Machine Learning improve in proximity targeting, marketing profiling, prevent churn, and increase MROI
Our algorithms based on sub-group discovery are very effective at spotting small groups of customers that behave in a similar fashion. Successful Customer segmentation is paramount to reducing MROI through targeted actions.
Using a historical dataset of customers who churned in the past and discovering patterns within this data, we can forecast the current customers that have a high probability of churning. Marketers are therefore enabled to implement churn preventive actions that also improve MROI.
Our machine learning systems are an excellent way to predict the LTV of existing customers. Business forecasts and growth prediction often rely on LTV. As these forecasts drive marketing spending, good LTV prediction is a key reducing costs.
Since 2015 we have been doing hands on ML for the insurance sector, starting with bespoke 6-8 weeks data science training courses for your analytics team. Our approach favors improving an existing process rather than disrupting it via ML or AI bricks.
Our full offering of solutions covers:
Business use: identify small and large segments of customers with higher than average increase or decrease in volumes, and eventually bad expected loss ratio levels
Inputs: New business quotes data on two different periods of time, before and after the tariff change
Outputs: Segments presenting high risk of adverse selection (New Business)
Tools: Proprietary local exploration tool (Bottom-up approach)
Goal: Improve product pricing by reverse engineering competition prices for P&C insurance
Business use: Outbid competitors in real-time and increase market share
Inputs: Customers data + Brokers data
Outputs: Competitive index
Goal: Decrease churn rate or increase conversion rate through action
Business use: Generate concrete targeted actions based on the existing churn or conversion model
Inputs: Customers data and churn/conversion model
Outputs: Specific marketing action
Tools: Proprietary prescription tool
Goal: Improve the performance of a fraud detection team through ML across all insurance lines
Business use: Improve on an existing time-testd business rules approach
Inputs: Customers data + Claims data + Graph theory
Outputs: Enhanced Fraud Score
Proprietary affinity matching engine for HR agencies to find the perfect fit between candidates and job openings using MBTI type questionnaires, Resumes and job openings via NLP and machine learning.
Voice analysis for support centers to improve customer experience and reduce time to handle. Voice pitch-based fraud detection model.
Custom Online and on-premise Data-Science Accelerator program covering all aspects of machine learning for Insurance and Finance (data pipeline and Algorithmic Trading).