Click-Through Campaigns for Dummies

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If you are a Digital Marketer, chances are you are using social, paid, email, and/or affiliate campaigns to drive click-throughs to your website. While your marketing tool may be showing you a good volume of clicks, it is important to not simply take those numbers at face value. Several factors can inflate click volumes, so before you dedicate your whole budget to an ad platform, it’s important to have an analytics program in place to validate the impact of your campaigns.

Reporting gaps

There can be a large gap between the “clicks” that your ad platform reports and the impact you might see in Google Analytics or Adobe Analytics. Here’s three reasons why that might be:

1. “Clicks” do not equal “Visits”
   Email servers and ad networks usually count the number of “clicks” on a link. This can be very different than the number of “visits” counted in Adobe or “sessions” counted in Google. Users can click a button multiple times, whether it be inadvertent, or in frustration because of a long load time. Perhaps the user opened several different links at once to see all the options before clicking again to move forward. In each case, your ad network will count multiple clicks, while Google and Adobe will only count a visit once in a 30-minute period. Having an analytics program will ensure that you’re looking at the right metrics and not comparing apples with oranges.

2. The Click’n’Close

   Ad networks may report clicks from users that never actually reach your site. Users may close their tab or browser before the page load data can be sent to Google or Adobe. Ads can sometimes be so well embedded in native content that users don’t realized they clicked an ad until a new window starts to load - prompting them to close it immediately. While you might think your web pages load quickly, you might be surprised. If your link uses a jumpstation, vanity URL, or redirect, the intermediate step adds time. Now think if the user clicked on your link from an iPhone 4, while out of network, on a business trip to Estonia. Your ad networks will report clicks for visit where the page never successfully loads.

3. Filters
   Your implementation of Adobe or Google Analytics will have filters to block some traffic. Ad networks will have their own set of filters. Keep in mind, humans are not the only ones using the internet. Bots ping servers and web crawlers index pages all the time. While analytics platforms work to filter out this non-human traffic, ad networks are actually incentivized NOT to have too strict of filters. If you are paying them per click, why should they discriminate? Bots often have telltale signs that you can watch for. Crawlers hit hundreds of pages in one visit and bots request the same URL hundreds of times. Bots are usually appear as “direct” traffic and come from IP-Geo locations that don’t match your target market. An analytics program can help you improve your filters and figure out which ad networks provide quality traffic and which don’t care who is clicking.

Start small and scale up

If you want to know what impact your click through campaign is really having, it is important to not try to do everything at once.

Start with smaller audience

Why should you limit your audience? A bigger audience is going to have a bigger impact on your bottom-line, right? In actuality, broadcasting your content can be costly and ineffective. Your messaging might be perfect for a social post, but completely unwelcome as an email. Instead of blasting your content through all means possible, start with a single message, on one platform. Once you have a baseline, iterate and discover the best channel and ad network for your message. Likewise, by specifying a single metro area for your campaign, you can have a more cost-effective impact. False traffic will be easy to identify and eliminate before scaling up to a national level. By starting off in a single metro area, the campaign will have limited impact, but it will dramatically ease the execution by having a single landing page with no need for translation. You will also get a greater response if you are able to tailor the messaging to the metro area.

Make it an easy transition

You may think it is important to ‘wow’ users on the landing page with a big image or video. You may want to enable users to do anything they want by providing the full header and footer. In my experience, that strategy plays out well in front of the executive board, but poorly in real life. Executive are already familiar with your site and are looking to be impressed by something dramatic, while normal users want to see what they were expecting to see. The best landing pages have few buttons and limited text. The page needs to be light-weight and mobile-friendly to keep the barrier to entry low. Reuse the text of the link to affirm that the user is in the right place and that any details they need will be shown if they click the CTA.

Grow into optimization

A/B optimization is a great way to improve your campaign methodology. Unfortunately, too often the job gets tacked on to a Digital Marketer’s other responsibilities. Test data can get polluted and the results get misconstrued. A dedicated analyst is needed to ensure proper hypotheses are tied to the right success metrics for a clear conclusion that can then be validated. Without someone dedicated to optimization, false-positives can create misconceptions that negatively impact the company for years.

