Cross-selling lead online generator for insurance and banking industries will help you to gain more appropriate leads from your database of clients

1. Gain more appropriate
leads from your database
of policyholders
www.rbcgrp.com
smart
cross
sales

2. 2
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https://www.insurancethoughtleadership.com/customer-experience/lowering-costs-customer-acquisition
https://www.retentionscience.com/blog/customer-retention-should-outweigh-customer-acquisition/
https://blog.agentero.com/2018/10/04/the-optimal-cross-selling-strategy-for-insurance-agents/
Insurance has the highest customer
acquisition cost of any industry 7-9x
7-9 times, it costs more for an insurance agency to attract a
new customer than to retain an existing customer
Companies focus more attention on
acquisition than retention 44/18
About 44% dedicate themselves to the acquisition, while only
18% focus on retention
Customer retention has a profound effect on
the bottom line 5>>25-95
When customer retention increases by only 5%, profits
increase by 25% to 95%
Cross-selling is a crucial component of
customer loyalty
The cross-selling opportunities for insurance clients are tremendous:
 61% of policyholders have only one policy with their agent
 29% have two policies
 10% have three or more policies
Why insurers need cross-selling optimization

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Four big mistakes why cross-selling fails
1 2 3 4
Cross-Selling
without Analysis
It isn’t easy to succeed without
choosing the target audience
appropriately, identifying their
preferences, and determining
what has value
Improper
Identification of
Target Buyers
The starting point of any
selling process is to find the
appropriate customers
Unprofitable
Customers
One in five 1) cross-buyers is
unprofitable, although the
average profit from shoppers
who cross-buy is higher than
those who don’t
Cross-selling the
Wrong Products
Cross-selling inappropriate, no-
value products to shoppers are
one more reason why it fails
1) https://hbr.org/2012/12/the-dark-side-of-cross-selling

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We know how insurers can get more
most appropriate cross-selling leads from their
database of policyholders

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We democratise AI
and we launch
smart
cross
sales

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How democratized AI works to gain leads?
You tag customers who have already bought the target product.
We predict other customers that might buy it.
Data preparation and
uploading
AI training
Prediction & accuracy
evaluation
Business case modeling and
data downloading
Data checking
• This is your homework –
compose data for customer
profiles and mark target
customers
• We will provide tips and
templates for data
preparation
• Upload prepared data
• Checking the data structure
• Data quality check
• Clustering and selecting
• Converting and
normalization
• Preparation of datasets for
train/test
• Making and training NN's
• Saving a NN models
• Prediction purchase
probability
• Calculation of accuracy
metrics
• Generating CumulativeGain &
CumulativeLift plots
• Modeling a business case by
changing the conversion rate
and average revenue per
customer
• Download a list of leads to a
file
2 3 4 5
1
Fully automatic data processing and customer similarity prediction
User interaction User interaction

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How democratized AI works to gain leads?
2 3 4 5
1
User interaction User interaction
Upload the data
Confirm the field name with the customer ID and tag of the target customers
Evaluating model performance
Download the data

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Key features and benefits
 Theshortlearningcurve,uploadthefileandgetresults
 Preparecustomerprofiledataonceandeasilychangetargetcustomers
asnewleadsareneeded
 Dramaticallytimereduction.Spendminutestogainthousandsofleads.
 Automaticdataqualitycheck
 Estimatefirst,thengetresultsandpay
 ReduceCACupto80%
 Increaseconversionrate morethantwotimes
 Identifyingthemostappropriatecustomersforcross-selling
 Personaldataofclientsisnotused
 Youcanscramblethedata
 ThetransmitteddataisnotsavedandisstoredinRAMonlyuntilthe
resultsarereceived
Simplicity
Fullyautomaticdata
processing
High-accuracyAI-
poweredmodels
Safety

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How much does it cost?
€0,1
per lead
EVALUATION
GET RESULTS
PAY
Are you estimating
100 000+
leads per month?
Request a corporate
price!

