Event-driven data layers with Adobe Launch

Written by Matt Bentley, Head of Data Architecture & Digital Analytics 

What’s the story 

The aim of this article is to give you insight and a few different options for integrating event-driven data layers (EDDLs) – such as Google Tag Manager’s dataLayer object – with Adobe Launch; something that isn’t immediately obvious out of the box. Loop Horizon have used event-driven data layers (EDDL) with a number of clients. In some cases, clients who are using Google Tag Manager, which is underpinned by the event-driven dataLayer object. In other cases, clients migrating away from GTM, usually to Adobe Launch, whose sites are heavily integrated with GTM’s dataLayer. And in other cases, clients who are migrating to an EDDL from a customer experience digital data layer (CEDDL) (the old W3C-type data layer). 

If you’re reading this, we can assume a certain level of knowledge, but this article from Jim Gordon gives a nice overview of EDDL vs CEDDL. TLDR version though: an EDDL pushes data / objects into an array / queue; you can subscribe to these push event in order to trigger rules in your tag manager. 

A CEDDL does not emit events (as it is an object rather than an array), instead relying on browser-level events (e.g. DOM ready) to prompt the tag manager to collect data. Data is passed into the data layer object, which is continually overwritten as new data becomes available. 

Adobe Launch and the EDDL 

Google Tag Manager natively integrates with an EDDL – Google’s dataLayer array. This dataLayer array underpins the entire tag manager. You can natively hook onto events passed to it, and any data sent with those events. 

Adobe Launch does not natively integrate with an EDDL. This isn’t a criticism of Launch – it’s extensible and can be made to fit your needs easily. But this lack of a clear way of doing things, means it’s easy to do things the wrong way (or at least in a sub-optimal way). That being said, it also means it’s easier to customise Launch to the way you want to do things. 

The easy(ish) option 

Launch has extensions – which expand the functionality of Launch – designed to hook onto an EDDL. 

Adobe Client Data Layer 

This extension integrates with Adobe Experience Manager’s Core Components, so if you’re using AEM, it’s a good option. 

However, you can also link it to any EDDL you’re running on your site, and it will give you a suite of event triggers, data elements and rule actions. It will also compute your EDDL array down to a single object – to all intents and purposes, creating a CEDDL – giving you the best of both worlds; the ability to tightly control event triggering and avoid race conditions from the EDDL, and the ability to combine data from multiple events via the CEDDL. 

Unfortunately, there are three key downsides that I found with this extension. 

The first is that it seems to “take over” your data layer. It automatically creates an object called adobeDataLayer, and if the location of the data layer you want this extension to work with differs from that, it copies the adobeDataLayer object into your data layer. 

This might not be an issue in most instances. But if you’re migrating from GTM (for example), and want to maintain your usage of Google’s dataLayer object (or if you just want to use dataLayer as your data layer location, because so many tools natively integrate with it), you can’t really use the Adobe Client Data Layer extension. When we tested, using the Adobe Client Data Layer with Google’s dataLayer array caused all the Google suite of tools to stop working; Google Analytics, Google Ads and so on. 

The second issue is that it doesn’t seem to be great at handling race conditions. The computed data layer it generates can be referenced via data elements but takes time to process. We found that, if your data layer has multiple events pushed into it in very quick succession – potentially in an undefined order (that is to say, events may be pushed in A>B order or B>A order depending purely on how quickly certain resources load) – there’s every chance that data will be missed or referenced incorrectly. 

And on the flip side; while you can reference data directly from the event object, see above if you need to combine data from multiple events together. 

The third issue is that you cannot access the event name from the data layer; the one that triggered your rule in Launch. It’s a minor point, but if you want to use multiple data layer events to trigger a single rule (for example, when your normal page view happens and when a user first consents to allow cookies), or you simply want to collect this information dynamically without having to hardcode it in each rule, there is no way to send information about which event triggered your rule. 

(My old colleague Andrew would disagree, but his innovative solution is nonetheless subject to race conditions, as it relies on Adobe Analytics to reference back to the data layer array – if another event has triggered between the event name you’re interested in and Analytics processing that event, you lose the data.)

EDIT: after posting this article, my old colleague Michael Schubert contacted me to let me know that using “event.message.event” in custom code / the launch interface allows you to access the event data from the Adobe Client Data Layer.

