From Econometrics to GA4: A Guide to Modeling User Behavior with BigQuery and Gretl

Introduction

If you have a background in econometrics, you’re already equipped with a powerful toolkit for understanding complex systems. Concepts like Ordinary Least Squares (OLS), time-series analysis, stationarity, and multicollinearity are your bread and butter. This article will show you how to apply that exact knowledge to a new domain: the rich, granular world of digital analytics, specifically the Google Analytics 4 (GA4) BigQuery Export.

We will bridge the gap between theoretical econometrics and practical business strategy. You will learn how to go beyond simple correlations to build a statistically robust model that explains how different types of content on a website contribute to a key business objective.

To make this practical, we will use a hypothetical scenario: a fan website for the video game “The Last of Us.” The site’s primary goal is to encourage visitors to subscribe to its newsletter. We want to determine which content—character bios, lore pages, or walkthroughs—is most effective at driving these subscriptions.

This guide will walk you through the entire process:

1. Extracting the necessary time-series data from GA4 using a BigQuery SQL query.
2. Analyzing that data using the free statistical software Gretl, following a rigorous econometric checklist.
3. Interpreting the model results to produce actionable strategic recommendations.

Part 1: Getting the Necessary Data in BigQuery

Our first step is to create a time-series dataset. We need daily counts of pageviews for our key content pages (the independent variables) and daily counts of our conversion event (the dependent variable). For our “The Last of Us” fan site, a successful newsletter subscription is registered when a user views the /newsletter-thank-you.html page.

Our key content pages are:

• /lore/cordyceps-fungus
• /characters/joel-miller
• /walkthroughs/part-1
• /media/show-vs-game-comparison

The following BigQuery SQL query will structure this data for us, creating a clean table with one row per day, ready for export.

BigQuery SQL for Data Extraction

This query first calculates daily pageviews for all pages, then pivots the data to create separate columns for our specific pages of interest, joining them all on the date.

Please remember to replace your-project.your_dataset.events_* with your own project, dataset, and table name before running the query!

WITH pageviews_counts AS (
    -- Step 1: Count daily pageviews for every page on the site.
    SELECT
        event_date,
        (SELECT value.string_value FROM UNNEST(event_params) 
            WHERE key = 'page_location') AS page,
        COUNTIF(event_name = 'page_view') AS page_views
    FROM
        -- Replace with your own project, dataset, and table name
        `your-project.your_dataset.events_*`
    WHERE
        -- We will analyze the last 30 days of data.
        _table_suffix BETWEEN 
            FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)) 
            AND FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
    GROUP BY
        event_date,
        page
),

lore_page AS (
    -- Step 2: Aggregate views for the 'Lore' page.
    SELECT 
        event_date,
        SUM(page_views) AS page_views_lore
    FROM 
        pageviews_counts
    WHERE 
        page LIKE '%/lore/cordyceps-fungus%'
    GROUP BY 
        event_date
),
  
character_page AS (
    -- Step 3: Aggregate views for the 'Character' page.
    SELECT 
        event_date,
        SUM(page_views) AS page_views_character
    FROM 
        pageviews_counts
    WHERE 
        page LIKE '%/characters/joel-miller%'
    GROUP BY 
        event_date
),
  
walkthrough_page AS (
    -- Step 4: Aggregate views for the 'Walkthrough' page.
    SELECT 
        event_date,
        SUM(page_views) AS page_views_walkthrough
    FROM 
        pageviews_counts
    WHERE 
        page LIKE '%/walkthroughs/part-1%'
    GROUP BY 
        event_date
),
  
comparison_page AS (
    -- Step 5: Aggregate views for the 'Comparison' page.
    SELECT 
        event_date,
        SUM(page_views) AS page_views_comparison
    FROM 
        pageviews_counts
    WHERE 
        page LIKE '%/media/show-vs-game-comparison%'
    GROUP BY 
        event_date
),
    
newsletter_signups AS (
    -- Step 6: Aggregate views for our conversion page (the dependent variable).
    SELECT 
        event_date,
        SUM(page_views) AS newsletter_signups
    FROM 
        pageviews_counts
    WHERE 
        page LIKE '%/newsletter-thank-you.html%'
    GROUP BY 
        event_date
),

