[ UPDATE 8/16 ]
We adopted the term “click-through” to better differentiate between clicking events and make the event more self explanatory. Click-through is the event in which the ad triggers the desired action and typically sends the user to the app store.
[END UPDATE]
We already discussed some click patterns in previous posts. Specifically there was one about Heatmaps and another one showing click distribution over time.
This post is about the correlation between two events that happen in the process of ad clicking:
- Touch event – this is the physical contact of the users finger with the screen
- Click-through event – this is the event when the ad takes an action and opens a website, launches the app store or initiates the in-app app store dialog – this event is also the one reported to the attribution
You may think that these events are coupled together but in reality there are different ads with different patterns. The image below shows two extreme cases.
As you can imagine – this type of data is now available in AdIntel
3 Patterns of correlation
Drilling down a bit into the topic. We were able to identify 3 scenarios:
| Pattern | Details |
| Touch directly triggers click-through | This is rather simple and what you would expect. The user touches the screen and the ad launches the desired action while reporting the click-through to the attribution. Typically the time difference between the two events will be quite low |
| Save for later | In this scenario – the user touches the screen but the ad will not immediately trigger the action. Instead, the ad will wait and only trigger the click-through later (without another touch). In video ads this is done to allow the users to see the full length of the videos. In playable ads, some of the ads will allow the user to click as part of the interactive play and will then consider the user engagement with the ad as trigger to launch the ad action |
| Click-through with no touch event | Here, the user will not even touch the screen but the ad will still trigger the ad action unless the ad is closed before. This is more common with in-app dialog actions where the app is not actually redirecting the user to another app or website. |
What’s in it for the ad makers?
If you are new to the industry you might be puzzled by these various patterns that may seem misleading and overall bad user experience. There are 2 motivation in player here.
The first one is from the advertisers themselves. As odd as this may seem, some of the top performing ads have bad UX. At the end of the day, the publisher is paying for advertising with the expectation that many users will try his app. Ads that have a high ratio of installs to impressions are pushed to the front of the stage by both algorithms and human decisions.
The second motivation has to do with the way the industry measures and attributes the install of apps. When an app marketer uses multiple channels it’s possible and even likely that the same user is watching many ads to the same app. Moreover, these ads could be serving by different marketing providers. The question then becomes if a user saw an ad from 2 providers and then installed the app, who should the credit go to. The industry’s answer to this in the last 10 years and probably even more is called last click attribution. It’s a limited approach but it’s the best we have. One of the pitfalls of this system is that ads that generate more click-throughs are more likely to get credit for the install. This leads to a situation where industry dynamics incentivize network algorithms to prioritize ads with high click through rates.
The norm – Touch triggers a click-through
What we expect to see here is a combination of 3 elements:
- Reasonable CTR
- bell curve or half-bell curve distribution of clicks-throughs over time
- At least 80% of click-throughs triggered by touch events
Mid-way scenario – “Save for Later”
What we expect to see here is a combination of 3 elements:
- Low to Mid CTR
- Click-throughs are mostly concentrated to certain time (spike pattern)
- At least 40% of clicks show as “Save for later”
Extreme scenario – Click-throughs triggered with no touch
What we expect to see here is a combination of 3 elements:
- CTR over 90%
- Click-throughs are mostly concentrated to certain time (spike pattern)
- At least 70% of click-throughs show as “Click with no touch event”
If you want to see this blog post in webinar style you can click play below.
The best advertising insights
Get the edge you need against your competition. AdIntel is the most advanced platform in the market for advertising insights. Don’t believe it? You can try it for free for 7 days. Get ready to see things you have never seen before.
