Just Starting out? You Still Need a Sales Forecast
Posted: Sun Feb 02, 2025 8:27 am
Who it’s for: It’s quick. It’s dirty. And it’s completely isolated from what’s going on elsewhere in the market. It’s like looking at a weather app and packing your bag for vacation next year, assuming the weather will be the same. Anything out of the ordinary and you’ll end up soaked, or worse. Ultimately, there’s still value in looking at your historical data (if you have it), but it should be used as a benchmark, not the fundamentals of your sales forecast.
7. Multivariable Analysis
What it is: As you can probably tell by now, the previous methods have their own pros and cons. The multivariable analysis method, however, takes the best parts of all these forecasting methods, and puts them together into one complex, analytics-driven system.
Here’s an example using data from the lead-driven, opportunity stage, and sales cycle methods we discussed earlier:
Let’s say you’ve got two sales reps hustling the same bangladesh telegram data or a similar account. The first one is working on a $10,000 deal and has just finished a successful product demo. Based on your rep’s individual win rate for this stage of the deal, your multivariable analysis says he’s 40% likely to close the deal this quarter, giving you a sales forecast of $4,000.
Your second rep is selling a smaller, $2,000 deal and is earlier in the process, yet their win rate is through the roof, also giving them a 40% chance of closing the deal this quarter and a forecast of $800. Your total sales forecast at this point for the quarter would be $4,800.
Who it’s for: While the example I used was incredibly simple, in real life the numbers rarely work out like that. Accurate forecasts based on multivariable analysis involve an advanced analytics setup that might not be in the cards for startups with a smaller sales budget. Also, you need clean data. So, if you don’t have sales reps who are diligent about tracking their deal progress and activities, this won’t work.
7. Multivariable Analysis
What it is: As you can probably tell by now, the previous methods have their own pros and cons. The multivariable analysis method, however, takes the best parts of all these forecasting methods, and puts them together into one complex, analytics-driven system.
Here’s an example using data from the lead-driven, opportunity stage, and sales cycle methods we discussed earlier:
Let’s say you’ve got two sales reps hustling the same bangladesh telegram data or a similar account. The first one is working on a $10,000 deal and has just finished a successful product demo. Based on your rep’s individual win rate for this stage of the deal, your multivariable analysis says he’s 40% likely to close the deal this quarter, giving you a sales forecast of $4,000.
Your second rep is selling a smaller, $2,000 deal and is earlier in the process, yet their win rate is through the roof, also giving them a 40% chance of closing the deal this quarter and a forecast of $800. Your total sales forecast at this point for the quarter would be $4,800.
Who it’s for: While the example I used was incredibly simple, in real life the numbers rarely work out like that. Accurate forecasts based on multivariable analysis involve an advanced analytics setup that might not be in the cards for startups with a smaller sales budget. Also, you need clean data. So, if you don’t have sales reps who are diligent about tracking their deal progress and activities, this won’t work.