Leading Practices in MultiChannel Distribution in Insurance Navdeep Arora 8 April 2020
6 key characteristics of leading multi-channel distribution models Leading Characteristics
Examples
1 Integrating digital and traditional channels to provide a consistent and seamless customer experience regardless of entry point
Connecting all channels to new and legacy platforms to provide a single view of customer product holdings and enable policy administration Establishing a seamless transition between channels across entire purchase cycles
Co-developing new channels with customers and agents, by piloting new models and seeking advocacy to ‘roll-out’ model
Recruiting volunteer ‘agents’ to co-develop, pilot and advocate the benefits of ‘digital channel enablement’ and multi-distribution channel model. Enables agent buy-in Using Crowd Sourcing to understanding customers needs and co-develop digital capabilities
Moving from a ‘push’ to ‘pull’ product and pricing strategy, providing common modularised products for customer tailoring, agnostic of channel
Move away from channel specific products and prices to a common product construct and across all channels with ‘modular’ options to allow customers to tailor product to needs. Channel agnostic pricing Information query to existing book to prevent lower new business premiums for existing customers
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6 key characteristics of leading multi-channel distribution models Leading Characteristics
Examples
Reinventing rating models using customer segment analytics to price on customer behaviour and life time value, not channel economics
Customer segment behavioural analytics are applied into rating models to inform technical price on a ‘life time value’ basis. Pricing based on predictive indicators such as length of product holding, claims frequency, average claims cost, propensity for multi-product holdings, contact centre utilisation
Managing conflict by assigning all ‘direct’ sales to an agent by postcode and providing ‘trail commission’ to support retention
Agents are awarded ‘trail’ commissions for all direct ‘new business’ sales of customers within allocated postcode. Agents are incentivised to promote carrier regardless of sales channel and provide service Improving agent productivity by leveraging ‘predictive analytics’ to provide ‘attractive’ target risk profiles
Capturing information from all customer touch-points for data analytics and machine learning, driving personalised customer journeys, consistent communications and pricing and next best action
Capturing information on customer preferences and behaviours from ‘unstructured’ sources such as social media data and contact centre notes and ‘structured’ sources such as customer journey break-points Data used for machine learning, providing personalised customer journeys on digital channels and telephony scripts, next best action prompts connected to customer touch-points, recorded quotes for consistent pricing across channels
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