Open Storage
Dynamic Extensions
Avoid information silos by integrating your existing valuable data sources into our analytics solution and gain truly unique insights.
Real-Time
Data is updated near real-time using our unique high performance storage algorithms. You gain access to aggregate information in near real-time.
Everything Filterable
Fine tune queries to only view relevant information or data. Filters can be saved and reused.
Scalability and Parallel File System Integration
Enrich subscriber intelligence by flexibly joining external data and reports to the data in the datalake.
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Successfully navigate the complexities of data stored across multiple sources in a matter of clicks, converting it into strategic information and insight.
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Seamless integration of disparate data types, source systems and archival systems means that no crucial information is inaccessible, no data is un-mapped.
Subscriber Activity Meta data information
Aggregated data on the system available directly using the subscriber data storage API in the following ways;
Calls
Calls per day, badly terminated calls per day and their time, National Calls, International Calls.
SMS
Number of SMS’s, Number of SMS failed.
USSD
Usage of USSD services such as Mobile Money services, Mobile banking services.
VAS
VAS Service profile, Short code profile based on grouped VAS services.
VAS Codes
Individual VAS code detections, banking profile and other interactions to determine the subscribers social standing and purchasing power.
Data
PDP Context Accept or Reject, PDP Reject reason, bad APN or resources.
Handset data
Select properties of the handset, Device type, Data capability from device library.
Location
Subscribers location areas for activities based on subscriber activity on municipal level due to privacy issues – i.e. City Centre, Hotel Name, Suburb Name etc. or location-based on economic social factors as low-income, medium-income, high-income areas. Depends on system configuration – either configured from certified income data, or autodetection from populations handset cost price.
Handset Change Pattern
Shows a subscriber’s pattern of change of handsets throughout the subscriber life time as to assess the subscriber’s loyalty towards brands or technologies.
Socio-economic pattern detection
Are subscribers swapping in for newer or more expensive handset representative in relation to their income area or are the subscriber achieving more purchasing power and should be targeted for upsell.
Social networking
Allows the system to understand the social importance of one subscriber towards another i.e. are they related – and on which level – and might they be important in terms of social interactions based on call patterns and SMS plus service uptake.
