Development of an Appropriate Time-series Forecasting Model for SEO Data
- Search Engine Opt imisation, also known as SEO, is one of the online marketing channels t hat, when it is s et u p s uitably, it could continue to pay dividends over time without investment. Recently, SEO teams of some companies keep investigating historical data to predict the future trends of revenue associated with the number of clicks, number of imp ressions, and number of sear ches which is prop osed to help with companies’ quality planning and campaign investment. Some of the challenges experienced by SEO analysts when attempting to forecast the revenue is that there is currently no way to standardize or forecast customers’ behaviour, which means the trends could be different every day, month, and year. In this research, SEO traffic data from one of the online travel a gencies are collected for the purpose of data exploration, analysis and forecasting which are expected to bring business values and give some be neficialinsights. Moreover, different time-series forecasting models are selected to conduct experiments seeking the best fit model for SEO data; Autoregressive Integrated Moving Average (ARIMA) model is initially performed, followed by Long-Short Term Memory (LSTM) of Recurrent Neural Networks (RNNs). As a result, it is proved that ARIMA is yet a classical statistics model but powerful enough for such small-size data, albeit the data is non-stationary and has too much white noise. Me anwhile, the LSTM is a deep learning tool which could deal with different types of data, but still need to be applied with a larger size of data to prove its competence.