this case a telco) and visualises these within an interactive bubble chart to allow for 3 dimensions of the data. The app also features an interactive timeline where a user can drag the timeline to view the various customer segments for various daily timeframes.

Unlike historical telco data, the data was grouped by user behavior and analyzes the way people interact with their mobile devices as well as tracking their migration to different uses and spend patterns.

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On the summary screen, a user is able to view a snapshot of the data over the selected period. Here they can also view their top performing customer segments.

Customer Segmentation

In the customer segmentation layer, each customer segment gives you a breakdown of:

1. Average revenue per user (ARPU)
2. The percentage that the customer base has changed (compared to previous day)
3. Number of subscribers in that segment (also indicated by the size of the bubble)
4. Average calls in per user
5. Average calls out per user
6. Average data used
7. Average SMS/MMS sent
8. Percentage of overall subscriber base this segment makes up

Customer Migration

When on customer segmentation, if a user taps on a segment, they will then be able to view the migration of users from the selected segment to all the other segments and from all the other segments into the selected segment.

This allows users to easily view migration patterns of their users and the percentage change this makes to that overall base.

Detailed Customer Segment

On the detailed customer segment layer, a user can view additional information on that segment. For example:

1. Customer distribution around the country via a heat map
2. Average data consumed over the day
3. Average revenue per user over the day
4. Average data sessions over the day
5. Average data voice calls over the day