Project Analysis : Week 3, Millenials and Data(MAD)
As part of the 3rd week at MAD(Millenials and Data) bootcamp spearheaded by Chantilly Jaggernauth, the project focused on implementing nuances of dashboard design while enhancing my understanding of the numbers behind the viz.
Without further adieu let’s dive right in.
Webalytics is marketing company who specializes in analyzing web traffic data for clients. Representing KPI’s for a marketing company in a way that facilitates understanding among clients was my main task.
So how did I go about doing it.
Since this is a marketing company, the key metrics for analyzing web data were important for analysis. Vists per day, visits per region, visits per city, visits per source and visits per contet type was what I focused on.
Then, I decided to group them. Visits per city and region could be grouped together under ‘location’. Visits per source and content type could be grouped under ‘types of web traffic’. Visits per day solely represented a KPI that focused on ‘time’.
An overall picture of web traffic data was reprsented using BANS. According to Data School, UK, BANS(Big Ass Numbers) are important figures that give a glimpse to the data reprsented in the dashboard.
No of pageviews, no of downloads and total no of visits are classified under BANS. The only way to maximise impact of these numbers is to make them seriously big and make the text accompnying them small.
To maximize visual impact, I decided to group the bar charts together and give more space to the map as this allowed viewers to zoom in and out of the map with ease without compromising on the data quality.
Now that we have our layout decided, lets get down to understanding chart types.
When it comes to analyzing categorical data, bar charts are the ideal way of reprsenting them.Visits per region, per source and content type are commonly defined by levels of categories. Each categorical value claims one bar, and the length of each bar corresponds to the bar’s value. Bars are plotted on a common baseline to allow for easy comparison of values.
An area chart is a good way to demonstrate trends over time to the viewer, for example showing visits per day over a month. This chart is based on the line chart. The filled area can give a greater sense of the trends in a particular dataset.
Symbol maps show symbols sized and colored according to the data. They work great for data for specific locations (like cities). In this case , visits per city turned out to be ideal for symbol maps. I also incorporated the basic attributes of size and color to the symbols. The bigger the size of the circle, the larger the no of visits to the city and the color red signified cities that received maximum web traffic .
Now that we have our chart types sorted, let’s get down to analysis:
- Webalytics received more than 1 million visits between August and September 2020 . 43% of the visits were from Europe and 22 % of the visits were from Asia. Together these 2 continents accounted for 65% of the web traffic the portal received. London accounted for the
maximum no of visits (45,216) from any city across the world. - Of the visits that accounted for maximum web traffic, 39% come from direct sources. This means that the user arrived directly on to the site and not through any third-party sites.Webalytics must be doing something right either through marketing or anything else to be receiving such heavy traffic. 55% of the visits were related to user content implying that
webalytics must be generating user content deemed necessary or useful. - 18 September 2020, turns out to be the day when there was heavy traffic to the site. More than 100,000 visitors landed on the site that day. Its lowest footfall was on 8 September, 2020 with only 13,695 visitors. This was almost 8 times less than its maximum footfall.
How did you like that? This brings me to the end of the analysis for this week’s project. Please feel free to comment or like the page if you found this useful.
