Last Updated: February 25, 2022

What are missing touchpoints?

In the context of attribution modelling in Google Analytics and Google Ads, a touchpoint is an exposure to a marketing channel.

The missing touchpoints are those user interactions that were not identified and recorded by your tracking tool or ad platform.

The missing touchpoints usually happen because of broken tracking or because of some other technical constraints.

The more heavily you are involved in multi-channel marketing, the higher the probability that your users’ conversion paths are going to contain missing touchpoints.

Note: A touchpoint is known as an interaction in GA multi-channel funnel reports and Google Ads attribution reports.

Types of touchpoints

There are two types of touchpoints:

1. Online touchpoint – exposure to an online marketing channel like paid search, organic search, social media, email, display, etc.
2. Offline touchpoint – exposure to an offline marketing channel like TV, radio, outdoor advertising, point of purchase display, etc.

Drawbacks of attribution modelling in Google Analytics and Google Ads

You need to be aware of the following drawbacks and technical constraints involved in attribution modelling in Google Analytics and Google Ads before you interpret the attribution/multi-channel funnel data and take business and marketing decisions based on such data:

1. Google Analytics and Google Ads attribution modelling is based on only ‘known’ touchpoints
2. Both Google Analytics and Google Ads record and report attribution for only online touchpoints.
3. Both Google Analytics and Google Ads do not record and report attribution for ‘all’ digital marketing channels.
4. Both Google Analytics and Google Ads do not record and report attribution across devices and browsers
5. Google Ads conversion paths in Google Analytics do not include impression interactions
6. Filtered views omit certain touchpoints on conversion paths
7. Data sampling issues can skew the conversion path data.
8. Not all touchpoints are equally valuable.

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#1 Google Analytics and Google Ads attribution modelling is based on only ‘known’ touchpoints

Google identifies a person through his/her web browser and device in GA multi-channel funnel reports and Google Ads attribution reports. 

For example:

If a person visited your website via the Chrome browser on a desktop PC and then later converted via the Safari browser on an iPad, then Google Analytics will report that they are actually two people who visited your website.

• The first person visited your website via the Chrome browser on a desktop PC but didn’t make a purchase.
• The second person visited your website via the ‘safari’ browser on an iPad and made a purchase.

Clearly, this is not true, but that’s how you will be reported of the customers’ behaviour on your website. So your attribution modelling will be based only on known touchpoints.

Another example: 

If a person saw an ad on TV and then later converted via a paid search ad on a desktop PC, then Google Analytics will report that a person clicked on a paid search ad and made a purchase.

GA will completely ignore the role of the TV ad prior to conversion. Again, your attribution modelling will be based only on known touchpoints.

So we can conclude that in certain situations, we can get a distorted picture of conversion paths from our multi-channel funnel reports.

So if you are heavily involved in multi-channel and multi-device marketing, both online and offline, your conversion path reports could be way off the mark.

#2 Both Google Analytics and Google Ads record and report attribution for only online touchpoints.

In other words, both Google Ads and Google Analytics do not record and report attribution for non-digital marketing channels. They show attribution only across ‘digital’ marketing channels.

Consequently, offline users’ interactions, like via phone calls and in-store visits, are not taken into account while determining which marketing channel should get credit for sales and conversions.

For example:

If a person saw an ad on TV and then later converted via a paid search ad on a desktop PC, then both Google Analytics and Google Ads will give all the credit for conversions to the paid search ad.

They completely fail to report the role of the TV ads in assisting conversions. So you may conclude that your TV ads are not working, but in reality, they are.

You can get a distorted picture of conversion paths from your GA multi-channel funnel reports and Google Ads attribution reports.

In the context of attribution modelling, a conversion path is the sequence of interactions (sessions, ad clicks, ad impressions) with digital marketing channels during the 1 to 90 days period, which leads to conversions.

Needless to say, if you are heavily involved in online and offline marketing, you can not 100% trust your attribution reports.

Note: Google Ads can report on offline conversions (phone conversions, store visit conversions), but they are not part of its attribution reports.

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#3 Both Google Analytics and Google Ads do not record and report attribution for ‘all’ digital marketing channels.

For example:

Google Ads records only those users’ interactions in a conversion path which resulted from Google Search Network ads and/or Google Shopping ads.

It does not record the users’ interactions in a conversion path that resulted from Google Display network ads or mobile apps.

So if a customer viewed one of your Google Display Network ad, couple of times and then later returned to your website by clicking one of your Google Search network ads, then both Google Analytics and Google Ads give all the credit for conversions to the search network ad in their attribution/multi-channel funnel reports.

They completely fail to report the role that the display network ad played in assisting conversions. So you may conclude that your display ads are not working, but in reality, they are. You can get a distorted picture of conversion paths from your multi-channel funnel reports and Google Ads attribution reports.

#4 Both Google Analytics and Google Ads do not record and report attribution across devices and browsers

In other words, both Google Ads and Google Analytics record and report attribution for only one device and one browser (by default).

