Decision Support
❯
Predict
❯Moving Beyond Generalised Linear Models: A sophisticated pricing strategy incorporating competitor pricing
A fairer and more personalised credit decision
For:
Payment and credit providers
Goal:
Improved Customer Experience
Moving Beyond Generalised Linear Models: A sophisticated pricing strategy incorporating competitor pricing
For:
Customer pricing
Actuaries
Goal:
Increase Revenues
Machine-learning based triage to determine low-severity patients that can be fast-tracked to admission in ED due to their short discharge length
For:
Emergency Departments in hospitals.
Goal:
Improved Employee Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities
Automated threat enrichment intelligence in Cyber security
Goal:
Improve Operation Efficiency
Deep reinforcement learning for personalized treatment recommendation
For:
Clinicians who prescribe long-term and adjustable treatments
Goal:
Improved Employee Efficiency
Applying machine learning to predict patient risk for in-hospital new infections
For:
Hospitals
Goal:
Improved Customer Experience, Anticipate Risks
Large Companies
Small Companies
Management - Market Intelligence
For:
Product managers and Marketers.
Scope:
Growing active users for a music streaming service by identifying high-value customer behavior.
Goal:
Improved Customer Experience, Increase Revenues
Weather Forecasting in Agriculture
For:
Farmers
Goal:
Anticipate Risks, Improve Operation Efficiency
AI for customers' loss prevention of telecommunication services
For:
Telecommunication companies
Goal:
Improved Customer Experience
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities.
Moving Beyond Generalised Linear Models: A sophisticated pricing strategy incorporating competitor pricing
For:
Customer pricing
Actuaries
Goal:
Increase RevenuesProblem addressed
A general insurance company, Suncorp found that competitors’ pricing strategies were affecting their sales conversion and revenue due to undercharge or overcharge. The call center staff found that they needed to manually look at multiple websites from competitors in order to stay competitive with a new offered price at discretion. There was no consistent customer pricing framework for the staff to override in price when making a new offer.
Description
Suncorp partnered with external consultant to reverse engineered competitors’ pricing to understand the actuarial rates. With this, they could develop a new rating factor - the competitor price - which became part of their ML model to calculate new insurance premiums for customer pricing. As a result, the call center staff no longer had to look up competitors’ websites for their prices in order to stay competitive, removing this manual process and opmitising productivity. Their pricing strategy now reflects their market positioning and the competitive landscape.
Outcome
The company’s insurance premiums and prices are now more competitive while still reflecting its market positioning. In addition, this new pricing strategy increases conversion and revenue. The manual work done by call center staff is now also automated to improve productivity.
Raw Data
AI: Perceive
Machine Learning
Expert System
AI: Understand
Predict / Forecast
AI: Act