How can you include external factors in your SEO data model?

1 What is SEO data modeling?

SEO data modeling is the process of creating a mathematical representation of your SEO performance, based on historical data, metrics, and variables. SEO data modeling can help you identify patterns, trends, and correlations, as well as test hypotheses, measure outcomes, and forecast future scenarios. SEO data modeling can also help you evaluate the impact of your SEO actions and optimize your budget allocation and resource management.

2 Why include external factors in your SEO data model?

External factors, such as competitor actions, market trends, seasonality, and events, are outside of your direct control but can influence your SEO performance. These external factors can have positive or negative effects on your SEO performance depending on how they align with your website's goals, value proposition, and target audience. By including external factors in your SEO data model, you can gain insight into their impact on your SEO performance, adjust your SEO strategy and tactics accordingly, and improve forecasting accuracy and reliability by accounting for potential changes and uncertainties.

3 How to identify external factors for your SEO data model?

In order to include external factors in your SEO data model, the first step is to identify the relevant factors for your website and industry. You can use various sources and methods to find external factors, such as keyword research tools like Google Keyword Planner, SEMrush, or Ahrefs, competitive analysis tools like SimilarWeb, Moz, or SpyFu, market research tools such as Google Trends, Statista, or Pew Research Center, and event tracking tools such as Google Alerts, BuzzSumo, or Mention. Additionally, you can use your own analytics data like Google Analytics or Google Search Console to analyze your website's performance and identify any anomalies or changes that could be attributed to external factors.

4 How to include external factors in your SEO data model?

Including external factors in your SEO data model is the next step. Depending on the complexity of your data model, you can add external factors in various ways, such as independent variables or predictors in regression models, modifiers or multipliers in growth models, and scenarios or assumptions in simulation models. To do so, you can use tools like Excel, Google Sheets, Python, R, or SQL. Specialized SEO data modeling and forecasting tools like SEO Monitor, BrightEdge, or Conductor can also help automate and simplify the process.

5 How to evaluate and update your SEO data model with external factors?

In order to incorporate external factors in your SEO data model, you should evaluate and update it regularly. You can use metrics such as R-squared, MAE, and MAPE to measure the accuracy of your data model. Additionally, you can compare your data model with other models or benchmarks, like historical performance or industry averages, in order to assess its accuracy and relevance. It's also important to update your data model with new data and external factors on a periodic basis. Furthermore, you should review and revise your data model assumptions and parameters to make sure they are valid and in line with your SEO objectives and strategy.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?