Google Data Layer 

Hot off the press (it’s still in beta at the time of writing) is this new extension from Adobe. 

It’s very similar to the Adobe Client Data Layer extension, but it looks to solve the downsides we found: 

  1. It leverages Google’s own helper function, so it doesn’t break Google tools using the dataLayer 
  1. Though we have not tested extensively, from the documentation it looks as though this extensions handles race conditions better (that being said, some recent testing we did indicated that this extension missed pushes to the data layer that happened in quick succession) 
  1. You can access the event data – we’ve tested and printed the event data to console to prove this out 

All in all, this new extension looks an impressive new addition to Adobe Launch and a great option, especially if you already utilise Google’s dataLayer object. 

Data Layer Manager 

This extension from Search Discovery happily integrates with any EDDL you may happen to use – including Google’s dataLayer – without overwriting it or making other tools that integrate with your data layer unusable. 

It’s not as prone to issues with race conditions as some other extensions. It has what the extension calls “context aware” data elements, which know whether to get the data you want directly from the object that was pushed or from the computed data layer object. 

However, unlike the new Google Data Layer extension, it doesn’t make the event level data available. It also doesn’t allow data layer validation without upgrading to the paid version of the extension. Lastly, it doesn’t meet some of the more nuanced use cases I’ll cover below. 

The hard(er) option – build your own 

Given the options above, why would you want to build your own EDDL processing tool? For most use cases you wouldn’t. But eventually (and “eventually” may come faster than you’d think) you could come up against an issue that the above extensions cannot solve. 

And at that point, if you’ve integrated our entire Launch implementation with one of them, you’re going to have an issue; the extensions are not extensible in turn. 

Here’re the reasons why I’ve built my own. 

It’s not a back box 

By building our own EDDL processing tool, we have direct access to the code – it is extensible. None of the extensions give that level of access and understanding. Everything listed below stems from the fact that we have full access to modify the tool we built; the ability to make it fit the requirements, and to meet those requirements from a central location rather than having to employ clunky work-arounds and take on technical debt. 

Validation 

We built a JSON schema document for one of the data layers we created. We wanted the ability to validate objects passed on to the data layer, and return any errors to the developer console. 

This was to make the deployment process easier; to give developers the tools (errors in the console and the schema document) to fix issues themselves, removing a potential bottleneck. 

We / our clients didn’t want to spend thousands of pounds a year enabling validation. 

The answer – build your own EDDL processing tool. 

Access to event data 

As mentioned earlier, we wanted to have multiple events trigger a single rule – in this case, the view event from the data layer and changes to consent, so that we wouldn’t miss anything when the user initially consented to allow analytics to collect data. 

We also wanted to know which of those two events had triggered the data to send. 

Prior to the recent addition of the new Google Data Layer extension, the extensions available did not gave enough flexibility or access under the hood to deliver this. 

You guessed it – build your own EDDL processing tool. 

The ability to send events wherever you like 

We had multiple third-party vendors passing data to the data layer, and – to maintain ease of use – a strict implementation standard of: “do not add new rules to the Adobe Launch user interface (UI) unless you absolutely have to.” 

The Adobe Launch extensions enforce their own extension-specific “event” trigger to use in the UI. This does not give the flexibility to pick what event type you’d like (custom event, direct call rule etc) or sufficient access under the hood to understand how to access the events by other means; it therefore severely limits your ability to split events into type. 

For example, we wanted to process all objects sent to the data layer as events in one location (a <div> element on the page), but filter a subset of them (specific to user consent) in another location (a different <div> element), so we could include multiple data layer events a single rule without: 

  • Having to fire on every event sent by the consent platform (double firing the rule in most cases) 
  • Add consent-specific rule conditions that would break the firing of the standard data layer event 
  • Add a second rule with identical tags, breaking the implementation standards 

By building own EDDL processing tool, we could emit custom events to multiple div element on the page – one for the standard data layer events and another for the filtered subset of consent level event – thus allowing us to avoid double firing or having multiple rules to do the same job. That way, we were able to keep the Adobe Launch UI as simple and easy to use as possible. 

The ability to process data in multiple ways 

The Adobe Launch extensions provide a computed data layer object from your EDDL, to give the best of both worlds (EDDL and CEDDL). 

