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Last Updated: February 9, 2022

Why you may no longer need Google Tag Manager?

Google Tag Manager (GTM) is a free tag management tool that is used to deploy various marketing and analytics tags on a website and/or mobile app.

Instead of hardcoding several tags on a website, one tag (called the GTM container code) is placed on each and every web page and this tag is used to deploy and manage other tags on the website.

This makes tag management efficient. This is the advantage of using GTM. 

Up to now, it all sounds good. However, the happiness that you get by managing all the tags from one central location is short-lived and the trouble is around the corner.

As your need for integrating website data with various data sources increases and become more complex, you quickly realize, how hard it can be to create and maintain each integration.

In GTM we create integration with each data source via ETL (extract, transform, load).

What is ETL (Extract, Transform, Load)?

ETL is a process that is used to send data from one data source to another (usually from one database to another).

When you use a tag management solution like ‘GTM’ to route data from one data source to another (like from your website to Google Analytics) you knowingly or unknowingly carry out ETL:

The data source from which you extract the data is called the originating data source.

The data source to which you send the data is called the destination data source.

For example, if you are sending marketing data from your website to Google Analytics via GTM then your website is the originating data source and your Google Analytics account is the destination data source.

Your GTM account is your TMS which acts as a hub between the originating data source and the destination data source.

Note: You can also send data:

1. From one originating data source to several destination data sources, or 
2. From several originating data sources to one destination data source, or 
3. From several originating data sources to several destination data sources.

What are the ETL functions?

ETL is made up of the following three functions called ‘ETL functions’:

1. The extract function – used to extract data from the originating data source.
2. The transform function – used to transform the data into the format your destination data source can understand.
3. The load function – used to send the transformed data to the destination data source.

Let’s suppose you have a tracking requirement to send ecommerce data from your website to Google Analytics via GTM.

To accomplish this task, you typically first extract the ecommerce data from the data layers (that are hardcoded on your website) via GTM.

Then you transform the ecommerce data into the format which Google Analytics can understand. This is done via GTM tag templates.

Finally, you send the transformed ecommerce data to the Google Analytics server.

The process of extracting the ecommerce data from the data layers represents the extract function.

The process of transforming the ecommerce data into the format which Google Analytics can understand represents the transform function.

And finally,

The process of sending the transformed ecommerce data to the Google Analytics server represents the load function.

When you use a TMS to send data from one data source to another, you knowingly or unknowingly carry out one or more ETL functions.

The ‘Extract’ function

Extract function is used to extract data from a data source. 

Your website is an example of a data source. Your mobile app is another example of a data source.

Following are other examples of data sources:

• Local databases
• CRMs
• POS (Point of Sales Systems)
• Email platforms (like ‘GetResponse) etc.

There is really no limit to the number of data sources you can have. It all depends upon how your company collects and stores data.

Following are examples of extract functions in the case of GTM:

• Extracting data from a data layer hard coded on the website
• Traversing the HTML DOM to extract data from the website and then sending it to GTM

You need a considerably good knowledge of JavaScript and considerably good knowledge of the development environment of each of your data sources, in order to extract data via GTM.

For example,

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If you want to extract Facebook data (like ‘Facebook likes) from a website then you first need to develop at least a basic understanding of ‘Facebook JavaScript SDK for website’.

If you want to extract Facebook data from an IOS mobile app then you first need to develop at least a basic understanding of ‘Facebook SDK for IOS’.

If you want to extract Facebook data from an Android mobile app then you first need to develop at least a basic understanding of ‘Facebook SDK for Android’.

If you want to extract Youtube data (like video played) from a website then you first need to develop at least a basic understanding of YouTube Player API.

If you want to extract twitter data (like tweets) from a website then you first need to develop at least a basic understanding of ‘Twitter JavaScript SDK’.

For ‘N’ data sources there could be ‘N’ JavaScript libraries. How many JavaScript libraries you can learn and remember? 

On top of that, JavaScript libraries update all of the time. 

How many libraries you can keep a track of (for new updates) and for how long?

If the JavaScript library you used for tracking data, get an update and you are not aware of it then your tracking can stop working immediately and the worse part is you won’t know about it in a timely manner.

Think of all of the countless websites which relied on ga.js JavaScript library to build their tracking setups and which were later forced to update their installations because Google switched to analytics.js library.

The more data sources you have got, the more ‘extract’ functions you would need to create and maintain.

