Custom SEO Metrics Calculation

Calculating Custom SEO Metrics

To gain deeper insights into SEO performance, it’s often helpful to go beyond the default metrics in Google Search Console. In this section, we’ll cover how to calculate custom SEO metrics in BigQuery to create meaningful, tailored KPIs (Key Performance Indicators) for your specific SEO goals.

1. Why Calculate Custom SEO Metrics?

Standard metrics like clicks, impressions, and average position offer essential information, but they don’t always give you the complete picture. By calculating custom SEO metrics, you can:

Measure the effectiveness of specific keywords, pages, or campaigns more accurately.
Track metrics that align with business objectives, such as engagement or conversion rates.
Build actionable insights into your SEO performance, providing a more precise understanding of what drives results.

These tailored insights help refine your SEO strategy and make data-driven decisions.

2. Common Custom SEO Metrics and Their Purpose

Here are some custom metrics commonly used in SEO analysis and why they’re valuable:

Click-Through Rate (CTR): Measures how often users click on your listing after seeing it in search results.
Average Position Change: Tracks changes in keyword ranking over time to understand whether recent efforts are improving visibility.
Engagement Rate (if connected with Google Analytics data): Measures user engagement on landing pages from search results, such as bounce rate or time on page.
Conversion Rate (if connected with goal tracking data): Tracks the percentage of search traffic that results in conversions.

3. Step-by-Step Guide to Calculating Custom SEO Metrics in BigQuery

To calculate these custom metrics, you’ll need to use SQL queries that aggregate and manipulate your data in BigQuery.

Step 1: Calculate Click-Through Rate (CTR)
- CTR is the ratio of clicks to impressions. This metric helps you understand which keywords or pages are performing well and drawing in users.
```
SELECT
  query,
  SUM(clicks) AS total_clicks,
  SUM(impressions) AS total_impressions,
  (SUM(clicks) / SUM(impressions)) * 100 AS ctr_percentage
FROM
  `your_project.your_dataset.search_data`
GROUP BY
  query
ORDER BY
  ctr_percentage DESC;
```
- This query calculates the CTR as a percentage for each query, helping you identify keywords that generate high engagement.

Step 2: Calculate Average Position Change

Tracking changes in average position over time can reveal whether your SEO efforts are helping your keywords rank higher.

SELECT
  query,
  AVG(position) AS avg_position_current,
  LAG(AVG(position), 1) OVER (PARTITION BY query ORDER BY date) AS avg_position_previous,
  (LAG(AVG(position), 1) OVER (PARTITION BY query ORDER BY date) - AVG(position)) AS position_change
FROM
  `your_project.your_dataset.search_data`
WHERE
  date BETWEEN '2024-10-01' AND '2024-10-31'  -- Replace with your date range
GROUP BY
  query, date
ORDER BY
  query, date;

This query calculates the difference in position between current and previous dates, allowing you to monitor ranking trends.

Step 3: Calculate Custom Engagement Metrics (If Linked with Analytics Data)
- If your BigQuery setup includes data from Google Analytics, you can calculate metrics like bounce rate for SEO traffic.
```
SELECT
  landing_page,
  SUM(sessions) AS total_sessions,
  SUM(bounces) AS total_bounces,
  (SUM(bounces) / SUM(sessions)) * 100 AS bounce_rate
FROM
  `your_project.your_dataset.analytics_data`
GROUP BY
  landing_page
ORDER BY
  bounce_rate ASC;
```
- This query provides bounce rates for each landing page, helping you evaluate user engagement from organic traffic.
Step 4: Calculate Conversion Rate (If Linked with Goal Data)
- Conversion rate is a valuable metric for tracking how much SEO traffic converts into leads, sales, or other goals.
```
SELECT
  query,
  SUM(clicks) AS total_clicks,
  SUM(conversions) AS total_conversions,
  (SUM(conversions) / SUM(clicks)) * 100 AS conversion_rate
FROM
  `your_project.your_dataset.search_data_with_goals`
GROUP BY
  query
ORDER BY
  conversion_rate DESC;
```
- This query calculates the conversion rate, giving insight into how well specific keywords drive meaningful actions.

4. Visualizing Custom Metrics in Looker Studio

Once your custom metrics are set up in BigQuery, connect them to Looker Studio to create clear, actionable visualizations.

Step 1: Connect Looker Studio to Your BigQuery Data
- In Looker Studio, add BigQuery as your data source, linking it to the custom metrics you created.
Step 2: Create Custom KPI Visualizations
- Use Looker Studio charts and graphs to visualize these metrics. For example:
  - A bar chart for conversion rate by query shows which keywords drive the most conversions.
  - A line graph for average position change over time reveals the impact of recent SEO efforts on rankings.
Step 3: Add Comparisons for Deeper Analysis
- Compare CTR, position changes, or conversion rates across different date ranges to track improvement and performance.

Summary

Calculating custom SEO metrics with SQL in BigQuery allows you to tailor KPIs to your SEO goals. By tracking metrics like CTR, average position change, engagement rates, and conversion rates, you gain a deeper understanding of how SEO efforts translate to user engagement and conversions. With Looker Studio, you can bring these insights to life, creating visualizations that support strategic, data-driven SEO decisions.

Published 11 Nov 2024