What if you want multiple computed objects though, each serving a different purpose? One main computed object, one flattened object, one to drive product string creation, one to re-format data to new Google Analytics 4 standards, one to package up your data layer and send it to a server-side endpoint… 

It would be achievable via the Adobe Launch extensions, but would be clunky, and require a range of rule and data elements to achieve. There would also be potential concerns about race-conditions and missing data. 

By building our own EDDL processing tool, we were able to output all the computed objects we needed from a single rule; a range of underpinning utilities to run the whole implementation. 

Only process the events you want 

Because we had full access to the code, we were able to create lists of events pushed on to the data layer and do different things with them. 

Only want to validate the events in your schema and ignore events from third-party vendors? No problem – just provide a list of events to validate and only pass those to the validation script. 

Want to process a subset of events to a different div element on the page to allow filtering without requiring rule conditions and setting up multiple rules? Easy. 

Want to process different events go different computed objects on the page? You got it. 

Handling data processing 

Some of the Adobe Launch extensions handle race conditions gracefully and re-process historic data on the data layer array very gracefully. But by having access to the underlying code of our EDDL processing tool, we could determine exactly how it did this. This made de-bugging much easier. 

For example: 

  • We were able to ensure that processing the computed data layer objects happened before the events were emitted, making it more likely the computed data layer object would be up to date 
  • We were able to log the last event processed by the event listener and stop it from processing that event again in error as we loaded the script into the page – this allowed us to run our event listener script before our re-processing script to better avoid missing data 

There are other examples, but the point is – when you know how the data layer is being processed under the hood, it makes it much easier to determine where issues are coming from when they occur. 

Wrapping up 

To recap, now you should hopefully have some more information about how to leverage an EDDL using Adobe Launch, and some thoughts on the approach you might want to take. 

At Loop Horizon, we’ve deployed new event-driven data layers for a wide range of clients, and worked with others already utilising one. 

For some, we’ve used the Adobe Launch extensions to integrate with their EDDL. For others, we’ve built our own EDDL event listener. The key consideration has always been: what approach would be the most appropriate for the client – do they have experienced tag management resource; do they have complex use cases; and so on? 

The same considerations should inform your own decision about how best to proceed. Please get in touch if you would like to talk to us about: 

  • Data layer development and event-driven data layers 
  • Adobe Launch and key extensions / utilities for handling EDDLs 
  • Google Tag Manager 
  • Data layer validation 
  • Consent / cookie management platforms 
  • Analytics – Adobe, Google (including migration to GA4) 

Terms used in the article

EDDL – Event-driven data layer 

CEDDL – Customer experience digital data layer 

TLDR – Too long didn’t read

AUTHOR

Matt Bentley, Head of Data Architecture & Digital Analytics 

Loop’s data and digital analytics expert, with over ten years’ experience working in marketing and technology teams across a number of businesses 

Before joining Loop Horizon, Matt’s experience was client-side; in financial services and the media and entertainment industry 

Having managed centralised analytics and data collection functions, he’s worked across numerous verticals covering both business and technology teams, giving him the tools to tailor communication effectively on both a strategic and technical level 

Matt specialises in data collection strategy, tag management, data layers, analytics tool configuration, reporting and analysis, bringing business stakeholders and development teams closer together in order to better connect data to business performance 

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Business

GA4 – The Pros & Cons

We sat down with Matt Bentley and Rob McLaughlin to discuss their views on Google Analytics 4 (GA4) adoption and what it could mean for businesses going forward. Both Matt & Rob had some very strong points to make on why GA4 should be understood as an opportunity rather than a challenge, in fact they see it as something of a coming of age for digital analytics in general.

As discussed previously, GA4 offers an amazing opportunity for organisations to step up their analytics maturity and this is an exciting time to consider the pros and cons of adoption. Knowledge is power in everything we do so, we have a list of the main talking points regarding the switch to GA4 and why we are so excited about this new and improved technology.

Contact us to arrange a live demo on one of our GA4 accounts.

SPOILER ALERT: Overall, the PROS definitively outweigh the CONS, GA4 is a huge step forward

Rob McLaughlin

PRO – FRESH START & REDEFINING THE IMPORTANCE OF YOUR DATA  

GA4 provides a new interface, designed from the ground up. By taking things back to the bare bones you have the choice to either utilise this new interface and adapt the new layout OR use an alternative interface that appeals directly to yourself and that you find helpful to use. You can extract the data and send it directly into an interface such as DataStudio or PowerBI that is more compatible with your business. You can leverage Google Analytics 4 as a way to tidy up your data collection and optimise your setup. The ability to send the data through Big Query can be accessed via an interface.  

