# How to Calculate Probability Into Sales Projection

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Sales projections are subject to inaccuracies when you are determining either the likelihood of making a sale or the anticipated size of the sale. Sales projection accuracy can be increased by incorporating probability estimates into upcoming projections, but you must subsequently revise the probabilities used by measuring past sales to refine future calculations.

Determine the likelihood that a prospect will move forward to making a purchase. Use past data when they are available. For example, assume that a sales force has found 20 qualified buyers and has begun the sales process, placing these prospects at the beginning of the pipeline. The sales manager estimates that one in 10 prospects eventually proceeds to make a purchase. This is a probability of 10 percent, or 0.1.

Calculate the size of a likely sale for any prospect that makes a purchase. For example, assume that a professional company offers three services, priced at \$1,000, \$5,000 and \$20,000, respectively. Based on past results or the sales manager’s estimates, you project that 70 percent of first-time buyers will choose the cheapest option, 20 percent will choose the middle option and 10 percent will choose the most expensive option. This results in a probabilistic calculation of (0.7 times \$1,000) plus (0.2 times \$5,000) plus (0.1 times \$20,000), or \$700 plus \$1,000 plus \$2,000 -- meaning that the likely sale will be worth \$3,700.

Calculate each prospect’s worth in sales value and the number of prospects needed for a particular sales target. In the example, the sale is estimated to be worth \$3,700; assume that the likelihood of each prospect making a purchase is 10 percent. Each prospect is worth (0.1 times \$3,700), or \$370 in sales. In this scenario, if a company wishes to make \$50,000 in sales in a given period, the number of prospects needed is \$50,000 divided by \$370, or about 136 prospects. It is possible that each of the first three prospects could purchase a \$20,000 package, enabling your sales team to make the quota, but the probability estimates indicate the necessity of 136 prospects to meet the target.

Revise probability estimates over time with actual data from past sales. Increasing either the number of sales made to prospects or the value of the sale to each purchaser increases the value of each prospect. For example, assume that, after six months, an experienced sales team increases prospect turnaround to 18 percent and moves 10 percent more purchasers from the cheapest option to the middle-priced package. In this scenario, the calculations for the sales estimate change. The revised likely worth of the sale becomes (0.6 times \$1,000) plus (0.3 times \$5,000) plus (0.1 times \$20,000), or \$600 plus \$1,500 plus \$2,000 -- \$4,100. The revised likelihood of purchase, 0.18, times \$4,100 equals \$738 in sales. In this example with revised probabilities, the value of each prospect has nearly doubled, so the company can reduce the number of prospects needed; alternatively, it may likely double its sales projections.