So if a customer clicked on your Google Ads ad via the Chrome browser on a desktop PC and then later returned to your website and made a purchase through organic search made via Safari browser on his iPhone, then Google Analytics will give all the credit for conversions to the organic search made via a mobile device. Whereas Google Ads will not record and report this conversion as it has not resulted from a click on a Google Ads ad.

Both Google Ads and GA completely fail to report the role the desktop ad played in assisting conversions. So you may conclude that your desktop Google Ads are not working, but in reality, they are.

You can get a distorted picture of conversion paths from your multi-channel funnel reports and Google Ads attribution reports.

Needless to say, if you are heavily involved in multi-device marketing (marketing across desktop, tablets, and mobile devices), you can not 100% trust your attribution reports either in Google Ads or in Google analytics.

Note: Google Ads can report on cross-device and cross-browser conversions, but they are not part of its attribution reports.

#5 Google Ads conversion paths in Google Analytics do not include impression interactions

In Google Analytics, multi-channel funnel reports, all the Google Ads conversion paths (keyword path, ad group path, and campaign path) are made up of the only ad clicks interactions.

Whereas in Google Ads, the Google Ads conversion paths are made up of both ad clicks and ad impressions.

Thus Google Ads conversion paths reported in Google Analytics multi-channel funnel reports are less reliable than the Google Ads conversion paths reported in Google Ads report in terms of understanding the performance of your Google Ads campaigns. 

Note: Through Google display network impression reporting, you can get display ad impression interactions in your conversion path report. But this is a GA premium feature, which is not available to all.

#6 Filtered views omit certain touchpoints on conversion paths

The Google Analytics view filters can omit certain touchpoints on users’ conversion paths.

For example, if your filtered view excludes social media traffic then you may not see social interactions in the users’ conversion paths.

So look at Google Analytics multi-channel funnel reports via an unfiltered view.

#7 Data sampling issues can skew the conversion path data

Google Analytics has an upward limit on the amount of traffic data it will not sample to produce reports. This limit has been set to save resources like computation power and computation cost.

Following is an example of an unsampled report in Google Analytics:

Depending upon the nature of a user’s query, Google Analytics may choose to analyse the complete traffic data set or only a subset of traffic data.

As long as a data sample is a good representative of all of the traffic data, analyzing a subset of data will produce the same results as analyzing all of the data.

But when the selected sample is not a good representative of all of the traffic data then analyzing a subset of data will not produce the same results as analyzing all of the data.

In that case, Google Analytics samples the data badly and you cannot rely on the metrics reported by it. Following is an example of a sampled report in Google Analytics:

The smaller the sample size, the more inaccurate the reported data becomes.

In the case of multi-channel funnel reports, sampling occurs at the view level whether you use GA standard or GA 360.

Google Analytics can start sampling the data when you view a multi-channel funnel report which has got more than one million conversions.

In order to fix missing touchpoints on your conversion paths, you should always aim to minimize or eliminate data sampling issues.

#8 Not all touchpoints are equally valuable.

Some touchpoints are more valuable than others regardless of their position on a conversion path in terms of influencing the conversion. But rule-based attribution models do not take this factor into account while distributing conversion credit.

I have explained this concept in greater detail in this article: Multi-Touch Attribution in Google Analytics

Data integration is the key to minimizing missing touchpoints and fixing attribution issues

In order to minimize the number of missing touchpoints in your conversion path and to get a holistic view of your marketing, you need to integrate as much data as possible from different data sources.

These data sources can be (but are not limited to):

1. Google Analytics
2. Google Ads
3. Google Webmaster tools
4. Google Merchant Center
5. Bing ads
6. Kissmetrics
7. Qualaroo
8. Facebook Insight and other social analytics data
9. Compete
10. Survey Monkey
11. Phone calls data
12. CRM data
13. Point of Sale (POS) data
14. Data from customer support
15. Financial data and data from other departments.

Once you have integrated all the marketing and business data in one place, you can quickly track various aspects of your marketing campaigns, analyze the overall performance, and above all, take timely decisions.

Data integration can help you correlate all of your data with business bottom line impacting metrics like revenue, cost, gross profit, etc.

Without proper data integration, you will always get a SILO view of your marketing campaigns.

You need to create a robust data integration system in order to carry out any meaningful analysis. In fact, if you are a big organization, then it is completely pointless to collect and analyze big data without proper integration. 

You have to invest in data integration technologies if you are really serious about carrying out attribution modelling. 

Start by upgrading your Google Analytics account to Universal Analytics (if you still have not already) and then gradually move to custom-built applications.

Universal Analytics (UA) provides many more ways to collect and integrate different type of data than Google Analytics (GA). Through UA you can integrate data across multiple devices and platforms. This is something which is not possible with GA.

Consequently UA provides better understanding of relationship between online and offline marketing channels that drive sales and conversions than GA.

Source: Difference between Google Analytics and Universal Analytics

Eventually, you have to use customized applications because no single tool/software alone can minimize all of your data integration issues.

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