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    Home » A Practical Introduction to Google Analytics
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    A Practical Introduction to Google Analytics

    ProfitlyAIBy ProfitlyAIMay 30, 2025No Comments11 Mins Read
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    , I had the chance to work with Google Analytics, a robust platform for monitoring and understanding consumer behaviour throughout an e-commerce web site that sells clothes. 

    My job was to construct a knowledge pipeline that exports GA4 knowledge to BigQuery, a Google Cloud knowledge warehouse. Nevertheless, I rapidly bumped into a typical situation: most of the accessible guides had been outdated or inconsistent, which made the method extra time-consuming than anticipated.

    On this article, I’ll stroll you thru a transparent and up-to-date overview of Google Analytics, clarify its key ideas, spotlight a very powerful stories, and present you a working instance of the best way to export GA4 knowledge utilizing the Google Analytics Information API with Python.

    Curious to see what GA4 can do for you? Let’s dive in!


    Desk of Contents:

    • Life Cycle of Google Analytics
    • Dimensions and Metrics
    • Discover Core Reviews of GA4
    • Discover Information API

    Life Cycle of Google Analytics

    Illustration by Creator. Three phases of Google Analytics

    Google Analytics permits to know the completely different phases of the shopper journey, providing precious insights at every step.

    It begins with the Acquisition, the place you appeal to customers and spark consumer curiosity in what you are promoting. This stage focuses on channels and methods that carry the shopper to your web site or app.

    Subsequent is Engagement, which seems at how customers work together along with your content material or merchandise. For instance, they could browse pages, watch movies, or add gadgets to their buying cart.

    Then comes Monetization and Retention, that are a part of essentially the most essential section. Monetization section permits to know the place customers make purchases, turning them into clients, whereas Retention measures how typically customers return, serving to to evaluate long-term satisfaction and loyalty.

    By analyzing every stage of the journey, it’s potential to establish what’s working, uncover areas for enchancment and make smarter and data-driven selections to spice up the model’s efficiency.

    Dimensions and metrics

    Earlier than going additional, it’s necessary to know a elementary idea in Google Analytics: each report is constructed utilizing dimensions and metrics. Understanding how dimensions and metrics work collectively is crucial for deciphering stories and turning knowledge into significant data.

    Dimensions

    Dimensions are qualitative attributes that group your knowledge. The core dimensions are:

    • Marketing campaign is a paid promotion or Marketing marketing campaign
    • Supply is the place the consumer got here from. As an example, it may be from an internet site or a social community like Instagram and Fb.
    • Medium is the final class of the visitors supply, comparable to natural, CPC and referral. 
    • Channel is a rule-based group of visitors sources, mediums, or different guidelines to separate visitors. Examples of channels are Natural Search, Paid Search and Social.

    Metrics

    Then again, metrics comprise quantitative values. A very powerful metrics are:

    • Energetic customers are individuals who interact with the location or app
    • New customers are individuals who go to the location or app for the primary time
    • Returning customers are individuals who have visited beforehand
    • Classes are teams of consumer interactions inside a given timeframe
    • Engaged Classes are classes lasting not less than 10 seconds
    • Occasion is any tracked consumer motion, like clicking, scrolling and visiting for the primary time
    • Key occasion is a major motion that contributes to enterprise targets, comparable to a purchase order or a sign-up
    • Complete income is the revenue from purchases, subscriptions, and promoting

    Begin to play with Google Analytics

    Screenshot by Creator. An Overview of Google Merchandise Retailer.

    As soon as you’re acquainted with the important thing ideas of Google Analytics, it’s time to place them into follow on the platform itself. An ideal start line is Google’s introduction course to Google Analytics, which gives entry to a free demo account. This account lets you discover real-world knowledge and experiment with the platform’s options.

