Quick answer: Sales forecasting is the practice of predicting how much revenue your team will close in a future period, based on your current pipeline and past performance. A weighted forecast multiplies each open deal’s value by its probability of closing, then sums the results.
Why forecasting matters
A sales forecast is the bridge between your pipeline and your business plan. If you can predict revenue reliably, you can decide when to hire, how much stock to buy, and whether this quarter’s target is realistic. A bad forecast leads to overspending in good months and panic in slow ones.
The basic forecast formula
The most common approach is weighted pipeline forecasting:
Forecast = Σ (deal value × close probability)
A deal worth ₹1,00,000 at 60% probability contributes ₹60,000. Add the weighted value of every open deal and you have your expected revenue for the period. Probability usually comes from the deal’s pipeline stage — the later the stage, the higher the probability.
Sales forecasting methods
| Method | How it works | Best when |
|---|---|---|
| Weighted pipeline | Value × probability per deal | You have clean pipeline data |
| Historical | Compare to same period last year | Sales are steady and seasonal |
| Opportunity-stage | Probability fixed per stage | Your stages are well defined |
| Intuitive | Rep judgement per deal | Low volume, high-value deals |
A worked example
Suppose your pipeline this month holds: a ₹2,00,000 deal at 80%, a ₹1,50,000 deal at 50%, and a ₹1,00,000 deal at 30%. The weighted forecast is (2,00,000 × 0.8) + (1,50,000 × 0.5) + (1,00,000 × 0.3) = ₹1,60,000 + ₹75,000 + ₹30,000 = ₹2,65,000. That is your expected revenue, not the ₹4,50,000 raw pipeline total.
Improving forecast accuracy
- Keep data current: stale deals wreck forecasts.
- Set realistic stage probabilities: based on real close rates.
- Require close dates: a deal with no date cannot be forecast.
- Review weekly: compare forecast to actuals and adjust.
Forecasting in spreadsheets is error-prone. TatvaCRM, a BFSI-ready CRM built in India, calculates weighted forecasts automatically from your live pipeline and tracks historical close rates. Pair it with CRM reporting and analytical CRM for full visibility. Start free.
Frequently asked questions
› What is sales forecasting?
Sales forecasting is the process of estimating future revenue over a set period — a month, quarter or year — based on your current pipeline, historical close rates and market conditions. It helps businesses plan budgets, targets, hiring and cash flow.
› What is the basic sales forecast formula?
A simple pipeline forecast multiplies each open deal's value by its probability of closing, then sums the results. For example, a deal worth Rs 1,00,000 at 60% probability contributes Rs 60,000 to the forecast. Adding these weighted values across all deals gives your expected revenue.
› What are the main sales forecasting methods?
Common methods include pipeline (weighted) forecasting, historical forecasting (this period vs the same period last year), opportunity-stage forecasting (probability by stage), and intuitive forecasting (rep judgement). Most teams blend a data method with a sanity check from reps.
› How accurate should a sales forecast be?
There is no universal benchmark, but many teams aim to land within 10 to 15 percent of forecast. Accuracy improves as you clean your pipeline data, keep stage probabilities realistic, and review forecasts regularly against actuals.
› How does a CRM help with sales forecasting?
A CRM stores every deal, its value, stage and expected close date, so it can calculate a weighted forecast automatically instead of relying on spreadsheets. It also tracks historical close rates, making each forecast more grounded in real data.
› What causes inaccurate sales forecasts?
The usual culprits are stale pipeline data, overly optimistic stage probabilities, deals with no close date, and reps not updating the CRM. Clean, current data is the single biggest driver of forecast accuracy.