Sales

Forecasting sales: monitor pipeline generation and coverage

Calculate pipeline coverage and a weighted sales forecast, then use scheduled checks to flag gaps before your next forecast review.

Coverage is a question, not a promise

Illustration · scheduled checks every 12 or 24 hours

Open pipeline

$150,000

Remaining target

$60,000

2.5×Pipeline coverage
$150,000 ÷ $60,000

0×Review floor: 3×4×

2.5 < 3 · condition met

Example Slack notificationReview deal quality and creation paceA matching check queues a notification. This is not an exact message preview.

Forecasting sales is not just adding up open deals. You need to know which deals belong in the period, how likely they are to close, and whether enough new pipeline is being created. Kisu does not generate a forecast. It can check forecast inputs and calculated metrics in BigQuery or Google Sheets on a schedule.

Separate the target, the forecast, and pipeline coverage

A sales target is the result you want. A forecast is an estimate of what will happen. Pipeline coverage compares available open pipeline with the remaining target. Keep these concepts separate so a large pipeline is not mistaken for committed revenue.

Use a common period and currency. In this example, all amounts are illustrative:

A 2.5× ratio is not a universal good or bad result. Compare coverage with your own win rates, deal quality, and time left in the period. If the remaining target is zero or negative, handle that case explicitly rather than dividing by zero.

Calculate a weighted forecast without false certainty

A simple weighted forecast adds closed-won revenue to the sum of each open deal’s amount multiplied by its estimated close probability. If two open deals are $20,000 at 50% and $30,000 at 20%, the weighted open value is $16,000. With $40,000 already won, the estimate is $56,000.

Use probabilities grounded in your own history and deal context. Stage probabilities are estimates, not guarantees. Remove duplicates, exclude lost deals, and review close dates. A probability-weighted total cannot account for every deal risk or shared market shock.

Monitor pipeline generation as well as the current forecast

Pipeline generation measures newly created or newly qualified pipeline during a defined period. Choose one definition and keep it consistent. Do not count a deal twice because it changed stages or was edited.

A rolling seven-day pipeline generation metric can show whether future coverage is weakening even while the current forecast looks stable. Use a review floor based on your sales cycle. A team with a six-month cycle should not assume this week’s new opportunities will close this quarter.

Turn the calculations into scheduled checks

In Sheets, prepare a summary tab with separate numeric columns for coverage, weighted forecast, and newly qualified pipeline. Keep one summary value below each header. Kisu can check Minimum of Coverage below your chosen floor, or Sum of Weighted forecast below a review threshold. Do not mix detail values and summary totals in the same selected column.

In BigQuery, calculate the metric in SQL and return one Boolean comparison. Use explicit handling for missing inputs and a zero remaining target. A NULL result means no data in Kisu, not a healthy forecast. If missing inputs should trigger an alert, encode that as a TRUE condition.

Use Kisu to prepare for the forecast review

Connect your source, add a tested Slack destination, and choose a 12- or 24-hour schedule. Kisu queues a notification for each successful matching check, including repeated matches. It does not fetch CRM data, infer probabilities, or change deal stages for you.

When a check matches, review slipped close dates, concentration in large deals, and recent pipeline creation before changing the forecast. Pair this check with a stale-opportunity alert. Together, they help the team bring specific questions to the next sales meeting.