A Great Predictive Metric: "Power"

You've probably heard about all of the geeky metrics that folks compute ... and you're probably saying "PRODUCE SOMETHING LESS NERDY, NOW!"

If you want something less geeky, something that tells you where your business is headed in the next year, calculate a metric called "Power".

Simply put, "Power" is the sales expected from your twelve-month buyer file in the next year.  Here's how you calculate it.

• Step 1:  Segment your 12-month buyer file however you wish.  We do this as of a year ago, say 2010.08.23.  Let's pretend that you have five segments ... A / B / C / D / F.  Let's pretend that you have 100,000 customers per segment.
• Step 2:  For each segment, calculate the average amount a customer in that segment spent from 2010.08.24 to 2011.08.23.  Let's pretend that As spent $100, Bs spent $50, Cs spent $30, Ds spent $20, and Fs spent $10.
• Step 3:  Count the number of customers in each segment as of today.  Let's pretend that today you have 80,000 As, 100,000 Bs, 120,000 Cs, 120,000 Ds, and 120,000 Fs.
• Step 4:  Multiply last year's value by this year's file counts, yielding file "Power"!

At this time last year, you had 500,000 customers who generated 100,000*100 + 100,000*50 + 100,000*30 + 100,000*20 + 100,000*10 = $21,000,000.  At this time last year, your twelve-month buyer file was capable of $2,100,000 of "Power".

As of today, you have 540,000 customers who are expected to generate 80,000*100 + 100,000*50 + 120,000* 30 + 120,000*20 + 120,000*10 = $20,200,000.  You have more customers, however, your customers aren't capable of generating as much "Power" as customers were capable of generating last year.

This is a particularly important concept for online / e-commerce folks, because your web analytics tools make it really hard for you to see how powerful your customer file is.

Best of all, this metric is predictive in nature, it doesn't tell you what happened in the past, it tells you what is likely to happen in the future.

Retailers tend to run this metric on a monthly basis (some weekly), so that they can understand key inflection points.

Power --- a metric you need to calculate!!

Mega-Metrics: 12 Month Buyers

This week, we're going to explore some of the important metrics calculated in your garden-variety Multichannel Forensics analysis.

We start with the twelve month buyer file.

The metric is easy to calculate. Count the number of customers who purchased from your business in the past twelve months.

Next month, do the same exercise, and keep iterating through time.

Now graph the results.

The healthiest businesses do not grow by increasing spend per customer, or by increasing average order value. The healthiest businesses grow by continually increasing the number of customers who purchase in a twelve-month period of time.

I like to calculate several sub-metrics for the rolling twelve month buyer file. If I have, say, 50,000 twelve month buyers, I like to monitor the following:

• The number of buyers who are existing (purchased last year, purchased prior to last year).
• The number of buyers who are new (purchased last year, and are new to the file).
• The number of buyers who were lapsed prior to the past twelve months.

The trends across these three metrics are so important to measure. Over time, you'll clearly get to see what it is that is driving growth in your customer file (hint, it is usually customer acquisition activities).

You can use this style of mega-metric for any business. E-mail marketers frequently keep track of twelve-month e-mail buyers, as well as e-mail twelve-month clickers, comparing to last month and last year.

This metric is a lagging metric --- in other words, it only tells you what has happened, it has no predictive power. But it is an important one to track. This metric is worth posting at the front door of your office building, for all employees to view and understand.