Harness big data to drive faster adoption of revenue-generating services
eServGlobal’s Apeiron module allows service providers to manage, analyse and use rapidly growing volumes of data, by applying advanced data analytics and machine learning to drive customer engagement. This solution allows service providers to capitalise on the data, which exists both within and surrounding the network to ensure rapid service adoption and increased transactional throughput.
The Apeiron module applies data mining capabilities to existing data, which can be used to build customer segmentation lists to launch timely and compelling offers. The analytics provided by Apeiron allow service providers to evaluate service awareness, understanding and usage.
The volume of data available to service providers today, both within their system and in the surrounding networks (social networks, etc) provides an enormous resource for better understanding the customer base and delivering a customer experience which promotes faster adoption of new services and a higher number of transactions. The service provider is surrounded by data – both structured and unstructured – on a daily basis. However, it is not the volume of data that is important; it is the way it is exploited. To extract meaningful value from big data, you need optimal processing power, analytics, capabilities and skills.
How data analytics supports service providers
Marketing & Promotions : Targeting marketing & promotions efforts through customer segmentation using mobile data, leads to increased customisation of service provision
Service Offer : Using data analytics to increase service provider efficiencies
Pricing : Adapt pricing taking into account customer behaviour
For a more in-depth look into business data analytics, Apeiron offers an interactive web portal that includes key information such as service:
Apeiron provides two sets of default reports.The first one builds on service awareness, understanding and usage segmentation.The second measures electronic recharge and mobile money service activities such as transaction and subscriber numbers. Customers can also adapt this information to create additional reports.
All data is shown as a visual representation, which can be tailored to the customer’s interests. The data shows the relationship between specific profiles that may highlight remarkable actors and behaviours to indicate where further analysis is required. By creating new layers in charts, or tweaking existing ones, users are also able to access more complex data.