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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:03,779 Let's say you're the sales manager for a company that makes flatware, 2 00:00:03,779 --> 00:00:06,118 say using renewable products like bamboo, 3 00:00:06,118 --> 00:00:08,835 and you've been asked to forecast the sales for the company. 4 00:00:08,835 --> 00:00:14,330 Your sales team has several accounts which offer opportunities to make a sales deals, 5 00:00:14,330 --> 00:00:17,894 and you're trying to forecast your sales metrics for the next year. 6 00:00:17,894 --> 00:00:22,335 Account refers to the companies where you provide your products in bulk. 7 00:00:22,335 --> 00:00:24,990 You're not selling to the individual customer here, 8 00:00:24,989 --> 00:00:28,709 but rather to the company that will buy the products in bulk. 9 00:00:28,710 --> 00:00:32,954 Now, let's go over sales forecast example using a bottom-up approach. 10 00:00:32,954 --> 00:00:37,500 This is the more common approach and it comes from the sales file historical data. 11 00:00:37,500 --> 00:00:41,969 We start with having one row for each person in our sales team. 12 00:00:41,969 --> 00:00:43,460 So, in the model, 13 00:00:43,460 --> 00:00:47,465 we are trying to understand for each person in our sales team, 14 00:00:47,465 --> 00:00:49,920 which account is this opportunity with? 15 00:00:49,920 --> 00:00:55,365 How many units is this opportunity likely to create a booking for or sale? 16 00:00:55,365 --> 00:00:58,675 Based on how long the sales contract would be for, 17 00:00:58,674 --> 00:01:01,579 what are the sales that we would generate if 18 00:01:01,579 --> 00:01:04,819 these units were sold each month in the contract term? 19 00:01:04,819 --> 00:01:07,549 That provides us with our bookings forecast. 20 00:01:07,549 --> 00:01:11,299 In column F, we can go one step further and we can 21 00:01:11,299 --> 00:01:15,590 ask how likely is the probability of a win for this opportunity? 22 00:01:15,590 --> 00:01:17,680 Based on that probability, 23 00:01:17,680 --> 00:01:21,400 we can calculate the weighted bookings forecast. 2088

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