GA4 provides a new interface, designed from the ground up

Matt Bentley

CON- TIME & CHANGE  

Some people do not like the new interface and the way it has changed. Learning the way GA4 works and being able to navigate it with confidence for the information they are requiring is going to be key. The loss of historical data is another complaint as data from previous years will no longer be available, the ability to compare year on year data will not be available until you have a GA4 account for over a year.  

However, with time being considered it is important to remember that Google Analytics 4 was created with long term use in mind and the time spent learning it now will be worth it in the long run. 

The loss of historical data is another complaint as data from previous years will no longer be available

Matt Bentley

PRO – GA4 IS MORE VERSITILE & VALUED 

Google Analytics 4 takes digital analytics from a vanity metric generator to a genuine operational tool for business. GA4 has a working partnership with Big Query so they can directly transfer data through the pipeline into a chosen cloud system making it accessible to all, as well as having all the newly collected data now sitting side by side with all your other data. GA4 is growing the business potential of digital analytics – a move away from basic digital analytics and move towards customer analytics, using this data  is now a powerful way to know your customers deeper. 

Google Analytics 4 takes digital analytics from a vanity metric generator to a genuine operational tool for business

Rob McLaughlin

CON – IMPLEMENTATION & CHANGES  

GA4 has been referred to as difficult to implement. The data model changes have been causing people difficulties, getting used to the changes in the data collected. The new data model fits the modern web and its progression bringing GA4 forward to be consistent with all data models across channels, helping to improve all enterprise databases. GA4 applies to every platform and to every interaction that a business will have with its customer; registering all the collected data along the customer journey. GA4 gives its users the ability to access unsampled data free of charge which has not always accessible to everyone in the past whereas now everyone has the chance to access it. 

The new data model fits the modern web and its progression bringing GA4 forward to be consistent with all data models across channels

Matt Bentley

PRO – MODERN WAY OF THINKING 

GA4 is digital analytics reimagined for the 21st century. Google has recognised where the modern internet is headed and has adapted the way it works. The new way of working has privacy at the centre. With GA4 you are able to gain better insight into your customers with it’s the ability to better leverage machine learning. A big part of the new system is the ability to better use prediction abilities rather than relying on basic data analysis.  

GA4 is digital analytics reimagined for the 21st century

Rob McLaughlin

CON- SLIGHTLY ANNOYING 

Cardinality – a big issue with Google Analytics is the ability to pass custom data, customer ID for one. This causes issues for many businesses, but Big Query can eliminate this issue. So, it is not even that big of a ‘con,’ more of a slight annoyance than anything but it is easily fixable.  

Overall, the PROS definitively outweigh the CONS, GA4 is a huge step forward. Our recommendation to all  is to start the adoption process as soon as possible and start gaining that vital customer data sooner rather than later, guaranteeing that you are ahead of the game when Google stops collecting data on the previous platforms.  

Contributors

Matt Bentley, Head of Data Architecture & Digital Analytics 

Loop’s data and digital analytics expert, with over ten years’ experience working in marketing and technology teams across a number of businesses 

Before joining Loop Horizon, Matt’s experience was client-side; in financial services and the media and entertainment industry 

Having managed centralised analytics and data collection functions, he’s worked across numerous verticals covering both business and technology teams, giving him the tools to tailor communication effectively on both a strategic and technical level 

Matt specialises in data collection strategy, tag management, data layers, analytics tool configuration, reporting and analysis, bringing business stakeholders and development teams closer together in order to better connect data to business performance 

Rob McLaughlin, Co-Founder 

An experienced digital marketing professional and a proven leader in the field of data & analytics with a track record of bringing customer data into the complex marketing and technology landscape. 

A combination of visionary and pragmatist, Rob’s expertise in strategic planning, data-driven transformation and marketing technology vision creates solutions which blend established business processes with bleeding edge techniques. 

Having worked both as a consultant and in-house he has in depth knowledge of how businesses from multiple verticals can leverage data to power efficiency and growth. 

