Long Tail Prediction for SEO
In this hands-on project, we will train time-series prediction models to predict future long tail trends. Long-tail keywords are search terms that have a lower search volume and competition rate than short-tail keywords.
We will be using Prophet, a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
Why would an SEO be interested in this subject?
Long-tail keywords are a key to SEO, as they often bring in more traffic together than any top, highly competitive keyword. However, as they depend on such a small number of searches, they can be difficult to predict and plan for.
But several reasons can lead the head of a company, the head of a department, and many other decision-makers to ask for SEO traffic projections.
- To be certain of the investment. (SEO is first and foremost an investment as a marketing channel.)
- To balance expenses between the SEO budget and the investment in paid search (Google Ads, Shopping, etc.).
- Identify current long-tail queries for your site using GSC data
- Detect future long-tail trends based on your GSC data
- Understand the theory and intuition behind the Facebook Prophet time series prediction tool
- Build a predictive model to forecast future Google hits
- Assess trained model performance
- Import Libraries and data-sets
- Connect Google Search Console
- How to identify your long tail keyword traffic
- Prepare the data for Facebook Prophet
- Develop forecasting model and make predictions