Policies per client (PPC) is the average number of active policies held by each household or account in an agency's book.
PPC = total active policies ÷ total distinct clients
Why it predicts retention better than anything else
The relationship is steep, and it is one of the most consistently repeated patterns agents and carriers describe (treat the exact bands below as the commonly cited shape, not audited figures — and measure your own book against them):
- A monoline client (1 policy) is the most likely to leave — commonly retaining in the 70s
- Adding a second policy typically lifts retention into the high 80s
- Three or more policies pushes retention above 95%
The mechanism is simple. Switching one auto policy is a twenty-minute errand. Switching auto, home, and umbrella — re-establishing the multi-policy discount, coordinating effective dates, re-documenting the mortgage — is a project. Each additional policy raises the cost of leaving.
Realistic benchmarks
Most independent personal lines agencies sit between 1.4 and 1.8. Agencies that treat account rounding as a deliberate program rather than an accident reach 2.2 and above.
Why it is the best automation target in the book
PPC is measurable, it is directly actionable, and the action is highly repeatable. Identifying every monoline client whose profile suggests a plausible second policy is a database query. Timing the outreach to a renewal or a life event is a trigger. Drafting the specific, relevant approach is a generation task.
Compare that to trying to improve retention through better service quality — real, but diffuse, slow to measure, and hard to systematize. Raising PPC moves the same outcome through a lever you can actually pull on a Tuesday.