{"id":13688,"date":"2022-05-25T14:02:28","date_gmt":"2022-05-25T14:02:28","guid":{"rendered":"https:\/\/www.searchenginejournal.com\/ga-4-reorgs-agency-reviews\/447065\/"},"modified":"2022-05-25T14:02:28","modified_gmt":"2022-05-25T14:02:28","slug":"google-analytics-4-should-trigger-reorganizations-agency-reviews-via-sejournal-gregjarboe","status":"publish","type":"post","link":"https:\/\/marketingnewsbox.com\/?p=13688","title":{"rendered":"Google Analytics 4 Should Trigger Reorganizations & Agency Reviews via @sejournal, @gregjarboe"},"content":{"rendered":"
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By now, you may already know that GA4 operates across platforms, uses an event-based data model to deliver user-centric measurement, and does not<\/strong> rely exclusively on cookies.<\/p>\n

And you recognize that GA4 uses machine learning to generate sophisticated predictive insights about user behavior and conversions, create new audiences of users likely to purchase or churn, and automatically surface critical insights to improve your marketing.<\/p>\n

Heck, you may have already started to move to GA4 as soon as possible to build the necessary historical data before Universal Analytics (UA) stops processing new hits on July 1, 2023, and UA 360 stops new hit processing on Oct. 1, 2023.<\/p>\n

Many people may mistakenly think they have a good bead on things.<\/p>\n

Well, I was at Pubcon Las Vegas on Nov. 14, 2005, when Google announced that Urchin Software, which it had acquired in April of that year, was being renamed Google Analytics. Yep, I was in the room where it happened.<\/p>\n

I was standing next to one of my clients, John Marshall, the CEO of ClickTracks Analytics, which offered a range of competing solutions that cost $495, $1,195, or $3,495.<\/p>\n

That\u2019s when we both heard that the basic version of Google Analytics was free for the first time.<\/p>\n

So, I know a little something about the impact of new versions of Google\u2019s web analytics service.<\/p>\n

And, I\u2019ve learned that you don\u2019t need to wait for machine learning<\/a> to generate sophisticated predictive insights about a couple of \u201cevents\u201d that the adoption of GA4 is likely to trigger for your organization or clients in the next 14 months.<\/p>\n

One is a reorganization. The other is an agency review.<\/p>\n

The Reorg<\/h2>\n

The \u201cweb analytics\u201d team still sits in the IT department in far too many organizations.<\/p>\n

Why?<\/p>\n

Because the team was originally created back in 1995 when web analytics meant servers, log files, and complex handwritten code to parse the log files and pump out reports.<\/p>\n

So, putting them in the IT department made perfect sense back then.<\/p>\n

But, data collection, storage, and processing have all moved into the cloud (hosted by your application service provider rather than in-house).<\/p>\n

This eliminated the need to maintain IT teams for web analytics, except perhaps to update measurement codes and related code fragments collectively known as \u201ctags\u201d on your website or mobile app.<\/p>\n

In addition, your website itself has transformed from being \u201cbrochure-ware\u201d back in the early days into an increasingly integral part of your business \u2013 both online and offline.<\/p>\n

Nothing highlights this change more than the fact that we no longer count the number of client requests (or hits) made to the web server like they did a generation ago.<\/p>\n

Because of these trends, the \u201cdigital analytics\u201d team doesn\u2019t belong in IT anymore.<\/p>\n

Where does it belong?<\/p>\n

Well, ask yourself three questions:<\/p>\n

Who uses analytics?<\/h3>\n

Marketing (not IT) needs to see unified customer journeys across their websites and apps.<\/p>\n

Marketing (not IT) needs to use Google\u2019s machine learning technology to the surface and predict new insights.<\/p>\n

And marketing (not IT) needs to keep up with evolving customer needs and expectations.<\/p>\n

Who directs implementation?<\/h3>\n

Marketing (not IT) needs to decide which recommended events to add, which suggested audiences to use, and which events to mark as conversions.<\/p>\n

Marketing (not IT) needs to decide what associate monetary values to use for micro-conversions, custom insights to create, and anomalies to act on.<\/p>\n

And marketing (not IT) should decide which other platforms, such as Google Ads, Search Console, and Salesforce Marketing Cloud, to integrate with GA4.<\/p>\n

Who owns reporting?<\/strong><\/h3>\n

Marketing (not IT) needs to drive sales or app installs, generate leads, or connect online and offline customer engagement.<\/p>\n

So, marketing (not IT) needs to use data-driven attribution to analyze the full impact of their latest campaigns and ongoing programs across the customer journey.<\/p>\n

