What is web analytics & why do you need it? 

Web analytics is the process of measuring, collecting, analysing and reporting data to understand and optimise website or app usage. 

It provides insights into how visitors find and use a website, where they struggle to navigate, what products and content they are most interested in, how many convert and what they spend. 

Understanding this information helps organisations inform strategies to improve user experiences and journeys, tailor content to better meet user needs and increase conversions.  All of which should ultimately increase profits and success. 

The two main web analytics providers are Google and Adobe; each with offerings to suit different budgets and a wide range of needs. 

Google’s product is called Google Analytics 4 (“GA4”) which is free or has a paid tier with fewer restrictions. 

Adobe has many versions of its core Adobe Analytics (“AA”) offering, which have varying price points depending on features required and data volumes.  Adobe also has a new and much more flexible tool called Customer Journey Analytics (“CJA”) that will one day replace AA. 

120Feet’s goal is to help clients get the maximum value from their current web analytics tools and investment, or to migrate them from one tool to the other. 

This usually starts with a audit of the current implementation, leading to recommendations, fixes and continual improvement. 

Web analytics audit 

A web analytics audit is a critical step for any business that relies on data-driven strategies to ensure the accuracy, relevance and security of its data collection and analysis practices. 

It serves as a comprehensive review of the current state and implementation quality of the analytics tool.  Its primary purpose is to ensure that all tags – of which analytics is just one – are correctly configured, data is accurately collected and insights are effectively utilised to drive business decisions. 

By identifying discrepancies, tracking issues and highlighting inefficiencies in the collection or interpretation of data, an audit helps in laying down a solid foundation for reliable and insightful analytics.  

The process will include a discovery session to identify key players and their requirements, a review of the data layer and tag manager’s implementation to verify the accuracy of data collection, and a comprehensive review of the analytics admin console and reporting capability.  

The audit will answer the questions: 

  • What’s working as intended? 
  • What’s working but not as intended / is being misinterpreted? 
  • What’s broken and not fit for purpose? 
  • What’s missing and should be considered? 

The output of an audit is a comprehensive document and presentation walking through each finding together with a recommendation on how it should be resolved, an indication on the complexity of the likely solution and who needs to fix it; such as a client developer, 120Feet or someone else. 

Google Analytics 

The Google Analytics product naming has become rather confusing, so we’ll do our best to explain what’s meant by GA4, GA4 360, Analytics 360, GA360, GA UA, 360UA and GA3 360! 

Google Analytics has a free and paid-for tier. 

The free tier is referred to as Google Analytics 4, or GA4, and has been the main analytics tool of choice for sites worldwide since Google’s Universal Analytics (“GA UA”) stopped processing data on 1st July 2023. 

Google’s premium and paid tier is more confusing because two versions are live: 

  1. The soon to be obsolete Google Analytics 360, which may also be referred to as GA360, GA3 360 or GA UA 360.  To keep things simple, we’ll refer to it as GA3 360.
  2. GA4 360, which may also be referred to as GA360 or Analytics 360.  We’ll stick to using GA4 360. 

The premium 360 products are designed for enterprises in need of more advanced features than are available in [free] GA4; such as more custom dimensions, an SLA and higher data processing limits.  Also, 360 offers deeper integration with Google’s advertising and marketing products “Google Marketing Platform” allowing businesses to leverage their data for more effective decision-making and marketing optimisation. 

Key features of GA4: 

An event-based measurement model: unlike GA UA, GA4 primarily relies on session-based data, using an event-based model that offers more flexibility in tracking interactions across websites and apps, leading to a more detailed and comprehensive understanding of user behaviour. 

Cross-platform tracking: GA4 enables the seamless integration of data across websites and apps, providing a unified view of the customer journey.  This is particularly beneficial for businesses that operate both a website and a mobile app because it allows them to analyse user interactions across platforms in a single interface. 

Enhanced privacy controls: in response to increasing privacy regulations and a shift towards a more privacy-conscious web, GA4 offers improved data privacy controls.  It includes features like data deletion, consent mode and IP anonymisation by default, helping businesses comply with regulations like GDPR and CCPA. 

Predictive analytics: GA4 incorporates machine learning and AI to provide predictive insights about user behaviour, such as potential revenue from specific segments, churn probability and the likelihood of users completing certain actions.  These insights can inform marketing strategies and improve targeting. 

Improved engagement metrics: GA4 introduces new engagement metrics, including engagement time, engagement rate and engaged sessions, providing a more nuanced view of how users interact with content.  This helps businesses understand what captures user attention and drives engagement. 

Integration with Google’s Advertising Platforms: GA4 offers deeper integration with Google Ads, enabling advertisers to create custom audiences based on user behaviour and leverage predictive metrics to improve ad targeting and ROI. 

BigQuery integration for all accounts: unlike UA, where BigQuery integration was only available for GA3 360 users, GA4 offers this feature to all users, even those on the free tier.  This allows for more sophisticated data analysis and custom reporting capabilities. 

Given all of its features, at the time of writing (Feb ’24) GA4 is proving a challenging and problematic tool for many users who are struggling to adapt and extract value compared with GA UA.  If your business is struggling, get in touch and ask how we can help boost your knowledge, understanding and confidence in using it. 

