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Insurance domain data engineering use case
Primary Use case
- Error-free usage reporting and notifications to help you stay in the loop
- Prevent revenue leakage
Secondary Use case
- High bill review
Client Expectations
Identify anomalies in the broadband usage pattern.
A multi-national insurance service provider needed to uplift their technical layer and enhance customer experience. They wanted to replace their existing technology layer as maintenance was cumbersome. They wanted to improve customer experience by creating a model that considers real time changes and advises users to connect to the closest provider.
What did we do
GBee framework was used to revamp the data engineering layer
GBee framework was used to revamp the data engineering layer. Data already present in the existing system was converted to Rich Data. Using the time and space context embedded into the very dna of the platform, GBee was able to provide dynamic classification on a stream of data. The platform was also successful in providing a real-time queryable rest endpoint.
key features
Learn how we stand out from the crowd
benefits
Feeds downstream functions
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
Behavior/Animaly by accounts
- warn/Connect Behavior
- Suggest Plan Upgrades
- Revenue Leakage
- Customer Experience, Early Detection, Enhance Customer Trust
Behavior/Anomaly by Region/Division/ CMSTS
- Warn Outages
- Advice Infra Expansions
Behavior/Anomaly by Plan/Policy
- Advice Plan/Policy refinements
- Advice Plan/Policy performance
Behavior/Anomaly due to Maintenance
- Warn Software Patches
- Warn Device Upgrades
- Warn Network Maintenance
Behavior/Anomaly due to External Events
- Influences on usage Trends
- Group/Regional Behavior
- Preparedness for Impact/Opportunity
results
The numbers say it all
- 5 recommendation
- 31 Predictors
- 100’s of device types
- 10's of Plans and Policies
- 10's of Regions and Divisions
- 1000’s of network groups
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96%
Prediction Accuracy
5
Recom
10 Billion
Readings / day
120 Days
History
our techstack
We build and run efficient application
Make data-driven decisions