    On this tutorial, we’ll use Google Analytics to discover and analyse the info from the Google Merchandise Retailer, an internet retailer that sells Google-branded merchandise. It’s an ideal sandbox for studying the best way to observe consumer behaviour, monitor efficiency, and acquire actionable insights. You possibly can entry the Google Analytics demo account utilizing this link. It is best to see a web page just like the screenshot beneath.

    Screenshot by Creator. Residence web page of Google Analytics.

    These are pages which can be going to be explored:

    • Normal overview
    • Acquisition overview
    • Engagement overview
    • Monetisation overview
    • Retention overview

    Normal Overview

    GIF by Creator. Realtime Overview.

    The primary web page you’ll see is the house web page, which gives a high-level abstract of consumer behaviour on the Google Merchandise Retailer. It highlights key efficiency indicators, comparable to Energetic customers, Key occasions, Complete occasion depend and Purchases. These metrics provide a fast overview of how customers are partaking with the web site or app and the way nicely it’s performing.

    To view the metrics in real-time, click on the Reviews button and choose Actual-time overview. This function provides a stay snapshot of present consumer exercise, making it straightforward to watch what’s taking place on the web site because it happens.

    Screenshot by Creator. Realtime Overview.

    On the prime of the Actual-time Overview report, you’ll discover key efficiency indicators, together with the variety of energetic customers within the final 5 and half-hour. Beneath, a sequence of tables present an in depth breakdown of consumer exercise, exhibiting:

    • The place customers are coming from. It may be measured by metrics like supply, medium and channel.
    • Demographic and geographic knowledge can assist to know who the customers are.
    • What content material they’re viewing. Examples of content material are web page titles and display names.
    • What actions they’re taking.
    • Which key occasions they full.

    Acquisition Overview

    Screenshot by Creator. Acquisition Overview.

    The acquisition report is a precious software for understanding the place customers and visitors are coming from. On the prime, you’ll discover KPIs like Energetic customers and New customers, giving a snapshot of consumer exercise. Just under the KPIs, completely different tables present particular particulars of how customers are arriving on the web site. 

    Inside the overview part, there are two detailed stories. The primary report is the consumer acquisition report, which focuses on marketing campaign supply and medium that we see from that consumer. Subsequent is the visitors acquisition report, which considerations session supply, session medium and session marketing campaign.

    Engagement Overview

    Screenshot by Creator. Engagement Overview.

    Whereas the acquisition report solutions the query “The place are Customers and visitors coming from?”, the engagement report helps to know how customers are interacting with the location or app. 

    Key Efficiency Indicators on this report embody the typical engagement time per energetic consumer, the typical engaged session depend per energetic consumer, the variety of web page views (for web site) or display views (for app), and the depend of occasions.

    Beneath KPIs, two important tables present high-level particulars of consumer exercise. The desk grouping knowledge by occasion identify exhibits what customers are doing on the web site or app. Frequent occasions are viewing an online web page, starting a session or seeing a promotion banner or provide. The opposite desk segments the info by web page title and display class permitting us to know which pages persons are staying on the longest or essentially the most seen pages. 

    Monetization Overview

    Screenshot by Creator. Monetization Overview.

    Now, now we have the Monetization overview which presents high-level metrics to quantify income. These embody whole income, whole income from purchases and the entire income from promoting.

    Screenshot by Creator. E-commerce purchases report.

    To dive deeper into gross sales efficiency, you’ll be able to discover the e-commerce buy report. This report comprises a desk damaged down by merchandise identify, permitting us to see which merchandise are essentially the most worthwhile, seen, added to the cart or bought.

    From the desk, the Tremendous G Quilt Socks stand out as essentially the most performing gadgets with the best income.

    Retention Overview

    Screenshot by Creator. Retention Overview.

    Lastly, there may be the retention overview report that offers an thought of how nicely the web site or app retains customers over time. Key efficiency indicators embody the entire variety of new customers and the entire variety of returning customers.

