One well designed metric is better than multiple poorly designed metrics

Obvious, is not it? However, in practice we often go for quantity rather than quality. How about looking at this metric by region? What about product categories? Customer status? Average revenue? Those are all familiar ways to get to the answer by numbers rather than by insight. Instead of going on a slicing and dicing rampage, often it does pay to think twice about where you want to cut next, and cut very diligently. The is one of my zen habits of analytics - to answer the question with the least amount of data possible. Too many numbers on the report/screen translate into junk in the head.
Interesting take on the issue of choice and simplicity is described in the book Paradox of Choice by Barry Schwartz. Just adding to the eternal struggle of wanting more, but being unhappy when we get more. Simple is beautiful.

The most powerful analytical approach I have seen to date

This sounds a little pompous, but it is true. I have used this approach several times in different industries, and in different customer analytics settings, and every time it was huge success. Fortunately, the method is very simple (me like simple!), and can be replicated in a variety of situations.

1. Find a natural break of your total sales into units and dollars (aka rate and volume).
2. Pull the data by various groupings and trend over a few years. Find contribution of each part to the total and trend.
3. Take your volume variable, and repeat steps 1 and 2 again.

Here is an example. Total retail sales by week were broken down by the number of transactions and an average dollar amount per transaction and trended over five years. That of itself was quite a revelation. On the next level, we looked at the number of units per transaction and dollars per unit. Next, we looked at the breakdown of units and dollars per unit by category over time, and then split it into changes due to mix shift, or share of more expensive items that have longer life and lower sales volume, and inflation, or change in price of the same SKU. This was probably the most simple, perfect, insightful, and successful analytical project I lead.

Simple analytics is the best kind of analytics

"Make everything as simple as possible, but not simpler." -- Albert Einstein

Due to the nature of job, I get to have three measurement development and five measurement explanation conversations a week. Thus, it is topic near and dear to my heart. So dear, I sometimes want shoot myself rather than having another conversation about it. Here are my suggestions on how to build an easier road to measurement.

1. Concentrate on the bottom line. Did you make money or lose money? Did you improve overall sales or not? Did you improve overall churn? If you are not using control groups, concentrate on overall numbers. Granted it is much easier to show a stellar performance in one group of customers, but it is usually a false assessment due to self selection. If you have not moved the overall needle, you have not moved the needle. Overall revenue change is good. Overall revenue change year over year is even better. Revenue for a category or a certain selected sub-group of customers may be misleading.

2. Use control groups, if you can. Control groups are the great equalizer of metrics. It's the only panacea against a poorly designed measurement. Let's face it, shit happens, and metrics don't always work out the way we want them. Yet, if you measure it against a matched control group, even the worst metric will usually point you in the right direction.

3. Resist going into the weeds. Weeds require careful use of judgment, otherwise, they will turn into the hell of 100,000 numbers.You all know the drill, after the first look at the report, the executive asks, "and how about we look at those measures by cutting them by X... and Y, and of course, Z". Depending on how many categories are in XYZ, your total numbers in the report all of a sudden explode into a incomprehensible mess. So, this is where the analyst must go into the best diplomatic dance they can manage, saying that of course, we will look and see how that turns out, and surely report back. That's your chance to look at the weeds and see which ones are worth going into. If you see real insight that sheds light on the question at hand, the cut is worth reporting on. If it is just another iteration of the same numbers, simply cut into smaller pieces, then stay away.

It is my firm belief that a report should contain the least amount of data it needs to provide insight, and more data clog your understanding of the picture just as well as too much clothes clog up your clothes, so be ruthless with the stuff that does not add to the understanding. It may lead people on the wrong path, too.