Cost Per Customer: Free Worksheet

One of our loyal readers asked to have a spreadsheet created, one that calculated Cost per Customer and Profit per Customer.

Your wish has been granted. Please forward the following links to those in the marketing community who you feel would benefit from the content:

A link to the highly popular Cost Per New Customer and Profit Per New Customer post.

A link to the Cost Per Customer / Profit Per Customer Free Spreadsheet (in traditional Green Bay Packer green and gold ... change the cells with a gold background).


Profit Calculation:

Many of you have asked how to compute profit, specifically, how to identify the "profit factor" outlined in the spreadsheet.

The profit factor is the percentage of demand that flows-through to profit. Here's an example:

• You generate $100 of demand.
• Of the $100, the customer returns $20, leaving $80 of net sales.
• Of the $80 net sales, your cost of goods is $35, leaving $45 of gross margin.
• It costs you, on average, $11 to pick/pack/ship $100 of demand, leaving $34 of profit.
• The profit factor = ($100 - $20 - $35 - $11) / $100 = 34.0%.

In this case, 34% of demand flows-through to profit. If you generate $100 of demand, and you spend $30 of marketing to generate the demand, your efforts were profitable ($100 * 0.34 - $30).


Why Profit Per Customer?

Some of you want to know why you should use profit per customer, feeling like cost per customer is an appropriate metric --- especially among online marketers.

Web Analytics folks --- here's your chance to shine --- get the metrics in the worksheet above plugged into your web analytics software tool, and calculate profit per customer. The metric is important because you can compare this metric against long-term customer value. If you lose $10.00 generating the order, but generate $20.00 profit in the next twelve months, then you want to participate in marketing activities that lose $10.00 per customer. When you use cost per customer, you are not tracking average order value or profit --- you're still doing a good job, but you can add additional color to your analysis.