Various attribution models used in GA.

Evaluate success

Having a low cost-per-click is nice, but it is not an accurate measure of success. Revenue is a much better metric, but even that can be insufficient. While you may have created a campaign to drive purchases, users may have signed up for your newsletter instead. The revenue from the campaign would tell one story while the lifetime value of those newsletter sign-ups might tell a different one. You should monitor each step of the user flow through your click through campaign as well as any other site goals you have, since there are many ways your campaign can have value. Just as there are many KPI’s to watch, there are multiple attribution models. You should start with last-touch, but don’t stop there. Each channel and ad type will serve a different purpose, so you’ll want to find the attribution method that fits the ad, not the other way around.

Google Analytics Attribution Isn't What You Think It Is

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What is the attribution model used for Google Analytics’ standard Acquisition reports?  Last Interaction, right?  Not exactly.

The default model in Google Analytics is, in fact, Last Non-Direct Click.  While this may seem to be a subtle difference, it can have a big impact on how you attribute conversions, and how you interpret your data.  According to Google, this is when this model is useful:

If you consider direct traffic to be from customers who have already been won through a different channel, then you may wish to filter out direct traffic and focus on the last marketing activity before conversion.

But this makes some big assumptions, especially to make it the default model in all standard acquisition reports.  It can be very useful to know if a considerable percentage of your traffic is coming directly to your website to make a purchase - And this can help inform your overall acquisition strategy.

As you can see, there can be considerable differences between the two models:

While you can’t change the attribution method used for the standard acquisition reports, you can change or compare models in the Attribution Model Comparison Tool, and we fully encourage you to do just that; and not just for Last Interaction vs. Last Non-Direct Click, but take a look at how all of the models compare.  This, along with utilizing the Multi-Channel Funnels Top Conversion Paths report, provides essential insight into the roles that your various channels play.

Also, be wary of how Google Analytics defines and counts sessions.  As we all (should) know, a session (or visit) is a group of page views and interactions associated with a single user (on a single web browser) within a certain timeframe, typically the industry standard of 30 minutes (i.e. a session will end after 30 minutes of inactivity). 

However, in addition to time-based expiration Google Analytics also uses campaign-based expiration.  That is, any time a new traffic source value (e.g. utm_source, utm_medium, utm_campaign, utm_term) is recorded for a particular user Google Analytics will consider it a new session, regardless if this activity occurred within the 30-minute session window.  This can inflate the total session count for your website as well as diminish visibility into the traffic source that initially drove the visit and can also mis-credit conversions.

As an example, consider this scenario experienced by one of our e-commerce clients.  After migrating to a new e-commerce platform there was a requirement for all existing users to update their passwords the first time they logged into the new platform.  This triggered a password reset email for which the inbound links were tagged with campaign values to indicate that the user clicked through from that password reset email.  Now, if a user initially came to the website from a Paid Search listing, added an item to the cart, and then attempted to log in to complete their purchase then they would be prompted to reset their password.  After receiving the email and resetting their password then that would now be counted as a new session and the eventual purchase would be credited to the password reset email and not to Paid Search.

Now, I know what you may be thinking - just remove the campaign parameters from the links in the email, right? But it can still be useful to identify how many users are clicking through from those emails, we just don’t want to consider it a new session, nor do we want it to receive credit for the conversion.  What we’d want instead is to have Entry source dimensions (i.e. the source/campaign that initiated the visit) but also general source dimensions to provide insight into in-session activity.  One option here would be to use Internal Promotions tracking or a unique query parameter mapped to a custom dimension as an alternative to utilizing the utm parameters in these types of scenarios.

These are just a couple of examples of Google Analytics methodologies that differ from industry standards (or at least from the methodologies followed by other major web analytics providers), and yet they are not clearly called out in the Google Analytics user interface.  Rather they require a bit of digging through the documentation to find.

So be sure to keep these things in mind and do your due diligence into analytics platform methodologies before undertaking an analysis and presenting your data.  It is incredibly important to ensure that your insights are framed in a way that accurately represents what is actually happening so that you maintain trust in the data throughout the organization, and are making the correct decisions to optimize the business.