Not only you would need to manually create the ‘extract’ functions but you would also need to make sure that the system (tags, triggers, variables, data layers, and JavaScript library) you used to extract data, remain intact all of the time. 

This makes the whole process of extracting data from several data sources very complex and challenging.

Even for a seasoned developer extracting data from multiple data sources is nothing short of a nightmare, let alone a marketer with mediocre JS skills.

The ‘Transform’ function

The transform function is used to transform the data into the format, your destination data source can understand. 

For example, if you are using GTM to route/send data to ‘Google Analytics’ then GA is the example of ‘destination data source’.

Similarly, if you use GTM to route/send data to ‘Mixpanel’ then ‘Mixpanel’ is the example of ‘destination data source’.

Just like ‘originating data source’, there is really no limit to the number of destination data sources you can have.

Converting event data into the format which Google Analytics can understand is an example of a transform function, in the case of GTM.

For example, let us suppose you are tracking clicks on ‘add to cart’ button on your website. You sent this event data to GTM via a tag.

But before you can route this event data to Google Analytics, you would first need to convert the event data into ‘Event Category’ – ‘Event Action’ format. Because this is the format in which GA understands the event data.

Now if you want to send the same event data to ‘Mixpanel’, you would first need to convert the event data into the format, ‘Mixpanel’ can understand. 

Similarly, if you want to send the same event data to ‘Kissmetrics’, you would first need to convert the event data into the format, Kissmetrics’ can understand.

This process of transforming the data into the format, your destination data source can understand is the ‘transform’ function.

The more destination data sources you have got, the more transform functions you would need to carry out.

Not only you would need to manually create the transform functions but you would also need to make sure that the system (tags, triggers, variables, data layers, JavaScript library) you used to transform data, remains intact all of the time.

This makes the whole process of transforming data for several destination data sources very complex and challenging.

Even for a seasoned developer transforming data for several destination data sources is nothing short of a nightmare, let alone a marketer with mediocre JS skills.

The ‘Load’ function

The load function is used to send the transformed data to the destination data source.

Sending a pageview hit via GTM to Google Analytics is an example of a load function.

Depending on the quantity, type of destination data sources, and the type of data you are sending, creating a load function can also become very complex, very fast.

Let us suppose you want to send event data to Amazon Redshift (your data warehouse).

Now how you will do that via GTM?

Let us suppose you also want to send the same event data to 20 different analytics tools via GTM.

Now how you will do that via GTM?

The more destination data sources you have got, the more load functions you would need to create.

Not only you would need to manually create load functions but you would also need to make sure that the system (tags, triggers, variables, data layers, JavaScript library) you used to route data, remains intact all of the time.

This makes the whole process of sending data to several destination data sources very complex and challenging.

And above all, you would need to carry out ETL for each of your tracking requirements from a particular data source:

As your tracking requirements increase over time, so will be the need to create and maintain several ETL functions.

Now let us suppose, you have got more than one originating data source. You have also got one mobile app. And just like your website, you have several tracking requirements for your mobile app.

Consequently, you would need to create and maintain several ETL functions for your mobile app as well:

Now let us suppose, you have got several data sources. You have got your website, your mobile app, your CRM, your point of sales system, etc.

Let us suppose, you have got ‘N’ tracking requirements for each data source. So you would need to create ‘N’ ETL functions for each data source.

Let us also suppose, you have got ‘N’ destination data sources. So instead of just sending data to one destination, you now have to send data to ‘N’ destinations.

This is the point where your GTM configurations will no longer remain stable and manageable, and would likely collapse under their own weight:

Can you see yourself creating and maintaining several hundred ETL functions?…. NO

Can GTM automate ETL functions for you?….. NO

As long as you use GTM or in fact any tag management solution, you would need to manually carry out ETL functions for each tracking requirement, for each data source, and for each destination.

And on top of that, you would need to make sure that the systems (tags, triggers, variables, data layers, JavaScript libraries) you use to create ETL functions remain intact all of the time.

This is a lot of engineering work and it just defeats the whole purpose of using a tag management tool.

So what is the solution? 

The solution is if you are in a situation where you need to route data between several data sources and several destinations for hundreds of different reasons (tracking requirements) then say goodbye to GTM.

I know it can be hard to say goodbye to a tool that you learned for years. But that is the technology for you. No tool or technology lasts forever. A better alternative is almost always just around the corner.