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Early Adopters Program values
€0,01
per lead
for the first
100 000
EVALUATION
GET RESULTS
PAY
Getinsightsanddataforincreasingcross-
salesbeforeanyoneelseinthemarket
TimeWinning
Personalmanagerforadviceindata
preparation,applicationtrainingand
support8/5inthefirstmonthofuse
Babysitterssupport
Newfeaturesdevelopmentand
customisationbasedonyourfeedback
Quickdevelopmentresponse

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How does it work?
1) https://www.kaggle.com/datasets/ishandutta/analytics-vidhya-insurance-cross-sell-prediction
2) https://unbounce.com/conversion-benchmark-report#finance_and_insurance
3) https://www.insuranceeurope.eu/publications/465/european-motor-insurance-markets/
Among the company's clients, it is required to find
potential ones to offer the option of damage
coverage. For testing, we have a label with
customers’ responses.
381k customer records1 with 12 columns and 5k
listed as target customers
Case #1: €4 641 990
of expected insuran c e premium
Recall is the fraction of the relevant results
(customers who bought the product) that are
successfully defined. All relevant customers = 7+75
Recall=75/(7+75)=0.91
Models performance
Expected premium: €4 641 990
Num of recommended leads 94 015
Average conversion in the insurance industry 0,079
Lift 2,5
The average premium for «Damage covering» €250
Cumulative gains – when we
select ~30% with the highest
probability according to NN
models, this selection holds
~78% of all “Damage
coverage” cases in test data.
Lift curve– when we select
~30% with the highest
probability according to NN
models, this selection for
“Damage coverage” cases
is ~2,5 times better than
selecting without a model.
Other results…
Processing time: 163,3seconds
Recall: 91,46%
CAC reduce to ~28,1% from the original
Cost of leads processing: 94015*0,1= € 9401,5
in the original
dataset
after
clustering
after forecasting
similarity
94 015
recommended
leads
Columns name Role
Unique ID for the customer ID
Response for Damage covering option Target
Gender of the customer Feature
Age of the customer Feature
Driving license Feature
etc. Feature

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How does it work?
1) https://unbounce.com/conversion-benchmark-report#finance_and_insurance
Case #2: €3 737 086
of expected insuran c e premium
Recall is the fraction of the relevant results
(customers who bought the product) that are
successfully defined. All relevant customers =
101+1168 Recall=1168/(101+1168 )=0.92
Models performance
Expected premium: €3 737 086
Num of recommended leads 27 278
Average conversion in the insurance industry 0,079
Lift 15
The average premium for «Damage covering» €137
Cumulative gains – when we
select ~7% with the highest
probability according to NN
models, this selection holds
~90% of all “Damage
coverage” cases in test data.
Lift curve– when we select
~7% with the highest
probability according to NN
models, this selection for
“Damage coverage” cases
is ~15 times better than
selecting without a model.
in the original
dataset
after
clustering
after forecasting
similarity
27 278
recommended
leads
The client portfolio of a risk insurance company has
1,600 clients who have bought a life insurance
policy. It is necessary to find clients similar to them
to offer them to buy a life insurance policy as well.
~2,3M customer records with 144 columns in
“Customer Profile 360°” 1)
 Demographics (city, age, region etc.)
 Information regarding holding policies of the
customer etc.
1,6 k records listed as target customers
Other results…
Processing time: 3444 seconds
Recall: 92,06%
CAC reduce to ~1,1% from the original
Cost of leads processing: 27278*0,1= € 2727,8

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A few tips for use
Create a list of clients who have once left your company.
In this list, tag customers who have returned after leaving and see the list of similar ones generated by the AI to
add them to the return campaign.
Get back churned customers
Make agents cross-selling work easier. Let them concentrate on building customer relationships and trust AI to
generate leads lists.
Join 10+ agents, and we'll give them a free training webinar.
Bring AI-powered cross-selling to your insurance agents
Create a common client list with your partner.
Get a list of potential customers for your partner's product or service. Similarly, get a list of potential customers
for your target product. Combine these two lists and make a joint offer: partner's product + your product
Create co-branded offers for clients

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Have more questions?
Request a meeting!

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Ready to gain more leads?
Request full access!

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Need more complex solutions?
Request a meeting about:
SMART segmentation PERSONAL recommendations CHURN prediction

17. philipenko@rbcgrp.com
www.rbcgrp.com
Sincerely,
Igor Philipenko,
Director of Advanced
Analytics at RBC Group