Blog's Analytics

-


For the next analytics analysis, we are considering a time frame between January 20th and February 23rd. 

In the audience section, we can see that 47 users had a total of 109 sessions during this time frame, each user got an average of 2.32 sessions, viewing 257 pages, 2.36 pages per session each user, on an average session duration of 1:38 minutes. 

 January 20th was the day with more active users during the overall time frame with 10 users active on the website. 

The main language people who come to my blog speak in English. 


Most of the people come from Ireland, being 93.75% of users, followed by India with 4.17% and France with 2.08%. 


82.98% of the users used Chrome as their browser. 


80.95% of my audience used Android as their operating system, whereas just 19.05% of users used iOS. 


The new visitors corresponds to 60.26% of the total users, whereas the returning visitor represents 39.74% with a bounce rate of 43.12%. 

The engagement is measured in the number of seconds per session and the pages view in each session. We can see that just 1 session took more than 1801 seconds viewing 6 pages, whereas most of the sessions, 63, the content was scanned very rapidly between 0 to 10 seconds, viewing 80 pages in this process. 


The top sources where the traffic was before leading to my blog are blogger.com; 35.5%, elearning.dbs.ie platform; 14.5%, accounts google; 1.3%, and direct; 48.7%. The actions that might lead to these numbers were commenting on other people's blogs and sharing my blog posts links in our classroom's WhatsApp group. 


In the behavior section, we can see that the most view page was the main page of my blog with 31.91%, 82 pageviews, followed by "AI for Marketing" and "Benefits and Challenges using Customer Data for Marketing" with 17.51% and 45 pageviews in both post, in third place are the posts "Big Data" and "3V's of Big Data" with 13.23% and 34 pageviews, at last, is the post "Value in Big Data for Marketing" with 6.61% and 17 pageviews. 

Comments

Post a Comment

Popular posts from this blog

Benefits and Challenges of using Customer Data for Marketing

-
Customer data is defined as the information your customers provide while interacting with your business via your website, mobile applications, surveys, social media, marketing campaigns, and other online and offline avenues. (Deshpande, 2019).  Years ago, marketers would just target mass audiences with advertisements all around the city or on the TV. Of course, this represents big expenditures on marketing and very poor KPI metrics to monitor the effectiveness of the marketing campaigns.  Nowadays, marketers have access to a big amount of customer data but, why is customer data important for marketing? One of the benefits of customer data is that, and thanks to a single customer view database, we can know better target our customers. Segmenting customers is not only good for ensuring that the correct person receives the most appropriate message, but it is also far more cost-effective than a "one message fits all" approach to direct marketing. (Botibol, 2018). Hence, our...
Read more

AI for Marketing

-
Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. (Rousse, 2019).  Nowadays, AI is used in many fields, such as healthcare, education, finance, law, and manufacturing, but we are going to focus on how Marketing is using AI.  AI in marketing is the use of customer data, machine learning, and other computational concepts to predict a person's action or inaction. (Grimms, 2019).  It is important for digital marketers because artificial intelligence will allow analyzing big amounts of data from different platforms, such as social media platforms, emails and web activity in a very short period of time, thus, enhancing campaign performance and overall return on investment (ROI).  According to Dave Chaffey, AI is broken down into 3 different types of technology; machine learning; which uses algorithms to learn from historical data sets that can create propensity models, applied propensity models;...
Read more