combined_page_data AS (
    -- Step 7: Join all data together by date.
    -- We use a LEFT JOIN from the newsletter signups to ensure we don't drop days with zero traffic to other pages.
    SELECT
        ns.event_date,
        ns.newsletter_signups,
        COALESCE(lp.page_views_lore, 0) AS page_views_lore,
        COALESCE(cp.page_views_character, 0) AS page_views_character,
        COALESCE(wp.page_views_walkthrough, 0) AS page_views_walkthrough,
        COALESCE(comp.page_views_comparison, 0) AS page_views_comparison
    FROM 
        newsletter_signups ns
    LEFT JOIN 
        lore_page lp ON ns.event_date = lp.event_date
    LEFT JOIN 
        character_page cp ON ns.event_date = cp.event_date
    LEFT JOIN 
        walkthrough_page wp ON ns.event_date = wp.event_date
    LEFT JOIN 
        comparison_page comp ON ns.event_date = comp.event_date
)

-- Final result set ready for export.
SELECT
    *
FROM combined_page_data
ORDER BY
    event_date;

After running this query in the BigQuery UI, export the results as a CSV file. This file is the input for our econometric analysis in Gretl.

Part 2: A Practical Guide to Analyzing Web Traffic Relationships Using Gretl

Objective: Now that we have our time-series data, we will use the free statistical software Gretl to move beyond basic correlation and build a robust regression model. The goal is to understand which content pages have a statistically significant impact on newsletter signups. This process emphasizes diagnostic testing to ensure our conclusions are valid, reliable, and defensible—a core principle of econometrics.

Step 1: Data Preparation and Import into Gretl

A. Data Format Your exported CSV file from BigQuery should be structured with event_date in the first column, followed by columns for daily newsletter_signups and pageviews for each content page (page_views_lore, page_views_character, etc.).

B. Importing and Setting Up Time-Series Data in Gretl

1. Open gretl.
2. Go to File -> Open data -> User file. Select the CSV you saved.
3. Gretl will ask to interpret the data. Click OK to confirm the column structure.
4. A crucial prompt will appear: “Give the data a time series interpretation?”. Click Yes.
5. In the next window, select Time Series, click Forward.
6. Select Daily, click Forward.
7. Finally, click Apply. Your data is now correctly configured for time-series analysis.

Step 2: Correlation Analysis (The Quick Look)

This is a preliminary step to get a feel for the data. Correlation shows us the direction and strength of the relationship between any two variables, but it doesn’t control for the influence of others.

1. In the main Gretl window, select all your variables (hold Ctrl and click each one).
2. Go to View -> Correlation Matrix... and click OK.

Interpretation: You will see a table where each cell shows the correlation coefficient (from -1 to +1) between two variables.

• Values near +1: Indicate a strong positive relationship (e.g., as views on the walkthrough page rise, newsletter_signups also tend to rise).
• Values near -1: Indicate a strong negative relationship.
• Values near 0: Indicate a weak or non-existent linear relationship.

Caution: Correlation can be misleading. Two variables might appear correlated simply because they are both influenced by a third, unobserved factor (like a general increase in site traffic). Regression analysis will help us untangle these effects.

Step 3: Building and Interpreting the Initial OLS Model

We will now build our first model to quantify how our content pages (regressors) predict newsletter signups (the dependent variable).

A. Running the Model

1. Go to Model -> Ordinary Least Squares....
2. In the dialog box, select newsletter_signups and move it to the dependent variable box.
3. Select page_views_lore, page_views_character, page_views_walkthrough, and page_views_comparison and move them to the regressors box.
4. Click OK.

You will get an output window. Let’s assume it looks like this fictional example:

Model 1: OLS, using observations 1-30
Dependent variable: newsletter_signups

                        coefficient   std. error   t-ratio    p-value 
  ---------------------------------------------------------------------
  const                   12.1532      5.4811       2.217     0.0359   **
  page_views_lore          0.0157      0.0289       0.543     0.5915   
  page_views_character     0.0811      0.0325       2.495     0.0195   **
  page_views_walkthrough   0.2045      0.0451       4.534     0.0001   ***
  page_views_comparison   -0.1182      0.0512      -2.308     0.0298   **

Mean dependent var      152.4500   S.D. dependent var      45.6781
Sum squared resid       7854.251   S.E. of regression      17.7249
R-squared                0.9153    Adjusted R-squared      0.8998
F(4, 25)                 67.4312   P-value(F)              1.55e-12
Log-likelihood         -125.1124   Akaike criterion        260.2248
Schwarz criterion      -125.1124   Hannan-Quinn            262.5517
Mean abs. Prct. Error     8.5%

B. Understanding the OLS Output The output has two key sections: summary statistics (overall model health) and the coefficient table (individual variable impact).

