Monthly Archive for September, 2008

Making sense of results from online campaigns - Part 1

Published
by
Steve Latham
on September 29, 2008
in Analytics and General
. 0 Comments

When I talk to marketers these days, one of the most frequently cited needs I hear about is the ability to measure (and make sense of) results from online campaigns. More specifically, the gap in most organizations is their ability to trace results (e.g. engagement, leads or sales) back to individual ads, email blasts or search phrases that drove the desired results. They have data but it often doesn’t make sense. While many have figured out how to use tools such as Google Analytics for measuring conversions and attributing those to the ad unit (e.g. display ad or search phrase) that preceded the conversion, the results often do not make sense, or are contradictory to what their media plan said should happen.

If you are in this category, don’t despair – you are in good company. My personal research indicates that 94.59% of marketers are struggling with the same issues. I believe the challenge of measuring results that make sense is two-fold: 1) shortcomings of cookie-based tracking, and 2) the fallacy of last-click analysis, which I refer to as the “Wingman effect”.

Crumbling Cookies

As consumers become smarter, savvier online users, they are becoming more deliberate and less impulsive in their decision process. With so many more options at their fingertips they can easily do research and comparison shopping before buying that new camera or requesting information on your services. For big purchases, they often confer with others, e.g. sending a link to that new road bike to their friend or spouse to get their input). Another issue is the growing trend of “surf at work, buy at home”, where they do research on one computer and take action on another. Since we rely on cookies to track actions for each individual, these issues impact our ability to measure results. Here’s an example:

Let’s say you are at work, and just as you are thinking about how much you need a vacation, you see a VacationsToGo ad for a Caribbean cruise on your MyYahoo home page.You click through and like what you see but you have a lot to do and cubicles don’t offer much privacy for vacation shopping. Later that day you tell your spouse about the trip and tell him to go to VacationsToGo.com to learn more. He Googles it and finds it through a paid search listing. Later that evening, in the safety and comfort of your home, you jump on your personal computer and navigate directly to VacationsToGo.com to book the trip. Five minutes later, you are thinking about where you’ll eat in Cozumel.

Since the site sees that your home computer does not have its cookie, it assumes you have not visited before. Consequently, it views you as a 1st visit buyer, and does not know that you responded to the MyYahoo ad (or that your spouse found the site through a paid search ad). Both of the prior visits will appear to be a waste of ad spend. The poor analyst who has to measure performance of ad buys has no clue that the Yahoo ad started the engagement and that the paid listing contributed to the process. He will only see that at 7pm a 1st time visitor booked a cruise.

The example above illustrates how multiple visits and machines reduce the effectiveness of cookie-based tracking. The increasing use of cookie-cleaning tools adds to the dilemma. If, as widely reported, 40% of 3rd party cookies are either not accepted or deleted within 30 days, we’re blind to what impacts a significant portion of our results. Consequently, it’s difficult to take an accurate measure of which media buys are performing, and which are not.

So with that we conclude the first part of this subject. In my next post I will introduce the Wingman and give new hope to display media salespeople. But for the mean time, please tell me what you think! If you found this to be of value, please comment below.

Later!