Have you heard about display re-targeter cookie bombers misleading your last-click attribution?
Cookie bombing is a tactic used to get bigger budgets because the attribution stats will boost attribution metrics. The issue here is that when you use a last-interaction attribution model, cookie bombing claims the attribution for a lot of conversions.
Cookie bombing is a “spray and pray” method of tagging user cookies with ad impressions by purchasing cheaper and low-quality ad impression inventory that never gets seen by users. Only the advertiser’s retargeting cookie gets credit for the conversion with attribution – not any of the campaign tactics in the marketing funnel before this last interaction. Cookie bombing ‘games the system’ of last interaction attribution and claims credit for something where it did not contribute value.
The solution can be to limit your retargeting partners, put strict frequency caps and only give attribution to viewable ads. The last view-through must actually be viewable to the user, otherwise it is worthless. That’s why you can consider strict limitations on frequency capping, or the number of times your ads appear to the same person over a given time period. You can set this to only serve impressions every 3 hours – they will do a better job of pacing the messaging and showing users the right ad at the right time.
Do you suspect your attribution model is being gamed by attribution models? Let us know in the comments!
So, you are interested in learning how digital marketing channels such as Display and YouTube contributed to a conversion (or multiple conversions). In other words, this is what Avinash Kaushik calls MCA-ADC: Multi-Channel Attribution Across Digital Channels. You’ve already seen this other Last-Click blog article and as a result you enabled your Google Analytics account to tie display impressions to your conversions.
You’re happy with the new insights and especially when you’re responsible for Display campaigns, very happy with the new impact you’re seeing in the multi-channel attribrution funnel. Now you’ve seen that Display makes an impact on your conversions, you want to integrate this in your attribution model – but how to actually click your way through Analytics? Here’s how.
In the same blog by Avinash mentioned above, he mentions that the core premise of the time decay model is that the media touch point closest to conversion gets most of the credit, and the touch point prior to that will get less credit based on a smart and simple algorithm. It passes the common sense test, as overall it does seem to make sense that the further back in time a media touch point is, the less credit it should get. After all, if the touch points were so incredibly magnificent, why did they not convert? So, in this example we will switch the attribution model from Last-Click to Time Decay, and we will adjust credit for impressions.
First, let’s go into Analytics and scroll way down to Conversions:
Then, click Model Comparison, and then click the little blue links under Custom Models that says Create New Custom Model:
You will see this lovely screen pop up:
Second, you can adjust days prior to conversion on top of the Time Decay tool based on your Time Lag report in the Multi-Channel Funnels folder. This means that an interaction that happens more than 7 days before the conversion will get half the credit of an interaction within the last 7 days before the conversion.
You can apply custom credit rules, which are custom rules that apply uniquely to your company. Here, you can start to value your campaigns based on the interaction they deliver. Here we adjust credit for impressions. If there is an impression (people only see the ad), you can start to value that too, in addition to clicks that are the default interaction that gets credit.
Step 3, see the cryptic line “credit all impressions 1 times other interactions in the conversion path.”. Huh, what’s that? Well, maybe you value an impressions a bit less than ads that get people to click on them. This setting at 1 times means that you equally value an impression to another interaction, most commonly a click. So, if you leave this setting, that means that when a prospect sees an impression and then goes to Search and clicks one of your ads, and then converts, both the display impression and the search campaign get 0.5 conversions ascribed to it.
Last, you can go nuts with the setting to adjust credit for impressions based on how much hours or days before a visit to your website:
You’ve done it! Now your attribution model is Time Decay adjusted by also giving credit to display impressions. Well done, now realize that this is not the end but just the beginning; remember that this switch does not mean you’re right, it just means you’re getting less wrong with your attribution.
Lately there is a lot of talk about how “last-click is dead” and people are discussing other models, like first-click attribution models. However, there is an important distinction to be made between rules-based attribution models and algorithmic attribution modeling.
When you agree to apportion credit based on frequency, recency, perceived strength of the interaction, etc. you are using rule-based attribution. The default attributon model in most tools, last-click, is a rule-based model. By today’s standards, this rules-based approach seems crude. The althernative is the algorithmic approach (statistical algorithms) to attribution in order to the determine the weights. The algorithmic approach is similar in that it aims to weight things like frequency, recency and the quality of the interaction in coming up with a composite credit for each conversion.
I hear you thinking sure, got it. But that’s not all there is to it – because if you use last-click attitribution, that means that you can only award credit to campaigns that get you clicks. And that might be nice for Search, but where does that not work out at all? Right, in Display and even more so in video, where clicks are far from the right metric to judge effectiveness by. In Display banners and video, the impressions and views drive your prospects to conversion. Furthermore, your clickers might not be your converters (source: Quantcast PDF). I hear you thinking “sure, I get that, but how to measure all that?” – Well, make a first move into a better, or less wrong, attribution model by including impression impact in your model and assigning some credit to it.
So, are you rushing into your Google Analytics at this very moment to include impression interactions into your custom attribution model (see here how). Let us know how this works for your campaigns!
Although it has been noted many times that “last-click attribution is dead”, most marketers still use last-click attribution, often simply because it’s the default setting in the tool they use to measure conversions, like Google Analytics. You might say “But Google Analytics has these other models you can pick!”. Sure, but they are still click attribution models, aren’t they? Totally awesome if the first-click attribution model suits your marketing better than last-click, but what do you do when you’re using media where The Click is not the most relevant KPI to judge the media?
