Cube vs. Mosaic for a P&C Agency's Renewal Book Forecast

A book of business with a high renewal retention rate behaves close to a subscription in its predictability, month to month revenue is largely a function of the existing book plus modest growth, which makes a straightforward spreadsheet forecast reasonably reliable. An agency growing aggressively through new business, by contrast, has a less predictable near-term revenue pattern that depends heavily on the current sales pipeline, closer to how a brokerage or an agency selling into a competitive market would need to forecast.

If your book is renewal-heavy and stable, a simpler model may be entirely adequate; if new business is a large and growing share, you need a tool that can handle pipeline-based forecasting with real rigor, not just a renewal roll-forward.

Carrier contingency and bonus payments depend on factors like the agency's loss ratio and overall volume with that carrier, factors an agency doesn't fully control and often can't confirm until the carrier calculates and pays it, sometimes months after the underlying policy year ends. Treating contingency income as guaranteed revenue in the forecast overstates near-term reliability; treat it as a range based on historical payout patterns instead, and recognize it only once the carrier has actually confirmed the amount.

An agency that builds its budget assuming contingency income at last year's level, without acknowledging the real uncertainty in that number, risks a real gap if loss experience or carrier appetite shifts even modestly.

If your team already tracks renewal retention, new business pipeline, and contingency history in a spreadsheet, Cube's approach of syncing that spreadsheet against your agency management system keeps the model where it's understood, with less manual export from policy and commission data each month.

This tends to be the better fit for a smaller agency where the principal personally reviews the book each month and wants that review kept in a familiar, fully auditable format rather than a new dashboard interface.

Once you're running enough producers that reconciling individual books of business, renewal rates, and new business pipeline in a spreadsheet becomes unwieldy, a dashboard rolling that up can help agency leadership see which producers are driving growth and which books are at renewal risk. Confirm in a demo that Mosaic can separate renewal revenue, new business revenue, and contingency income into distinct lines rather than one blended commission figure.

A consolidated dashboard also makes producer-level accountability easier to see clearly, since agency leadership can spot a producer whose book is shrinking well before the annual production review makes it unavoidable.

A renewing book isn't guaranteed to renew at the same rate or the same premium every year; a hard market can push premiums up even as a client's coverage stays the same, while a client shopping their policy at renewal can shrink the book even if the relationship stays intact. Track renewal retention rate by producer and by book segment, and flag any account showing early signs of shopping behavior, so a renewal-driven revenue model reflects the real risk of attrition rather than assuming every policy simply renews.

Build these habits into the agency forecast:

• Forecast contingency and bonus income as a separate, clearly labeled line based on historical payout patterns, not blended into base commission revenue.
• Track renewal retention rate by producer and by book segment, and flag accounts showing early signs of shopping their policy.
• Model a hard market's effect on premiums separately from client retention, since higher premiums can coexist with a shrinking book.
• Confirm in a demo that the tool separates renewal revenue, new business revenue, and contingency income.
• Test producer hires against a realistic ramp-up period instead of a flat revenue-per-producer assumption.

Jirav's driver-based approach is useful when you're deciding whether to hire another producer, since a new producer typically takes a real ramp-up period to build a book large enough to be self-sustaining, and modeling that ramp against realistic new-business assumptions gives a more honest read on when that hire actually pays for itself than a flat revenue-per-producer assumption would.

A producer hired into an unfamiliar specialty line or an unfamiliar geography should be modeled with a longer ramp than one working a niche they already know well, since existing carrier relationships and referral networks matter as much to early production as raw sales skill does.