• Identify which prospects are most likely to buy & which existing customers are most likely to churn.
• Assess their likely level of future claims & potential for fraud. Evaluate their probable customer journey & life-time value.
• Using this insight, establish the most appropriate channels, offers & contact strategy in order to maximise returns and minimise costs.
• Optimise pricing for risk and return, augmented by behavioural insight from ‘Internet of Things’ telemetry data where possible.
• Streamline fraud detection processes to minimise losses, again using behaviour where possible.
• Grow revenue by targeting customers with ‘next best’ up-sell and cross-sell offers.
• Quickly identify changes in competitor activity by spotting anomalies in market behaviour.

• Assess loan portfolios by predicting risk of arrears, bad debt & recovery.
• Predict loss ratios & provisioning requirements.
• Forecast likely future business performance levels given current & projected market scenarios.
• Use existing or purpose-built financial models to simulate & optimise planning cycles, maximising ROI from limited resources whilst achieving multiple conflicting targets.
• Identify key client behaviour patterns & transactional activity across a range of retail financial products. Assess the value, cost & risk implications of each.
• See how client & market dynamics are changing. Understand client needs & devise optimal treatment strategies based on the insight gained.
• Target customers for up-sell, cross-sell or retention activity as relevant.
•  Identify trigger events & warning signs. Detect & prevent potentially fraudulent behaviour & events.
• Enhance account access security using behavioural traits.

• Understand client behaviour & device usage patterns & use this insight to feed pro-active retention, up-sell & cross-sell activities, as well as the design of suitable tariffs.
• Optimise the allocation of offers, replacement devices, subsidies etc. to maximise their impact.
• Use the behavioural insight gained to devise new ‘value-add’ services & offers, enhancing customer satisfaction.
• Identify fraudulent or other potentially criminal activity & enhance account security using behavioural insights.

• Understand the different types of usage behaviour in your consumer base & the type of customers concerned.
• Use this insight to understand the cost, value, acquisition & churn hot spots in the business & how best to address the needs of each segment identified.
• Forecast future demand & understand its drivers.
• Predict the effects of smart metering roll out and prioritise customer groups needing assistance.
• Understand the impact of tariff changes on both your client base and the business.
• Optimise tariff changes to achieve your goals and responsibilities to more vulnerable sectors.
• Predict customers at risk of churn in time to take action to retain them, especially prior to fixed term deal expiry.
• Identify cross-sell & acquisition targets, how best to sell to them & optimise your marketing efforts to maximise ROI & achieve business targets for minimum cost.

• Simulate production processes, identify bottlenecks, understand & optimise workflows & schedules automatically to maximise operational performance & efficiency.
• React quickly to production line failures, late order changes, etc.
• Predict failure rates on products, components, machinery & fleets and identify possible causes.
• Perform preventative maintenance in time to avoid costly shut downs.
• Optimise delivery schedules & delivery routes.
• Maximise efficiency by optimising asset use & stock control to meet forecast demand or planned schedules in the most cost-effective manner, minimising the amount of capital tied up internally.
• Identify unusual stock variations, transport or warehousing activity, thus helping to combat fraud or theft.
• Utilise new sources of data on items & work flow from RFID & ‘Internet of Things’ telemetry, where available.

• Target potential students with appropriate courses & offers.
• Optimise pricing to balance demand, availability & returns.
• Predict no-show rates to maximise the use of resources.
• Segment candidate behaviour to identify different needs or potential, allowing tailoring of offers, message content or training plans.
• Predict drop out & success rates at both course & student level, allowing recommendations to be made to aid student decision making or to drive candidate selection, maximising the potential of both the individual & the institution.
• Optimise the scheduling & allocation of resources to maximise performance.