In a study, Quantcast argues that:
“For display advertising, clicks aren’t just suboptimal—they’re anti-optimal. And they’re likely to produce significantly poorer results”. (PDF here)
It’s not a surprise that marketers are so click-focused; the Click and the CTR are great indicators of your campaign health – when you run a Search campaign, that is. And Search just happens to be the channel online marketers spend their lion’s share of budget and time one. In search, optimizing clicks is essentially optimizing for conversions because they’re so closely aligned. With display ads, if you optimize for clicks, you get . . . clicks. But, you don’t want clicks, you want conversions, right?
The display experience is quite different from the search experience for consumers, and the influence of a display ad is more nuanced than that of a search ad. Display is most appropriately seen as a touch point that stimulates consumer interest along the path to eventual conversion and purchase. Rather than clicking and immediately buying, ad viewers waited, visiting website sometime later, and perhaps via another channel or device, to make a purchase. Mind you that this holds for paid search ads as well; it takes 3.5 days on average for a customer to convert after an initial click on a paid search link.
Optimizing for clicks is a proven and effective approach for search advertising. For Display, optimizing for clicks to drive conversions will not get you far; if you’re optimizing your campaign for clicks, there’s a good chance you’re actually anti-optimizing for sales. And although clicks may work for some banner campaigns with limited reach, looking at clicks will certainly keep you from seeing the value in video. Which should not be super surprising, as videos are meant to be viewed; last time I checked Watch Time still was YouTube’s video KPI and not Click Time, or is it?
So, instead of clicks, you should optimize toward your ultimate objective—the campaign conversion rate—rather than toward the click-through rate and focus on developing the necessary systems and skills to understand the true impact of your ad investments throughout your customer’s path to purchase. Are you a Google Analytics user? Then a great example of a ‘necessary system’ is this ever-in-beta feature called “GDN Impression in Multi-Channel Funnels Reporting”. The Analytics blog post nicely aligns with the story above:
Every customer journey is different — a customer may see your display or video ads, receive an email, and then click through to your site from a search ad or organic search listing. Often, viewing display ads can attract your clients’ interest in your product and brand even if no click occurs. Traditionally, measurement technology separated out impressions or “view throughs” from clicks, but this separation missed out on valuable data on the impact of display advertising.
Thanks to our integration with the Google Display Network (GDN), Google Analytics can now break down the separation between clicks and impressions and give a more complete view of the customer journey. When a user views display ads on the GDN, or video ads on YouTube, and later visits your website and converts, these interactions with your brand can now be captured in Google Analytics Multi-Channel Funnels reporting.
Quantcast in the article mentioned above lists the following advice for getting the most out of display media:
How to Optimize for Conversions
If, like most advertisers, your objective is conversions rather than clicks, you can optimize your display campaign to capture them. Here are four steps to get you started:
Posted in Attribution in Online Advertising, Attribution Model, Attribution Modeling, Conversion Rate Optimization, Last Click Attribution, Marketing Attribution Tagged with: atttribution model google analytics, display attribution, gdn impression reporting, multi channel funnels, quantcast, video for performance, view through conversions
The last-click attribution model gives 100 percent of the credit for the sale to the last channel a customer clicked on. That model of reality would be fine if there was only one channel and one touchpoint, but with more than 50 percent of customer interactions involving multiple channels, the last-click attribution model does not do justice to key channels that drive conversions. Besides that, does it actually matter to you if your customers see a search or display ad first or last before they buy? Because you care about revenue, less about the order in which ads are served. The Internet is an open and chaotic place, so even if you could know the ideal order in which ads are served, you can do very little to force-serve ads in that way. So, let’s spend your time in a more actionable way; another way to approach attribution is to voice your hypothesis of what a new device or medium would do to your revenue.
So, what do hypothesis-driven marketers do? They run what scientists call a controlled experiment. You add a medium or device to the mix and do your best to measure lift in revenue from adding that over your baseline marketing mix. You create an incremental revenue model, which looks at customer conversion based on the treatment they received. You can randomly select people to be put into two different groups. A percent of people will be put into an A group, and a different percent of people will be put into a B group. So this might be 90% of your users, and then a randomly selected 10% will be put into a B group, which is what we’ll call the control group. Sounds a lot like the biology lab experiments back in school, right?
Incrementality testing is done by separating a small subsection of customers who don’t receive a certain marketing message and then measuring whether there is a difference in the customers who received the messages (the exposed group) and those who didn’t (the control, or holdback, group). You compare the two conversion rates between the groups that was retargeted and the group that wasn’t and see the actual lift. Imagine that your control group had a 6% conversion rate, and your exposed group 8%, which is a 33% conversion lift.The way that you set up and optimize a campaign to deliver optimal incrementality is different than if you were optimizing a campaign just for the lowest CPC or the lowest CPA. No matter what the CPC is, CPA, or anything else, this is the net impact of your medium or device new to your mix.
Here, AdRoll explains the method:
You you have experience with incremental attribution and running controlled experiments? We look forward to your comments below!
Further reading: uplift modeling article on Wikipedia
Posted in Attribution in Online Advertising, Attribution Model, Attribution Modeling, Controlled Experiments, Conversion Tracking, Last Click Attribution, Marketing Attribution Tagged with: click order attribution, controlled experiment, conversion lift, incremental attribution, incrementality testing