Year-over-Year SEO Trend Analysis

Performing Year-Over-Year Comparisons and Historical Trend Analysis

Year-over-year (YoY) comparisons and historical trend analysis are essential for understanding long-term SEO performance. By tracking changes over months and years, you can identify seasonal trends, evaluate the impact of SEO strategies, and gain valuable insights into search behavior over time. Using Google Search Console data in BigQuery, you can structure your data to perform SEO trend analysis and generate SEO year-over-year comparisons that provide actionable insights in Looker Studio.

1. Why Year-Over-Year Comparisons and Trend Analysis Matter

Analyzing SEO data over time allows you to:

  • Spot Seasonal Trends: Recognize if certain keywords or topics peak at specific times of the year, such as holidays or industry events.
  • Assess SEO Strategy Impact: Determine whether recent optimization efforts have positively affected metrics like clicks, impressions, and rankings.
  • Make Informed Predictions: Use historical patterns to predict future performance and adjust strategies proactively.

Year-over-year comparisons reveal trends that aren’t visible in short-term data, making them a key part of strategic planning.

2. Preparing Data for Trend Analysis in BigQuery

Setting up BigQuery to analyze historical data enables easy access to year-over-year comparisons and long-term trend insights.

  • Step 1: Collect Data Over Time in BigQuery

    • Ensure your Google Search Console data is exporting daily into BigQuery so you can build a historical dataset. Each row should include fields such as date, query, clicks, impressions, CTR, and position.
    • You may need at least a year’s worth of data to perform effective year-over-year comparisons.

  • Step 2: Structure Data for YoY Comparison

    • Use SQL to format your data by year. This enables easy side-by-side comparisons between different years.
      SELECT
        EXTRACT(YEAR FROM date) AS year,
        EXTRACT(MONTH FROM date) AS month,
        query,
        SUM(clicks) AS total_clicks,
        SUM(impressions) AS total_impressions,
        AVG(position) AS avg_position,
        (SUM(clicks) / SUM(impressions)) * 100 AS ctr_percentage
      FROM
        `your_project.your_dataset.search_data`
      GROUP BY
        year, month, query
      ORDER BY
        year, month;
    • This query aggregates data by month and year, summarizing metrics like clicks, impressions, and CTR for each time period.

  • Step 3: Save the Data as a View or Table

    • Save this query as a view or table for easy access and integration with Looker Studio. This structure simplifies YoY comparisons by organizing data across multiple years.

3. Creating Year-Over-Year Comparisons in Looker Studio

Connecting BigQuery to Looker Studio allows you to visualize trends and YoY comparisons in an easy-to-read format.

  • Step 1: Connect BigQuery Data to Looker Studio

    • In Looker Studio, add BigQuery as a data source and select the view or table you created with year and month data.

  • Step 2: Set Up YoY Comparison Visualizations

    • Use line charts or bar graphs to visualize data by month for multiple years, providing a clear view of YoY changes in clicks, impressions, and CTR.
    • For example, a line chart showing clicks per month for 2023 vs. 2022 helps highlight performance differences over time.

  • Step 3: Add Comparison Filters for Specific Keywords or Pages

    • Use filters to focus on individual queries, pages, or countries, allowing you to compare the performance of specific keywords or content over time.
    • Add date range controls to allow users to switch between different time periods, making it easy to analyze data from various years.

Example: A bar chart displaying CTR for December 2023 vs. December 2022 helps reveal if your holiday-specific SEO strategies have improved over the past year.

With your data organized by time period, you can start interpreting trends to uncover actionable insights.

  • Step 1: Identify Long-Term Growth or Decline

    • Look for keywords or content categories showing consistent growth or decline over multiple years. This helps in understanding shifts in search demand.
    • In Looker Studio, set up line charts showing impressions and clicks over multiple years to observe these long-term trends.

  • Step 2: Detect Seasonal Peaks and Lows

    • Seasonal keywords often show recurring spikes and drops. Use a year-over-year chart to see if keywords or pages peak consistently during certain months.
    • For instance, a YoY chart showing high search volume for “holiday gifts” in December can inform your content planning for that time frame.

  • Step 3: Compare CTR and Position Changes Over Time

    • CTR and average position provide insights into how search intent and competition have evolved. A YoY comparison of these metrics can reveal shifts in user behavior or highlight the impact of recent SEO optimizations.

  • Step 4: Use Annotations for Context

    • Add notes in Looker Studio to highlight events or updates that may have impacted your performance, such as algorithm changes, site updates, or new SEO strategies. This contextual information helps explain sudden changes in trends.

Example: If your YoY comparison shows a CTR increase for specific keywords, it might indicate that recent title or meta description updates were effective.

5. Practical Applications of YoY and Trend Analysis

The insights gained from year-over-year comparisons and historical trend analysis allow you to take data-driven actions:

  • Plan Around Seasonal Trends: Recognize predictable peaks and dips to create timely content or adjust your strategy during high-traffic months.
  • Adjust SEO Strategy Based on Long-Term Trends: For keywords showing steady growth or decline, adjust content or keyword targeting to align with search demand.
  • Evaluate SEO Performance Impact: Use YoY comparisons to evaluate whether recent SEO changes have had a positive impact on rankings, CTR, and overall search traffic.

Example: Identifying that your site’s clicks have consistently increased each spring allows you to prepare content and promotional strategies in advance, capitalizing on expected traffic growth.

Summary

Performing year-over-year comparisons and historical trend analysis using Google Search Console data in BigQuery and Looker Studio provides essential insights for long-term SEO planning. By structuring your data in BigQuery and visualizing it in Looker Studio, you can quickly identify seasonal trends, measure the impact of your SEO strategies, and adapt your approach based on historical patterns. This approach to trend analysis ensures that your SEO decisions are data-driven and aligned with both current and anticipated search behaviors.

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