And marketing (not IT) needs to export that analysis to Google Ads and the Google Marketing Platform\u2019s media tools to optimize those campaigns and programs.<\/p>\n

This is why digital analytics belongs in marketing \u2013 and it has belonged there for more than 10 years.<\/p>\n

But, inertia is a powerful force \u2013 and most people hate reorgs \u2013 which explains why far too many organizations are loath to move their analytics team out of IT and into marketing.<\/p>\n

So, why do I think that GA4 will be the irresistible force to overcome this immovable object?<\/p>\n

Well, one of the features that you\u2019ve already heard about is Analytics Intelligence<\/strong>, which uses machine learning and conditions that you need to configure to help you understand and act on your GA4 data.<\/p>\n

And one of the statistical techniques that Analytics Intelligence uses is Anomaly detection<\/strong>.<\/p>\n

Using historical data, Analytics Intelligence \u201clearns\u201d to predict the value of metrics for the current time period and flags any data points as anomalies if their actual value falls outside a \u201ccredible\u201d interval.<\/p>\n

For detection of weekly anomalies, the training period for GA4\u2019s machine learning is 32 weeks.<\/p>\n

For detection of daily anomalies, the training period is 90 days. And for the detection of hourly anomalies, the training period is two weeks.<\/p>\n

In other words, somewhere between 2 and 32 weeks after GA4 is set up and starts collecting data, Analytics Intelligence\u2019s machine learning will be sufficiently trained to analyze your data and predict future actions that your end-users may take.<\/p>\n

That\u2019s when marketers will begin seeing \u201cInsights\u201d appear on their GA4 Home page.<\/p>\n

These Insights will show unusual changes, emerging trends, and other anomalies about your site or app.<\/p>\n

Seeing specific Insights can help you quickly identify data changes that warrant further analysis and action.<\/p>\n

That\u2019s when the marketing department will start \u201cfreaking out\u201d if the IT department doesn\u2019t respond to urgent requests for \u201chelp\u201d within a week, a day, or even an hour.<\/p>\n

And that\u2019s when the business case for moving the analytics team from IT to Marketing will suddenly become data-driven.<\/p>\n

Why is this scenario likely to ripple across organizations worldwide over the next 14 months?<\/p>\n

Well, early adopters of GA4 have already reported the benefits of getting a complete view of their customer lifecycle with an event-based measurement model that isn\u2019t fragmented by platform or organized into independent sessions.<\/p>\n

And I\u2019d argue that the same benefits are available to an organization that isn\u2019t fragmented by department or organized into independent silos.<\/p>\n

For example, Gymshark, a fitness apparel and accessories brand based in the UK, used GA4 to understand its customers across touchpoints on its website and app.<\/p>\n

This enabled the Gymshark team to see how users moved through the purchase funnel. As a result, they reduced their user drop off by 9%<\/a>, increased their product page clickthroughs by 5%, and reduced their time spent on user journey analysis by 30%.<\/p>\n

Oh, and non-profits can benefit from seeing the user journey from end to end, too.<\/p>\n

For example, 412 Food Rescue, a non-profit organization based in Pittsburgh, needed to recruit more volunteers to deliver food from retailers to people experiencing food insecurity.<\/p>\n

Automated Insights in GA4 showed their team that weekends tended to be a little bit slower in terms of volunteers and engagement, so they adjusted the social media campaigns that were driving traffic to their website.<\/p>\n

And they\u2019ve cut their reporting time by 50%<\/a>, which has freed up their already limited staff to grow their impact throughout the community and expand to new cities.<\/p>\n

Watch \u201cGoogle Analytics: 412 Food Rescue Case Study\u201d, which was uploaded to YouTube on Mar. 24, 2021, to hear the team tell their story in their own words.<\/p>\n

[embedded content]<\/p>\n

This brings us to the second \u201cevent\u201d that GA4 is likely to trigger for your organization or clients: An agency review.<\/p>\n

Agency Review<\/h2>\n

Now, some big ad agencies were using Google Analytics with DoubleClick\u2019s advertising services, which Google Acquired in March 2008, even before the Google Marketing Platform was launched on July 24, 2018.<\/p>\n

So, they should weather the storm created by the move to GA4 without too much difficulty.<\/p>\n

But, many other ad agencies will need to hold an \u201call hands on deck\u201d meeting to figure out how to hang on to a client that\u2019s just configured their GA4 property and started recording YouTube Web Engaged View Conversion (EVC) events.<\/p>\n

To do that, the client:<\/p>\n