Business Rules

Last post I talked about reporting analytics - the process of data tabulation, data aggregation, and other reporting techniques. In the online marketing world, reporting analytics tell us page views, unique visitors, impression volumes, click through rates, conversion rates, time on page and other interesting data.

But acting on that data requires a human to first read the report, figure out what to change, make the change, then remember to go back and check that the change worked. When you have hundred of web pages, thousands of keywords, dozens of page tags... how can you humanly manage this?

This same problem was solved by the financial services industry many decades ago. The key came when they implemented credit policy in the form of strict business rules. As economic forces changed from year to year, the credit policy was changed at a macro level, and passed down to local lending officers. This replaced individual ad-hoc decisions made at the branch level. Since that time, business rules and platforms that implement them have created an entire branch of analytics. Business rules are now used in almost all industries.

So how can we apply business rules to the online world? There are SEM bid management platforms that allow you to put in rules to increase or decrease bid prices automatically based on ad position and click through rate. Some display advertisers are now starting to team up with online list providers (in the form of cookie data) to do behavioral targeting. This is a simple form of business rules - if the user is in a certain segment, show them a certain offer. No doubt over time these business rules will get more sophisticted.

One of the maxims of behavioral targeting is that humans are terrible at stereotyping "who buys what product". For example, what is the demographic of people who will pay $15,000 for a bicyle? What is the demographic for mortgage applicants of condo's in London. In the first case, its men between 24 and 30 with an income between $25,000 and $35,000. In the second case, its predominantly professional single women. If you know this, you can implement business rules around these attributes to better target offers. That's behavioral targeting.

How are we to discover this in an automated way? What happens when we have hundreds of attributes that we can use, and dozens of ways of slicing and dicing and combing them? How can we possibly sift through all this?

Back in the 70's, statisticians invented the next type of analytics to address this... predictive analytics. We will talk about that next post!