    One of the vital helpful options of this report is the Consumer Retention visualisation that exhibits how often customers return after their preliminary go to. As an example, it solutions questions like “What number of customers who first visited on a particular date (date 0) got here again within the following days?”.

    This report is crucial for evaluating consumer loyalty and long-term engagement, serving to to establish developments and enhance methods to maintain customers coming again.

    Discover Information API

    Now that we perceive how the stories are structured, let’s take a step additional and see the best way to export knowledge utilizing the Google Analytics Information API.

    Arrange Information API

    Earlier than writing any code, we have to full a number of setup in Google Cloud Console:

    • Create a brand new undertaking or choose an already current one
    • Allow the “Google Analytics Reporting API” to your undertaking
    • Create a service account, generate the credentials and obtain the JSON file. 

    For deeper particulars, I like to recommend this YouTube video, which helped me quite a bit with Google Cloud Console setup. 

    After we should discover the property ID, which is required within the code later. This time we have to go to Google Analytics, press Admin from the menu and choose Property Particulars. Simply copy and paste the property ID in your code.

    The final step consists of putting in the mandatory Python libraries:

    pip set up google-analytics-data==0.18.18
    pip set up google-auth-oauthlib==1.2.2

    Export report Information utilizing Information API

    As soon as the setup is full, we will use the Google Analytics Information API to obtain the report knowledge with Python. Let’s say we wish to export a report exhibiting energetic customers and new customers, damaged down by date. On this case:

    • Metrics: activeUsers, newUsers
    • Dimensions: date

    To seek out the right discipline names for dimensions and metrics utilized by Information API, confer with the official GA4 API reference. It contains complete tables for every.

    Now, it’s time to point out an instance of code to export the info. First, we instantiate the analytics knowledge shopper. Then, we outline the report request with the size, metrics and date vary. Lastly, we will execute the report request.

    from google.analytics.data_v1beta import BetaAnalyticsDataClient
    from google.analytics.data_v1beta.sorts import (
        DateRange,
        Dimension,
        Metric,
        RunReportRequest,
    )
    
    PROPERTY_ID = "your-property-id"
    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "your-path-to-json-file"
    shopper = BetaAnalyticsDataClient()
    
    request = RunReportRequest(
            property=f"properties/{property_id}",
            dimensions=[Dimension(name="city")],
            metrics=[Metric(name="activeUsers"),Metric(name="newUsers")],
            date_ranges=[DateRange(start_date="2024-01-01", end_date="yesterday")],
        )
    
    response = shopper.run_report(request)

    To transform the API response right into a pandas Dataframe, we want another strains of code:

    # Extract column headers
    headers = [header.name for header in response.dimension_headers] + 
            [header.name for header in response.metric_headers]
    
    # Extract rows
    rows = []
    for row in response.rows:
        row_data = [dimension_value.value for dimension_value in row.dimension_values] + 
                [metric_value.value for metric_value in row.metric_values]
        rows.append(row_data)
    
    # Create a DataFrame
    df = pd.DataFrame(rows, columns=headers)

    That’s nice! We have now efficiently retrieved the report knowledge from Google Analytics utilizing the Information API. 

    Remaining ideas:

    This was an outline of Google Analytics, its core stories and Information API. With these instruments, you’ll be able to acquire a deeper understanding of the place customers are coming from, how they interact along with your content material, which merchandise are performing nicely and the way successfully your website is retaining guests over time. 

    Nevertheless, it’s value noticing a number of limitations of the Information API. There could also be discrepancies between the API and the GA4 Consumer Interface on account of knowledge processing delays. Google Analytics interface can replace even earlier day’s knowledge after a brief lag. Furthermore, Google Analytics typically applies knowledge sampling, particularly on giant datasets, which can result in mismatches when evaluating outcomes with the uncooked API output. 

    Regardless of the challenges, getting began with Google Analytics is a precious step towards making data-informed selections. I hope this tutorial supplied a transparent and sensible start line to start with confidence. Thanks for studying! Have a pleasant day!


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