1. Summary Statistics: Overall Model Performance Always start here.

• F-statistic and P-value(F) — The “Gateway” Test
  • The Question It Answers: Is my model, as a whole, any better at explaining newsletter signups than simply using the average number of signups?
  • Interpretation: In our example output, look at the line P-value(F). The value is 1.55e-12, which is a very tiny number (0.00000000000155). Since this is far less than 0.05, our model is highly significant overall. We can confidently proceed.
• R-squared / Adjusted R-squared and MAPE
  • Adjusted R-squared: Look at Adjusted R-squared. Our value is 0.8998. This is excellent. It tells us that our four content pages explain about 90% of the daily variation in newsletter signups.
  • Mean absolute percentage error (MAPE): Gretl calculates this at the bottom as Mean abs. Prct. Error. Our value is 8.5%. This means our model’s predictions for daily signups are, on average, off by only 8.5%. This indicates a good predictive accuracy.

Conclusion: The summary statistics paint a very positive picture. The model is significant, explains a lot of the variance, and is reasonably accurate. Now we can look at the individual parts.

2. The Coefficient Table: Individual Variable Effects If your model passed the F-test, you can now analyze the individual variables from the top part of the output.

• page_views_walkthrough
  • Coefficient: 0.2045. For every 100 extra views of the walkthrough page, the model predicts an additional 20.5 newsletter signups, holding all other traffic constant.
  • p-value: 0.0001. This is much less than 0.05, making the result highly statistically significant. This is a strong, reliable driver.
• page_views_character
  • Coefficient: 0.0811. For every 100 extra views of the character page, the model predicts about 8 additional signups.
  • p-value: 0.0195. This is less than 0.05, so this effect is also statistically significant.
• page_views_comparison
  • Coefficient: -0.1182. This is a fascinating result. It suggests that for every 100 extra views on the game vs. show comparison page, we see about 12 fewer newsletter signups.
  • p-value: 0.0298. This is less than 0.05, meaning this negative relationship is statistically significant. This page may attract a different kind of audience that is less interested in subscribing.
• page_views_lore
  • Coefficient: 0.0157. A small positive effect.
  • p-value: 0.5915. This is much greater than 0.05. Therefore, this result is not statistically significant. We cannot conclude that traffic to the lore page has any reliable impact on newsletter signups. Its small effect is likely due to random chance.

Step 4: The Advanced Model Health Checklist (Validation)

This is the most critical phase for any econometrician. We must test the assumptions of OLS to ensure our coefficients and p-values are trustworthy. Run these tests from the OLS model output window (Model 1).

Test 1: Stationarity (Are the relationships real or just a shared trend?)

• Why: Non-stationary data (data with a trend) can lead to “spurious regression,” where unrelated variables appear significant just because they are both trending up over time.

• How: In the main Gretl window, right-click on each variable (e.g., newsletter_signups) and go to Unit root tests -> Augmented Dickey-Fuller test....

• Interpreting the Confusing Output: The ADF test output can be dense. You are looking for one specific p-value.

  You only need to look for the asymptotic p-value. Here it is 0.0011. Since this is < 0.05, the variable is stationary. This is good.

  Now, let’s say we test page_views_lore and find it’s non-stationary:

  Augmented Dickey-Fuller test for page_views_lore
  test statistic: tau_c(1) = -1.2345
  asymptotic p-value 0.6489  <-- Problem!
  

  The p-value is 0.6489. Since this is > 0.05, this variable is non-stationary.

• Interpretation: A p-value < 0.05 is good; the data is stationary. A p-value > 0.05 is bad; the data is non-stationary.

• Fix: We must fix this. In Gretl, select page_views_lore, then Add -> Differences of selected variables. This creates a new variable, d_page_views_lore. We must use this new differenced variable in our model instead of the original.

Test 2 & 3: Autocorrelation and Heteroskedasticity (Are the model’s errors random?)

• Why: OLS assumes errors are random. Autocorrelation (where today’s error is correlated with yesterday’s) or Heteroskedasticity (where the model’s error size varies over time) can make your p-values artificially small, leading you to believe a relationship is significant when it isn’t.
• How (From the OLS output window):
  • Autocorrelation: Tests -> Autocorrelation....
  • Heteroskedasticity: Tests -> Heteroskedasticity -> White's test.
• Interpretation: For both tests, a p-value > 0.05 is good (no problem). A p-value < 0.05 means your standard errors (and thus p-values) are unreliable.
• Fix: The standard industry fix is to use robust standard errors. From the OLS output window, go to Model -> Robust standard errors... -> Newey-West. This will generate a new model output with corrected p-values. The coefficients will not change, but their significance levels will be more accurate.

