Persistency: the number that decides whether your distribution model works
Persistency is the share of policies (or premium) that remains in force after a defined period, most commonly measured at the 13th and 61st month. Indian life insurers typically report 13th-month persistency in the 75-88% band and 61st-month between 40% and 60%. Because most lapses are decided at the point of sale rather than at renewal, persistency is really a measure of sourcing quality, not of the collections team.
Persistency is the one insurance metric that punishes short-term thinking with a delay long enough that nobody feels responsible. A policy mis-sold in March looks like a success in March, a problem in the following April, and an unexplained drag on the 61st-month number five years later, by which time the advisor, the branch manager and often the product have all changed.
This guide covers how the ratio is calculated, what the numbers look like across channels in India, and the operating routines that actually move it - most of which happen at sourcing and in the ninety days before a renewal is due.
How persistency is calculated
The basic form is straightforward: of the policies issued in a given cohort, what proportion is still in force after n months. The complexity is in the denominator and in whether you count policies or premium.
Premium-weighted persistency flatters a book with a few large cases and hides a mass of lapsed small-ticket policies. Policy-count persistency does the opposite. Regulatory reporting in India uses defined bases; internally you should look at both, because the gap between them tells you which segment is leaking.
- 13th month - the first renewal. The cleanest read on whether the sale was understood and affordable.
- 25th, 37th, 49th month - the middle years, where affordability changes and servicing quality shows.
- 61st month - the fifth renewal. The number that determines whether a long-tenure product actually earned anything.
- Premium basis versus policy-count basis - report both, and investigate whenever they diverge by more than a few points.
- Grace-period treatment - decide whether a policy in grace counts as persisting, and never change the convention mid-year.
Cut persistency by acquisition cohort, not by collection month. A book improving because you sold fewer policies last year is not an improving book.
Indicative persistency by channel
Channel differences are structural, not accidental. Bancassurance sits on an existing banking relationship and an auto-debit mandate; direct digital buyers self-selected into the product; a first-year agency advisor selling to their natural market has neither advantage.
| Channel | Typical 13th month | Typical 61st month | Main leakage driver |
|---|---|---|---|
| Bancassurance | 82-90% | 50-62% | Relationship-led sales that outlive the relationship manager |
| Agency - experienced advisors | 80-88% | 48-58% | Affordability drift on high-ticket cases |
| Agency - first-year advisors | 62-75% | 30-42% | Natural-market sales without a needs fit |
| Direct and digital | 78-86% | 45-55% | No servicing touch after issuance |
| Broking and corporate agents | 75-85% | 42-55% | Weak renewal ownership between broker and insurer |
Most lapses are decided at the point of sale
When we look at lapsed cohorts alongside the sourcing data, the pattern is consistent. Policies that lapse at first renewal cluster around a handful of sourcing behaviours, all of which are visible at the time of sale if anyone is capturing them.
- Premium out of proportion to income - a commitment the customer could meet once, not annually.
- Mode mismatch - annual mode sold to a customer with monthly cash flow, or no auto-debit mandate set up at issuance.
- Weak needs documentation - no recorded reason the customer bought, which means no argument to bring to the renewal conversation.
- Contactability gaps - a phone number that belonged to the advisor's assistant, an address never verified.
- Advisor churn - the servicing advisor left within twelve months and the policy was never reassigned to a named person.
- Single-product relationships in a bank branch, where the customer sees the policy as a condition rather than a purchase.
The renewal routine that works
Renewal management fails when it starts at the due date. By then, the customer has already made a cash-flow decision. The insurers with the best 13th-month numbers start the conversation roughly sixty days ahead and treat it as a servicing touch rather than a collection call.
| Window | Action | Owner | Measure |
|---|---|---|---|
| T-60 days | Contactability and mandate validation | Servicing advisor / branch | Reachable ratio |
| T-45 days | Servicing touch - fund review, nomination, benefit recap | Advisor | Contacted ratio |
| T-15 days | Payment reminder with mode options | Automated plus advisor | Pre-due collection % |
| Due date to T+15 | Mandate retry and assisted payment | Branch operations | Grace-period recovery |
| T+15 to T+30 | Field visit for high-value or repeat-lapse cases | Field team | Revival conversion |
Orphan policies - those whose servicing advisor has left - lapse at materially higher rates than assigned ones. Reassigning them to a named advisor with an incentive on renewal, not just on new business, is usually the fastest available persistency gain.
Making persistency a field metric
Persistency improves when it is visible to the person who made the sale, at a frequency short enough to change behaviour. That means advisor-level 13th-month persistency published monthly, tied to incentives and to lead allocation priority, and renewal-due lists that appear in the advisor's daily activity view alongside new-business prospects.
- Advisor-level and branch-level persistency dashboards, cohort-based, refreshed monthly.
- Renewal-due and grace-period lists routed to a named owner with a visit or call SLA.
- Persistency weighting in the incentive plan, not just first-year commission.
- Lapse reason codes captured by the person who spoke to the customer, feeding a monthly Pareto.
- A revival campaign calendar - lapsed books respond well to structured revival windows and badly to ad hoc reminders.
Where Toolyt fits
Toolyt puts renewal and servicing work into the same mobile workflow advisors already use for new business. Renewal-due and grace-period cases are allocated to a named owner, visits and calls are logged and geo-verified, lapse reason codes are captured at the point of contact, and managers see advisor-level activity against the renewal book rather than a month-end collections report.
Frequently asked questions
- What is persistency ratio in insurance?
- Persistency ratio is the percentage of policies from an issuance cohort that remain in force after a set number of months, most commonly 13 and 61. It can be measured on policy count or on premium, and it is the standard measure of whether a book of business was sold well.
- What is a good 13th month persistency?
- For Indian life insurers, 13th-month persistency above 85% is strong, 78-85% is workable, and below 75% signals a sourcing-quality problem. Bancassurance books usually sit at the higher end and first-year agency books at the lower end, so compare within channel rather than across.
- Why do life insurance policies lapse?
- The dominant reasons are affordability - premium set too high relative to income, or an annual mode against monthly cash flow - no auto-debit mandate at issuance, a sale the customer never fully understood, loss of contactability, and orphaned policies whose servicing advisor has left without reassignment.
- How can insurers improve persistency?
- Fix it at sourcing: check premium-to-income fit, set up the mandate at issuance, and record the customer's stated need. Then run a structured pre-due routine starting sixty days out, reassign orphan policies to named advisors, publish advisor-level cohort persistency monthly, and weight incentives on renewals rather than only on first-year premium.
- What is the difference between persistency and lapse ratio?
- They are two views of the same book. Persistency is the share still in force at a given month, while lapse ratio is the share that discontinued over a period. Persistency is cohort-based and comparable across time; lapse ratio is period-based and moves with the mix of new business.
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