Traffic in Travel Agency Website

Part 1. Background and the business problem ------------------------------ Page

Part 2. Objectives --------------------------------------------------------------- Page

Part 3. Data collection ---------------------------------------------------------- Page

Part 4. Variables and definitions ----------------------------------------------- Page

Part 5. Visualizing the data ----------------------------------------------------- Page

Part 6. Relationship between variables --------------------------------------- Page

Part 7. Growth forecasts ------------------------------------------------------- Page

Part 8. Suggestions and recommendations ---------------------------------- Page

Visualizing the data

With the data collected in csv format on the click patterns of customers in the website. The data is visualized by plotting to different graphs. Visualizing the data aids on observing easily whether there is a pattern in the data for further investigation.

We would plot graphs on the click counts and search counts based on below to locate if any visual patterns could be located.

Search Count and Click Count per Day

In the graph above, the total click counts and search counts per day are plotted for the past two years.

The lines seemingly have a slight upward trend. With search counts growing higher that click counts recently as the gap between the lines widened

There are zig zag shapes in both lines, there might be seasonality based on weeks or months on the traffic

It is likely that the business as a whole is growing, especially in the recent months. As there has been increased click and search counts, meaning more traffic entering the site.

Further plot on the graph based on hour, week and month would be performed, as we would like to observe if there might be time dependent variables affecting the traffic amount and quality.

Search Count and Click Count Per Month

The above graph shows the total of search count and click count by month.

The highest traffic (both search and click count) is observed on March, followed by January and February. This indicates people check on flight information at those months the most.

For the high traffic in early years, it may be because people plan their trips close to the beginning of the years, while the exceptional high count in March may be due to Easter holiday season.

Interestingly, other months before holidays, such as November did not reveal a significant higher count according to the plot.

Search Count and Click Count By Day

The above graph shows the total of search count and click count by the day in a week.

The highest traffic (both search and click count) is observed on Tuesdays, while weekends (Fridays to Sundays) have the lowest traffic. This indicates people actually checks flight more during weekdays than weekends

A possible reason is due to people