How to Use Statistics for Data Driven Marketing - dummies

How to Use Statistics for Data Driven Marketing

By David Semmelroth

Turning raw data into meaningful and useful insights for data driven marketing is what the field of statistics is all about. A statistic is essentially a measurement of something. More specifically, it’s a summary of several measurements.

Some examples: A batting average is a statistic that purports to summarize how well a player hits. Intelligence quotients summarize the scores from a test. Political poll results summarize how a group of people answered certain questions. Stock market indices summarize the performance of a group of stocks.

The field of statistics is, to some extent, the black sheep of the mathematical sciences. A popular saying lists the degrees of dishonesty as “lies, damn lies, and statistics.”

The fact is, in many cases, the conclusions reached by performing statistical analysis are just downright counterintuitive. Numerous studies show that even people who are well trained in statistics can be really bad at applying that training to real-world situations. In other words, when people try to intuitively interpret data, they are pretty bad at guessing.

The counter-intuitive nature of statistics leads inevitably to its misuse. People half-jokingly (or maybe not) claim that, given any set of data, they can make it say whatever they want it to. This is known as fudging the data. In its simplest form, fudging involves ignoring or excluding data that doesn’t support the desired conclusion.

People have a innate tendency to fudge data when it comes to their past experiences. Horoscopes, for example, are popular for precisely this reason. People look for and remember situations when their horoscope was right on target.

The same sort of thing happens when people report their lottery winnings. People never forget the $1,000 prize they won two years ago and will tell the story over and over again. But they leave out the fact that they’ve spent $20 a week for 5 years on buying lottery tickets, which works out to more than 5 times their winnings.

Fudged data is the enemy of good database marketing. It prevents you from learning what is and isn’t working. When you decide to analyze data regarding your marketing campaigns, you need to analyze all of it. You can’t pick and choose the results you want to see.

Despite having a somewhat spotty reputation, the science of statistics really is a science. Proper use of statistical techniques can bring some order to what at first appears to be a chaotic mass of data. Careful analysis can provide you with useful insights into your customers.

An example of the power of statistics when it’s properly used can be found in the gaming industry. Casinos are associated with gambling. But the casinos themselves are doing no such thing. They understand the statistics — the odds — associated with every game they operate. And they set the payouts.

If you’ve ever been to Vegas, you’ve seen countless advertisements about slot machine payouts. They entice you with claims that they pay out something close to 99 percent of what they take in. True enough. But that means the casino keeps 1 percent. And literally millions of silver dollars are dropped into those machines.

The business of database marketing uses statistics in a similar way. The idea is that you want to stack the odds in your favor when you choose whom to communicate with. The majority of the people who receive your offer may not respond.

But you use the power of statistical methods to guarantee that enough will respond to cover the cost of your marketing efforts and provide a healthy profit to boot.

The results of data analysis are often open to interpretation. Often the results are inconclusive. But respect whatever the data is or isn’t telling you. Don’t respond to disappointing results by asking that the data be analyzed differently.

Trying to find what you expected to see in the data will not help you learn. And it won’t improve the effectiveness of your marketing efforts. Unless you have specific concerns about the way an analysis was done, trust what your analyst is telling you.