Google Analytics 4’s new metrics can reveal more nuanced, time-dependent insights.
While marketers have been working to update their Google Analytics accounts to the latest iteration, GA4, Google has been releasing new iterations of the features in its widely adopted analytics solution. One announcement that has been heralded among analytics practitioners is time — or in this case, more precise time dimension metrics for Google Analytics 4 (GA4).
The new metrics are called the Date and Hour dimensions. They are now available in the customizations for both Google Analytics 4 explorations and report settings.
Date and Hour Dimensions Allow More Nuanced Reports
Time series data is an understandable part of analytics reporting — how many conversions occur in each campaign period or the growth of app sessions over time. In most analytic reports, metrics are shown over a daily timeline. The Date and Hour dimensions enhance that display with options for more nuanced time periods in an exploration or a custom report. When selected, the data and hour dimensions are populated automatically in the report or visuals.
Date and hour dimension choices include the following time periods: Hour, Date + hour, Week, Month, and Year. Each dimension metric is measured according to a GA4 event being measured.
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Hour is the hour when an event was collected.
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Date + hour is the date and hour when an event was collected.
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Week is the week of the event, a two-digit number from 01 to 53. Each week starts on Sunday, and January 1st is always in Week 1.
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Month is the month of the event, a two-digit integer from 01 to 12.
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Year is year when an event was collected, formatted as a four-digit number (e.g., 2020).
Each one of these periods can also be used to set periods from the start of a specified date range. For example, the “Nth” day is the number of days since the start of the date range. The same can be applied to the Nth hour (number of hours since the start), Nth month (number of months), Nth week (number of weeks), and Nth year (years since the start of the specified date range).
Related Article: New Google Analytics Metrics Improve How Marketers Learn About Customer Experience
Marketers Can Use Date and Hours for Enhanced Insights
Marketers can use date and hours dimensions to quickly answer when certain event conditions occurred relative to site visits or reveal more nuanced time-dependent insights. Examples of questions include the following:
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How did my online sales data trend hourly after a campaign ad aired?
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What months do I see the most traffic on my website?
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Are users of my app or website more likely to convert in the evening hours or during the morning hours?
Marketers can use this data to understand if related customer experiences are occurring regularly within a specific time range. Knowing if customers convert in certain periods narrows down the activities that are influencing purchase decisions in real time. GA4 also has user-level time metrics that display a timeline of customer-triggered events that lead into a conversion activity. This can be compared against the time dimensions results to determine insights as to what happened on a website or on an app within a given amount of time.
Marketers can also compare the length of time that people cycle through conversion events against data retention, the time allowed for retaining user-level and event-level activity. This helps start some discussions on how long customers are processing through a site and if some behaviors are a factor for data retention.
Related Article: Leveraging Google Data Studio as the GA4 Transition Looms
Time Dimensions Are Also Accessible Through GA Data API
A terrific aspect of time dimensions metrics is that they are also accessible through the Google Analytics Data API. This is useful for importing the data within R or Python and incorporating it into an advanced time series analysis. The ability to import Google Analytics data in an API has long been available but adding the date and hour metrics makes that export a more convenient enhancement for advanced analysis.
So, for example, an augmented Dickey-Fuller test (ADF) can be applied to time series-related data in the Google Analytics to determine if stationarity exists in the dataset. Stationarity determines if a pattern in a time series can be used for a forecast model. This helps marketers predict the sustainability of a growth pattern, influencing strategic decisions such as investing more inventory in a product or service that has been growing steadily.
As I explain about time series analysis in my CMSWire post, granular time series data is significant for advanced business analysis such as forecasting demand and service operations. Advanced analysis models rely on granular time series data applied with statistical calculations to forecast trend and cyclical insights. Analysts can review time periods in R or Python with more precision than within cloud solutions, making on-the-spot decisions to remove noise that masks trends or incorporates data from multiple sources during a discrete period. If you want to understand if sales activity correlated to weather periods, you now have a means to run that analysis.
Related Article: Google Analytics 4 and Making the Most of the Customer Lifecycle
Final Thoughts on Google’s Time and Date Dimensions
With the new ability to add time dimensions into reports, Google has raised analysis quality for marketers. It may be a pun, but it arrives just in time as marketers make the transition into Google Analytics 4. Time dimensions can provide the right way of revealing cohorts, shifts in audience behavior and opportunities for engaging customers to bolster their experience with your brand.