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With clients increasingly looking to drive meaningful change and commercial results from the use of data & technology Loop Horizon continues to scale its specialist teams. Ata Mehmet joins Loop Horizon’s Activation team, a key hire as a consultant specialising in CRM within this highly regarded team led by Rachel Fox, Head of Marketing Strategy & Adoption.

Ata has over 15 years of experience in data-driven CRM transformation with expertise in campaign strategy, advanced analytics, experimentation and CRM capability development having worked at British Gas, Currys, M&C Saatchi and most recently Sky. He also has proven track record of driving commercial value through data and analytics, as a ‘translator’ bridging the gap between technical and business teams.

Commenting upon joining Ata stated, “I am incredibly excited to be joining Loop Horizon at this pivotal time. It has been amazing to see, albeit from afar, the growth that Loop Horizon has enjoyed by helping brands realise the true value of data through their marketing. I look forward to now playing a part in this continued success, and to work with such a passionate group of data-driven practitioners.”

Co-founder Rob McLaughlin stated, “We are very excited to welcome Ata to Loop Horizon, he brings a valuable array of skills and experiences, our clients will be lucky to have him working across their accounts. Naturally several of us know Ata well from our time at Sky, he has a strong reputation for innovative, clear thinking. It’s a great time to be joining Loop Horizon and Ata is a great addition”.

To learn more about Loop Horizon, how we help brands and career opportunities please contact info@loophorizon.com

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GA4 – Upgrade, Migrate and Beyond

Written by Lindsey Williams, Senior Digital Analytics Specialist

Google Analytics is evolving, the next-generation measurement solution from Google has arrived. There are multiple benefits to implementing GA4, from an enhanced, scalable data model, access to raw data in Big Query, cross platform tracking, advanced analysis including predictive capability, to more intelligent tracking which is less reliant on cookies; if you’ve not started planning your GA4 implementation talk to the specialists at Loop Horizon today. 

Article's hero media

Why bother? 

Aside from significant benefits, if you’re already a GA customer, the single most important reason to implement GA4 is that Universal Analytics (UA) will be switched off on July 1st 2023. From this date Google will stop processing UA hits; unless you’ve implemented GA4 tags your digital properties will not collect any more analytics data. 

If you require year on year comparison data you must implement GA4 as soon as possible, GA4 tags can co-exist alongside UA tags therefore you don’t need to change your UA implementation, you simply need to add GA4 tags. 

What’s the difference

Data Model 

GA4 has a different data model to UA; the methodology behind data collection is different hence the dimensions and metrics captured are not the same. Your UA data will not sit in the same backend database as your GA4 data, consequently the GA interface and reports for GA4 are different to those for UA. We can help you understand your GA4 data and can support in navigating the new interface. 

Data Availability 

A big benefit of GA4 is that the event level data collected is automatically made available in the Google BigQuery database engine for no additional cost. If required data can also be streamed in to, and managed from, your own data repository, typically a cloud solution, making this truly first-party owned data. This data can be queried and visualised outside of the GA interface, and, crucially for audience development, it can easily be unified and curated alongside other enterprise datasets to generate sophisticated customer segments for downstream targeting. 

Server-Side Tagging 

An enhanced server side GA4 implementation is also now an option, this offers both improved performance (analytics tags run from a server rather than the customers web browser) and better security of visitor data (data is collected via a customer managed server-side environment). 

Additionally, this methodology offers consolidation of collected data, rather than multiple tags sending the same data multiple times, a single ‘stream’ of data can be sent once to a server endpoint and distributed to multiple downstream entities. This reduces the amount of JavaScript required on the page and minimizes the number of http requests thus improving website performance. 

Crucially server side tagging represents opportunity to set first party client IDs via http headers which are not subject to the restrictions and limited lifespan of third-party cookies.  

How can Loop Horizon help? 

From a straightforward implementation of ‘vanilla’ tags to a fully functional, future-proofed, server-side ecommerce tracking solution, and everything in between, Loop Horizon can help you plan and implement GA4. 

Additionally, we can provide strategic advice and hands on support in relation to the centralisation, unification, and curation of your analytics data alongside other enterprise datasets in your own cloud solution. Availability of analytics data at this level means your analysts and marketing teams will be able to segment and target your customers like never before. 

Get in touch to speak with Loop Horizon about how we can support your organisation.

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