The Science of Online Marketing (Part 2)

Let’s talk for a minute about science.  After all, I’ve made the assertion that there was a science to online lead capture.  So with my having made such an assertion, it is reasonable for you to expect me to back it up.  What do I mean by “the science of lead capture”? In the introductory article I defined science as a branch of knowledge or study dealing with a body of facts or truths systematically arranged and showing the operation of general laws: e.g., the science of online lead capture.

From that definition, you can assume two things:  that I have used a very systematic approach and that I’ve identified certain principles with regard to online lead capture.  Let me throw out another definition:

Scientific Method – A process that is the basis for scientific inquiry. The scientific method follows a series of steps: (1) identify a problem you would like to solve, (2) formulate a hypothesis, (3) test the hypothesis, (4) collect and analyze the data, (5) make conclusions.

Now I am the first to admit, I’m no rocket scientist.  But I am very analytical by nature so I’ve followed a very methodical approach to understanding and refining online lead capture.  Remember, I nearly failed in this business because of a lack of customers, so I made it a mission to generate an abundance of leads.  As it turns out, the approach I used was exactly that of the textbook definition of the scientific method.

First I identified the problem I wanted to solve.  For me it was capturing the maximum number of leads (customers) from my website.  Remember, I initially did this for me, not for other agents.  Next, it was forming a number of hypotheses or assumptions, one at a time, about how online customers act or behave.  Some of those assumptions were correct, while others were incorrect.  How do I know?  Because the next step in the scientific method is testing the hypotheses or assumptions.

Step three is testing the hypotheses.  In order to properly test assumptions it’s important that you have enough data to provide a valid conclusion and that you build in systems to accurately measure your results.  In order to have a margin of error of 2% (a generally accepted level of accuracy) you must collect a minimum of 2,500 data records for each assumption you are testing.  And you must similarly have as many data records for the baseline against which you are comparing.  So in order to compare two choices against one another, you must collect data from at least 5,000 customers over the same time period.  For most of us, that involves spending a lot of money to produce that much traffic.

Next, we collected and analyzed the data.  In order to measure the results we designed our test “LCM Gateway” so that we could route our Internet traffic to three different options while measuring and recording the data (metrics) for each option, independently.  And to safeguard against differences in customer responses for various times of the day and days of the week, we designed a proprietary traffic routing system that randomly sends real-time traffic down three separate paths.  This allows us to simultaneously gather data for a baseline as well as to test two different assumptions against that baseline and against each other.

Finally, after extensive testing and analysis, we were able to make conclusions as to customer preferences, each time allowing us to improve our results.  Each conclusion led to permanent changes in our LCM Gateway technology.  Each minor change added to our LCM Gateway’s efficiency, and ultimately to its power to produce outstanding results.  Unfortunately, however, we’re testing and measuring customer preferences, and customers are fickle.  What they like this month may be entirely different than what they preferred 90 days ago or what they will like 90 days from now.  That means that we never can stop testing or the moment we do, we become obsolete.

Over the next few articles, I intend to share with you some of the things we have learned in this process of years of testing.  Some may come as a surprise while other may seem obvious.  What amazed me, personally, is how some of the seemingly insignificant and subtle changes resulted in some of our biggest gains.  I have also been surprised by the somewhat fluid nature of customer preferences online.  Just like styles and tastes change, so do online shopping habits.

As you read this series of articles, I would encourage you to take notes.  Many of the things I will discuss will allow you to drastically improve your online marketing results, and I sincerely hope, put money in your pocket.  In fact, I will make you this promise: if you read and apply the principles I’ll offer you over the next few weeks, it will change forever the way you approach online marketing.  I know it’s changed mine.  Now stay tuned for the next installment.

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