Posts Tagged ‘attribution analysis’

Making Sense of Online Campaign Results – Part 2

October 3rd, 2008

Earlier this week I posted the first of two articles on the challenges of making sense of online campaign results.  The first posting addressed the shortcomings of relying on cookie-based tracking to quantify results.  This posting addresses the supporting role of display and email ads, and explains why Google gets more credit than it should for your online marketing success.  I use an analogy we can all relate to… the proverbial Wingman.

The Wingman Effect
Another impediment to measurement is that most analytics platforms (including Google Analytics) were designed to attribute credit for an action to the last medium clicked. As noted above, user engagement typically entails multiple touch-points, both online and offline. Especially true with considered purchases, customers often make multiple visits, by way of multiple paths (e.g. display ad, search, direct nav), before taking action. Even if all of the visits are done on the same computer (and you COULD track using cookies), assigning credit for the lead or sale to the last click provides only part of the picture, and it generally rewards Search at the expense of Display, Email, Social and other media.

Using a crude analogy, consider the proverbial Wingman.  Picture two guys out at dinner and they see an attractive woman. You know the routine – one guy is the leader; the other is his Wingman.  The job of the Wingman is to support his pal.  He is often the one who initiates conversation and breaks the ice so his friend can move in for the kill. If the lead guy gets a phone number or email address, he knows he has the Wingman to thank for the assist.

In online marketing, Display is often the Wingman, the one who starts the conversation, while Search is the guy who gets most of the dates. But unlike the example above, Search gets all the credit and the Wingman’s contribution goes unnoticed. Moreover, because his contribution is not noticed, he may not be invited the next time they go out. Media planners often cut ad buys because they can’t see the supporting role they play in the engagement cycle. If they don’t drive directly actions, based on last-click analysis, conventional wisdom dictates you stop buying them. This ultimately works against you and will result in fewer conversations that lead to your desired results.

The takeaway for marketers is that you need to track interaction through the engagement cycle. Recently coined “Engagement Mapping” by Atlas (now part of Microsoft), savvy marketers are now tracking the first, middle and last clicks to determine how each media unit impacts results.

As we’ve seen firsthand on numerous campaigns, for every lead or sale you can directly attribute to an ad unit, there are 0.5 to 2.0 actions that are not traceable due to the reasons cited above.

While these are formidable challenges, do not despair – there are affordable, proven methods for overcoming both of them. Since this is how we make a living, I can’t share all the secrets with you. But I can provide some general recommendations.

First, you need to take a strategic approach to engagement mapping that will shed light on the various contributions (lead, supporting, etc.) your various online media units play in the engagement cycle. Once you understand which units create awareness, and which ones close the deal, you can produce smarter media plans.

Second, you should treat every campaign as a learning experience and make systematic media testing an ongoing program. By varying flight dates of various media, you can will gain better insights into the performance and contribution of your online media mix.

Lastly, you have to look at the overall lift in site traffic and activity, not just the visits that are directly attributable to specific ads. Don’t underestimate the tendency for people to take action on their 2nd, 3rd or 4th visits. Take a holistic view and you’ll see a much clearer picture.

As always, your comments are welcome, so make your voice heard.  And if you think this is great – please share with your colleagues!

Making sense of results from online campaigns – Part 1

September 29th, 2008

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!