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PREDICTIVE CHURN MANAGEMENT FOR LIFE INSURANCE

insurance

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CLIENT BACKGROUND

Client is a pan India Private life insurer following a multi-channel
distribution strategy with a vision to help people plan their life better. It
has been offering a suite of insurance products and investment plans through
digital and offline channels since its inception in 2008.

 

Life Insurance products are long-term products meant to cover the risk of
untimely death of an insured person or allow an individual to build wealth for
future needs. Such needs can be child’s education, child marriage or savings for
retirement. Wealth Builder plans can be market linked like ULIPS or can offer
fixed rate of return through the period.

 

Most of the plans have minimum premium paying term of 10 years with pure term
plans going as high as 35 years. These plans have been traditionally been sold
through agency channel where agents get in touch with prospective customers and
explain those benefits of the plan. Agents, in turn, are offered fixed
percentage of commission for every policy sold. This commission is higher for
the first year and gradually reduces over next years.

 

BUSINESS OBJECTIVE

Persistency is a key metric through which Life Insurer measures the
effectiveness of its retention strategy. For most practical purpose 1st year
persistency is defined as a number of policies still in force after 1 year of
acquisition and similar for 2nd-year persistency and so forth. Client’s
persistency metrics (13th, 25th, 37th ) were below the industry average. Poor
persistency ratios are a cause of high concern for any life insurer given the
high cost of customer acquisition and market competition.

 

It was hence desired to understand factors behind poor persistency through the
quantitative approach and recommend scientific measures to improve same.  It was
also expected to devise optimal renewal strategy that currently involved agent
follow-ups, email reminders, SMS alerts & telephonic follow-ups.

 

SOLUTION

Our team of domain experts and data scientists spent a week at client premises
to understand

 

 1. Customer acquisition process & different acquisition channels along with
    their strength & weakness.
 2. Renewal strategy; how it was implemented then and whether it varied for
    different customer segments or not.
 3. Internal data fields available for study, namely, Customer Information
    collected during application stage, customer interaction data through
    emails, call logs etc, insurance product details, acquisition channel
    information etc.

 

It was observed that

 

 1. Renewal strategy was sub-optimal with premium amount as the only criteria
    used for pro-active reach out prioritization.
 2. Monthly policy renewals were of the order of 11k and call centre capacity
    was 12k. Assuming it takes 5 minutes for every call, a single agent will be
    able to make approx 1700 calls per month with a total of 21k calls across
    all agents. This meant an average of 2 calls per customer for renewal
    reminders. Optimal call centre reach out strategy across other life insurers
    had at least 4-5 calls at various days before renewal. This clearly meant
    call centre resources were stretched and we had to either increase the
    capacity or prioritize.
 3. 85% of the policy base had yearly renewal with the remainder evenly spread
    across monthly, quarterly, half yearly.
 4. There are several potential reasons for a customer not renewing the policy
    starting from the non-existence of customer need anymore, purchase made for
    wrong reasons (ex: tax savings only) without evaluating actual needs,
    misselling by the agent, low returns compared to the market, wrong agent
    practices, non-timely renewal follow-ups etc. Agent sourced policies were
    more prone to churn.

 

It was decided to have a churn score for each policy to be renewed. The score
would indicate the risk associated with policy not getting renewed, higher the
score, higher the risk. Valiance data science team started with looking at
different customer datasets and built a single view at a policy level. Datasets
included were Customer Demographics, financial product details, payment
transaction history, financial product performance and customer engagement
through different channels.

 

Each policy renewal was classified into lapse/non-lapse taking into account
grace period. Further study involved

 

 1. Studying the impact of each factor on lapse event. Team created additional
    variables like ratio of annual premium vs income, urban/rural location
    category to study additional effects. Correlation studies, scatter plots
    were used to communicate the results.
 2. Multiple predictive algorithms were built using Logistic regression and
    Random Forest techniques. Team shared results with the client to arrive at a
    model that was an appropriate fit and made business sense in explaining the
    outcome.
 3. An outcome model was used to score upcoming renewals on the basis of
    likelihood to lapse. Scores generated for each policy coming up for renewal
    on monthly basis.
 4. Customer Segments were created on basis of churn score and Annual Premium.
    Contact Strategy was finalized on basis of churn score and premium at stake.
 5. Customers with higher churn score and premium >25k were pursued through
    calls and visits if needed. Customers with lower churn score and lower
    premium were contacted via SMS and emails. The frequency of emails and calls
    was adjusted as per segment.

 

OUTCOME

 1. Optimal renewal strategy was developed for customer segments basis churn
    score and premium amount. High churn score and high premium amount were
    proactively reached out by agents. Low score, low premium amount customers
    were sent email & SMS reminders only.
 2. Targeted retention efforts resulted in an increase in Policy Persistency by
    20% over 1 year with increased revenue of 3M USD.





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