Test 4: Model Specification (Is the model’s basic form correct?)

• Why: This checks if you’ve missed important non-linear relationships.
• How (From the OLS output window): Tests -> Specification -> Ramsey's RESET....
• Interpretation: A p-value > 0.05 is good (your linear model is likely sufficient). A p-value < 0.05 suggests you may need a more complex model.
• Fix: Try adding transformations of your variables. A common fix is to log-transform them (Add -> Logs of selected variables) or add a time trend (Add -> Time trend) to the model as a regressor.

Test 5: Multicollinearity (Are your explanatory variables too similar?)

• Why: If two of your content pages (e.g., lore and character) have very similar traffic patterns, the model can’t distinguish their individual effects on newsletter signups. This inflates the standard errors, making significant variables appear insignificant.
• How (From the OLS output window): Tests -> Collinearity.
• Interpretation: Look at the Variance Inflation Factor (VIF). A VIF below 5 is excellent. A VIF between 5 and 10 is a concern. A VIF above 10 indicates a serious multicollinearity problem.
• Fix: Remove the explanatory variable with the highest VIF and re-run the model.

Step 5: The Iterative Workflow for a Robust Model

Analysis is not a one-shot process. The professional standard is an iterative loop:

1. Estimate: Run your initial OLS model.
2. Validate: Go through the full health checklist (Step 4).
3. Fix: If you find a problem (e.g., heteroskedasticity), apply the appropriate fix (e.g., use robust standard errors).
4. Re-estimate & Repeat: Re-run the validation checks on your new, corrected model. Continue this cycle until your model passes all the diagnostic tests.

Step 6: Final Evaluation and Strategic Recommendations

Once you have a “clean” model that has passed all health checks, you can confidently interpret the results and translate them into strategy. Use the coefficients and p-values from your final, validated model output.

A. Final Model Evaluation

• Check the Gateway: Confirm the final model’s P-value(F) is < 0.05.
• Assess the Fit: Is the Adjusted R-squared high enough to be meaningful for your business?
• Judge the Accuracy: Is the MAPE acceptably low?

Let’s assume after running all our validation checks, we found two things:

1. page_views_lore was non-stationary.
2. The model had heteroskedasticity (p-value on White’s test was 0.03).

Our fix is to:

1. Replace page_views_lore with d_page_views_lore.
2. Use Newey-West robust standard errors to correct the p-values for heteroskedasticity.

This produces our final, validated model output:

Model 2: OLS, using observations 2-30 (differencing reduced obs.)
Dependent variable: newsletter_signups
Robust standard errors (Newey-West, lag 2)

                        coefficient   std. error   t-ratio    p-value 
  ---------------------------------------------------------------------
  const                   13.0511      4.9821       2.620     0.0150   **
  d_page_views_lore        0.0452      0.0411       1.099     0.2825
  page_views_character     0.0755      0.0298       2.533     0.0182   **
  page_views_walkthrough   0.1988      0.0402       4.945     0.0001   ***
  page_views_comparison   -0.1240      0.0455      -2.725     0.0118   **

Mean dependent var      153.1110   S.D. dependent var      46.1234
Adjusted R-squared       0.9015    P-value(F)              2.18e-12

Notice the p-values have changed slightly, but our main conclusions hold. This is the model we trust.

B. Strategic Recommendations Now, we build our strategy table by directly linking it to our final, validated model output (Model 2).

Conclusion

This guide has demonstrated that the principles of econometrics are not confined to academic journals or macroeconomic forecasting; they are a powerful and immediately applicable tool for the modern digital analyst. By moving beyond surface-level correlations and embracing a rigorous, validation-focused approach to regression, we can transform raw GA4 BigQuery data into a source of profound business intelligence.

We’ve walked through the complete workflow: from crafting a BigQuery SQL query to extract a clean time-series dataset, to performing a comprehensive OLS analysis in Gretl. The critical takeaway is the emphasis on the model validation checklist. Testing for stationarity, autocorrelation, multicollinearity, and proper specification is what separates a fragile, potentially misleading analysis from a robust, defensible model. It is this diligence that allows us to confidently distinguish between content that merely attracts traffic and content that actively drives strategic goals.