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!

Types of Analytics

Like all great things, there are many flavors of analytics. There is a general trend of complexity, where each level of analytics builds on the previous ones. So lets start with the basics.

Reporting analytics is about the simplest form of analytics. People generate reports all the time in all sorts of business. You can tally sales by region, losses by credit vintage, fraud by zipcode, and so on. There is an old adage that you can't fix something if you can't measure it. Reporting analytics is exactly that - measurement.

In the online world, Google analytics is one of the most common reporting analytics systems. This system tracks page traffic, conversions, keyword and ad performance. In the hands of a sophisticated marketer, tools like this are useful in fine tuning marketing campaigns.

But reporting analytics is such entry-level, that many purists don't even like to use the word "analytics" in the same sentence as "reports". I've had people tell me that they have fully implemented analytics, just to find out that all they have done is put in Google analytics. To some extent Google has co-opted "analytics" in the online world. If only they'd called it Google Reporting, alot of us would be more content.

In the offline market world, reporting systems were built starting in the 1960's. Essentially as soon as people could put enough data, like mailing lists, into computers, they created reports as their first step down the analytic journey. Reporting systems are still an important component of any analytics platform today, and there are very sophisticated tools that bleed into data visualization.

However, 99% of reports are never looked at. Relying on a human in the loop to digest the reports and make adjustments to campaigns is well proven to be unreliable, error-prone, and inefficient. This was realized in the offline world 40 years ago, and resulted in the development of the next stage of analytics...

Welcome!

Inuvo is breaking the mould on affiliate marketing technology for the web. Our recently launched Inuvo Platform takes affiliate marketing to an entirely new level, with features, controls and reporting that are incredibly compelling.

At Inuvo, we are also changing the performance of affiliate marketing with a big investment in analytics. This blog is all about analytics, what they are, how they work, and how we are using them at Inuvo to make a measurable differences.

So, what is Analytics?

Analytics is the art and science of taking data and using statistics to try to describe a situation or improve a decision outcome. Analytics are all around us: when we apply for a loan of any type, dozens of different analytics are invoked. They check out credit worthiness, they try to figure out how profitable we will be for the bank, they even try to determine if we are truly who we say we are. Analytics are used to target direct mail offers, detect fraudulent behavior, and to optimize the prices of products.

In the offline marketing world, analytics have been used extensively for 40 years. Even though there is a lot of buzz around analytics in the on-line world, we are still very early on its adoption. Like in so many other industries, analytics has become a key part of how a business functions. For example, you could not start a credit card company today without first hiring about a hundred statisticians to figure out all your analytic systems.

I believe that analytics will fundamentally change online marketing by dramatically improving response rates and reducing bad traffic. At Inuvo we are assembling a team of very experienced statisticians to bring this to a reality.