Google Big Query and Google Looker Studio 

Both GA4 and GA4 360 can integrate with Google Big Query and Looker Studio, which is going to be essential for clients wanting to get the most of out their Google Analytics implementation and conversion opportunities. 

BigQuery and Looker Studio (formerly known as “Google Data Studio”) serve different purposes in data analysis and business intelligence, and it’s important to understand when to use each. 

When you should use BigQuery 

BigQuery is a fully managed, serverless data warehouse that enables scalable and cost-effective data storage and analysis.  You would use BigQuery when you need to: 

  • Handle large datasets: BigQuery is designed to process huge volumes of data, making it ideal for businesses that deal with data across various sources. 
  • Perform complex queries: use BigQuery for running SQL-like queries that require high computational power to process large datasets quickly and efficiently. 
  • Real-time analytics: BigQuery’s streaming capabilities allow for real-time data analysis, making it suitable for applications that need up-to-the-minute data.  Such as live dashboards or monitoring systems. 
  • Machine learning integration: BigQuery ML enables users to create and execute machine learning models directly within the data warehouse using SQL queries, ideal for predictive analytics and data science projects. 
  • Data integration and ETL: BigQuery supports data integration from various sources, including streaming data for real-time analytics and batch uploads for historical analysis.  It’s useful for ETL (extract, transform, load) processes where data needs to be prepared and made queryable. 

When you should use Looker Studio 

Looker Studio is a data visualisation and business intelligence tool that allows you to create customisable reports and dashboards.  Use Looker Studio when you need to: 

  • Visualise data: Looker Studio is perfect for creating interactive reports and dashboards that visualise data from various sources, including BigQuery, Sheets, and many third-party sources. 
  • Share insights: it’s designed to make sharing insights easy and accessible, allowing you to share reports and dashboards with team members, stakeholders, or clients, with controls over who can view or edit. 
  • Collaborate on data analysis: Looker Studio supports collaborative work on reports and dashboards, enabling teams to work together in real-time. 
  • Integrate data from multiple sources: if you need to visualise and analyse data from multiple sources in a single report or dashboard, Looker Studio can help you blend and visualise that data cohesively. 
  • No-code data exploration: Looker Studio is user-friendly for non-technical users, offering a drag-and-drop interface and no-code data exploration options. 

GA3 360 data collection end of life 1st July 2024 

Google will stop processing new data in GA3 360 from 1st July 2024.  It’s highly likely that your organisations started using GA4 360 a long time ago, in which case no immediate action is required.  If this isn’t the case, you’ll urgently need a plan to get on to GA4 360 asap. 

Business should consider what they want to do with their GA3 360 data in order to keep long term access to it.  Google are saying that they will support access to the data for at least six months. 

GA3 360 already has Big Query connectors, so you may already have a copy of your key data in here or another tool.  If not, you’ll need to decide on your legacy data and reporting requirements, and make a plan of action over the coming months. 

GA UA end of life for all access on 1st July 2024 

Google will cease to provide access to data and reports in GA UA on 1st July 2024. 

Therefore, if you may require access to any legacy data after this time, it’s vital you get a plan in place asap to extract and save it.  Unlike GA3 360, this is not straight forward because GA UA doesn’t natively connect to Big Query. 

We can help you decide on a strategy and execute it. 

If you are not sure what your longer-term needs may be, for now just saving the data somewhere may be your best option.  Whilst a further step would be having access to GA UA data in a data visualisation tool such as Looker Studio. 

The important thing isn’t to leave this to the last minute, and we suggest you work on this during March and April 2024.  If you need help, please get in touch.

Adobe Analytics 

Adobe Analytics (“AA”) is a powerful, sophisticated tool for digital analytics, offering deep insights into web and mobile app performance.  

Historically at least, it tended to be the analytics tool of choice for organisations with complex data capture and reporting requirements, and where the impact of Google Analytics’ data sampling and less flexible configuration options may have been a significant problem. 

AA enables businesses to track and analyse customer interactions across various channels, including websites, mobile apps, social media and more.  With real-time analytics, segmentation capabilities and predictive intelligence, AA helps organisations understand complex customer journeys, identify trends, and make data-driven decisions. 

Key AA features include: 

Customisable dashboards and reports: users can create tailored reports that highlight the metrics most relevant to their business goals, allowing for quick access to actionable insights. 

Advanced segmentation: AA allows for detailed segmentation of user data, enabling businesses to drill down into specific customer behaviours and preferences. 

Real-time data processing: it provides real-time analytics that help businesses respond promptly to customer interactions and market trends. 

Predictive analytics: utilising machine learning, Adobe Analytics can predict future customer behaviours, helping businesses anticipate needs and personalise experiences. 

Integration capabilities: as part of the Adobe Experience Cloud, AA integrates seamlessly with other Adobe products, enhancing data analysis and enabling cohesive marketing strategies across platforms. 

At the time of writing [Feb 2024], if you want a good quality and sharable reporting capability from within your analytics tool, AA is way ahead of GA4 unless you are also willing to invest in Google BigQuery, Looker Studio and put up with the many quirks and foibles of GA4. 

