Here’s my presentation on Leading Practices in Multi-Channel Distribution in #Insurance. I have put down 6 key characteristics of leading multi-channel distribution models and examples for each in detail.

1. Leading Practices in Multi-
Channel Distribution in Insurance
Navdeep Arora
8 April 2020

2. 6 key characteristics of leading multi-channel distribution models
Integrating digital and traditional channels
to provide a consistent and seamless
customer experience regardless of entry
point
1
Co-developing new channels with
customers and agents, by piloting new
models and seeking advocacy to ‘roll-out’
model
2
Moving from a ‘push’ to ‘pull’ product and
pricing strategy, providing common
modularised products for customer
tailoring, agnostic of channel
3
 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
 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
 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
Leading Characteristics Examples

3. 6 key characteristics of leading multi-channel distribution models
Reinventing rating models using customer
segment analytics to price on customer
behaviour and life time value, not channel
economics
4
Managing conflict by assigning all ‘direct’
sales to an agent by postcode and providing
‘trail commission’ to support retention
5
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
6
 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
 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 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
Leading Characteristics Examples