So there have been some unjustified slaps at Excel over the past week, as well as against hamsters, Ron Weasley, and the masculinity/femininity of people named Kris.  (The one against Clippy was totally justified.)

It seems only right, then, to talk about things that Excel is actually good at – doing calculations and presenting data.

There are two general schools of marketing people: art versus science.  The art folks appreciate the aesthetics of marketing and aim toward beautiful design and copy.  They will talk about white space and the golden ratio and L-shaped copy and such.  They elevate fad into trend into fashion. They were responsible for the Apple “1984” commercial and don’t understand why the guy with bad toupee on late night commercials is really successful. They can read the nine-point font they are proposing for your Website and don’t care if it is actually usable.

The job of the science people is to make sure that these people don’t damage your organization too much.*  Our motto is “Beauty is beautiful, but what else does it do?”, or it would be if we started having mottos.  Our tools are the well-designed study, the impertinent question (e.g., “I understand that our brand guidelines say to use Garamond, but our testing shows Baskerville converts better. Would we rather stick to the brand guidelines or raise more money?”), and the clear data presentation.

This last one can be hard for us. Too often, when we present our data, the data goes up against a beautiful story that people wish was true and loses.

So we need to cover not only what data you want to collect (today), but how to present it compellingly (tomorrow).

A standard Excel chart for mail pieces

The things I like to see, in approximate order, are:

• Enough things to identify the piece/panel/list
• Quantity mailed
• Response rate
• Number of donors
• Average donation
• Gross revenue
• Cost
• Net revenue
• Gross per thousand
• Cost per thousand
• Net per thousand
• Return on investment
• Cost to raise a dollar

That’s for a donor piece; for acquisition, I’d recommend adding cost to acquire.

So that’s what data to collect; tomorrow, we will look at how to present it.

* I am framing this as a battle largely for dramatic purposes. Ideally, you have a data person who respects the talents of a high-quality designer and a designer who likes to focus on what works. These together are stronger than any one alone.**

** But if you have to pick one, pick the scientist.