AA is also much more customisable than GA4 and GA4 360, which for many is a key strength of the Adobe.  However, the flip side means AA tends to be much more complicated and time-consuming to implement, resulting in many organisations needing specialist support from companies such as 120Feet. 

Adobe Customer Journey Analytics 

In 2023 Adobe started to push their new analytics solution “Adobe Customer Journey Analytics” (“CJA”). 

CJA and AA are both powerful analytics tools offered by Adobe, with each having distinct features and use cases.  Here are the key differences.

Data integration and sources: 

  • AA primarily focuses on digital data sources, like websites and mobile apps. 
  • CJA on the other hand, allows for the integration of a wider range of data sources, including offline data, CRM systems, and third-party data.  This broader data integration capability is a key differentiator. 

Data processing and architecture: 

  • CJA is built on the “Adobe Experience Platform” (“AEP”), which allows for more flexible data modelling and uses a different data processing approach.  It provides a more comprehensive view of the customer journey across various channels. 
  • AA, while robust in digital data processing, doesn’t natively support the same level of diverse data integration as CJA. 

Analysis capabilities: 

  • CJA offers more advanced cross-channel analysis features, enabling users to analyse the customer journey across different touchpoints more effectively. 
  • Adobe Analytics is very strong in web and app analytics but it’s more limited in cross-channel analysis compared to CJA. 
  • The somewhat frustrating concept of Props, eVars and Events don’t exist in CJA; so their limitations are eliminated too. 

User interface and experience: 

  • The user interface and experience of CJA will differ significantly from AA, reflecting its broader scope and different underlying architecture.

AI and machine learning: 

  • Both tools incorporate AI and machine learning but the capabilities can vary due to the different data models and integration capabilities. 

Customisation and flexibility: 

  • CJA tends to offer more flexibility in terms of data modelling and customisation thanks to its foundation on the Adobe Experience Platform. 

Use cases: 

  • AA is often the analytics tool of choice for businesses focusing primarily on web data and related digital channels. 
  • CJA is more suited for businesses looking for a comprehensive view of the customer journey that includes multiple channels and data sources, both online and offline and across the desktop site and app. 

While Adobe is currently selling CJA as an add on to standard AA agreements, their longer-term goal will certainly be to replace AA with CJA. 

Therefore, many AA users will probably start to plan a CJA implementation in 2024 / early 2025 and probably run it in parallel with AA for 12 months before switching over.  And it’s likely that some smaller Adobe clients may use this as an opportunity to review and switch to GA4 or GA360. 

“120Feet augment the Digital Analytics team at Bupa Global, covering all aspects from tagging implementation, analytical investigation to reporting and dashboarding. 

They are able to work with me and our stakeholders to understand which parts of the site are underperforming and then design and code new front-end experiences to improve site performance.” 

Digital Analytics Manager, Bupa Global 

120Feet has worked with Bupa for since 2011 

“I can’t stress enough how good they [120Feet] have been in terms of support and delivering against complex requirements. 

They have provided essential expertise to audit aspects of our Adobe and Google Analytics configuration and have efficiently migrated our tags from Adobe Launch to Tealium iQ. 

Overall, we’ve been very impressed.” 


120Feet has worked with AO since 2020 

“120Feet has worked with us for over 8 years, initially to audit and upgrade our analytics tracking to Universal Analytics and then to deploy the code through a TMS. 

We regularly work with them for new projects that we are bringing onto the website, scoping out the business reporting requirements and managing the analytics implementation process for us. They are considered an integral part of the team. 

In addition, they provide support for any ad-hoc tracking, reporting or analysis queries in a prompt and effective manner and help us with any implementation queries we have around analytics or third-party tagging.” 

Digital Analytics Manager, Aldermore 

120Feet has worked with Aldermore since 2016 

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Website experimentation is the practice of rapidly testing different versions of an element, page, or pages of a site to see which alternative works best.  It is sometimes called A:B or Multivariate (MVT) Testing.

The objective is to make continual user journey improvements by identifying and fixing potential issues before they become big or long-term problems.

120Feet can provide full-stack support and guidance from ideation to conclusion, including technical build, QA, analysis, and recommendations.

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Tag management systems streamline the process and accuracy of managing and deploying various tags on a website.

We work with all leading tag managers such as Google Tag Manager (“GTM”), Adobe Data Collection (previously “Launch”) and Tealium iQ through which we can deploy any marketing tag including analytics, campaign, email etc.

For many clients the process starts with a user requirements and tag audit, followed by a data layer and solution design, through to the configuration of the tag manager to deploy the marketing tags in accordance with an agreed specification.

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Delivering web analytics user training spanning implementation, dashboarding, analysis and data manipulation. 

Helping clients with data projects.  Perhaps to ingest data in to analytics systems or more usually to push data into client owned databases and data visualisation tools. 

We are an Observe Point partner and use their tool to automate tag testing ensuring analytics and other tag deployments are working as designed. 

Deploy privacy and cookie consent tools such as OneTrust and Usercentrics.

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