GTM, or in fact any tag management solution, is not designed for carrying out hundreds of complex ETL functions which is one of the primary requirements for carrying out ‘Business Intelligence’ (BI) and/or to work with ‘Big Data’.

Your time would be better spent in automating certain or all parts of ETL functions and focusing on more important things like ‘data analysis’.

This is something you can do via CDPs like ‘Segment’, keen.io and mParticle. That’s why I recommend using a CDP.

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What is a Customer Data Platform (CDP) and why do you need one?

A customer data platform (or CDP) is a tool designed to route (send) data between data sources.

Unlike a TMS, a CDP can be more easily used to route data between multiple data sources.

Both CDPs and TMSs are primarily designed to be used by developers. However, unlike a TMS, a CDP is much more non-developer friendly as it can automate many ETL functions.

Yes, you heard it right.

A CDP can automate ETL functions for you. Something a TMS can never do. This is the biggest advantage of using a CDP.

Many CDPs promote themselves as an alternative to a TMS. However, it is worth noting that they are not replacements for a TMS (yet).

For example, GTM has one very strong advantage over CDPs like Segment; and that is its ability to control tags. Through GTM, you can easily control:

• When a tag should fire
• When a tag should not fire
• Where a tag should fire
• Where a tag should not fire
• How often a tag should fire
• In which order different tags should fire
• What the tag should do when it fires

Segment lacks that ability. So in many cases, you have to rely on a TMS, like GTM, even when using a CDP.

However, here is a thing. I don’t see any reason why a CDP cannot also provide the features of a traditional TMS in the near future.

If you keep this possibility in mind, there will come a time when a traditional TMS may no longer be required. For this reason, I think the future of TMSs (like GTM) is bleak.

However, for now, you have to use a TMS alongside your CDP. And yes, you can install a TMS via a CDP and get the best of both worlds.

It is worth noting that a CDP is generally not free to use.

Yes, many CDPs do provide a free version, but these are very limited in functionality. If you want to use a CDP for commercial purposes, then you are better off using a paid version.

You would be charged based on your monthly API usage and the integrations you have enabled.

Meet the Google Tag Manager Alternative ‘Segment.com’

There are many CDPs available on the market. The one that I use and recommend is ‘Segment’.

Through tools like ‘Segment’ you can automate certain or all parts of ETL functions. 

For example, let us suppose you want to send clicks on ‘add to cart’ button (embedded on your website) to Google Analytics, Mixpanel, Adobe Analytics, and your data warehouse (like Google BigQuery).

With ‘segment’, you track the clicks on ‘add to cart’ button only once using their tracking code.

Then segment can automatically route the same event data to all of your destinations: Google Analytics, Mixpanel, Adobe Analytics, and your data warehouse.

You won’t need to create and maintain ‘transform’ and ‘load’ functions for each destination data source. This would be done automatically by the Segment API.

If there is any change in the JavaScript library used by a data source and ‘Segment’ supports the integration with that data source, then ‘segment’ will automatically start using the updated JavaScript library without breaking your tracking.

You won’t need to make any changes to your tracking code.

Remember you are sending your analytics data to ‘segment’ and ‘segment’ is routing that data to your chosen data sources.

Once you have sent your data to ‘segment’ then it is the job of ‘segment’ to route your data to various analytics tools.

‘Segment’ is not a tag management solution (TMS). It is a good alternative to GTM but not a replacement.

It acts as a hub between ‘originating data sources’ and ‘destination data sources’. Its job is to automate ETL functions and make data integration easier and less time-consuming. 

Unlike GTM, Segment does not rely on ‘data layers’. It uses an API that can replay’ your historical data into any new tool and can route your data to hundreds of tools with the flick of a switch.

When you use a CDP like ‘Segment’, you can easily and efficiently fix data collection and integration issues. All of this helps in getting the correct conversion attribution data.

The downside of using a CDP like Segment

One big downside of switching to a CDP like ‘Segment’ is that you would have to retag your website all over again. But that should be your last tagging. 

Another downside of using a CDP like ‘segment’ is that it is not free to use. They do provide the free version to developers but that is limited to 50k API calls per month. 

You would be charged based on your monthly API usage and the integrations you have enabled. 

So if you run a high traffic website, tracking and data integration can get very expensive, very fast. But that is the cost for automating ETL, for now.

Another article you will find useful: How to install and use Google Tag Manager in Segment.com

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