Digital Marketing Key Metrics and Analytics

This week, I will go over how many site visits my website received that focused on Girlfriend Original Promotions and Focus Marketing Group, with a small adjustment made with Sullivan Supply Inc. over this past semester. I will break down its purpose and function with the help of ChatGPT and Google Analytics.

Key metrics introduction (User acquisition Page Views & Unique Page Views, Demographic details)

When analyzing the success and performance of a website or digital platform, key metrics such as User Acquisition, Page Views and Unique Page Views, and Demographic Details play a crucial role. Here’s a breakdown of what these metrics mean and their importance:

1. User Acquisition

This metric tracks how visitors discover your website or platform. It includes insights into:

• Channels: Organic search, social media, email marketing, paid ads, or direct traffic.
• Campaigns: Effectiveness of specific marketing efforts.
• Referral Sources: Websites or sources driving visitors to your platform.

Why It Matters:

Knowing user acquisition helps identify which channels and strategies are most useful in driving traffic, enabling optimization for better reach and conversion rates.

2. Page Views and Unique Page Views

• Page Views: Total number of times any page on your website has been viewed. This includes repeated views by the same user.
• Unique Page Views: The number of sessions in which a page was viewed at least once. Multiple views by the same user in the same session count as one unique view.

Why It Matters:

These metrics reveal the demand for content and help assess user engagement. A high number of views indicates interest, while unique views highlight how many distinct sessions engage with your content.

3. Demographic Details

This includes data about the audience’s age, gender, location, interests, and device preferences. Typically gathered through tools like Google Analytics or Meta Insights, it offers a close-up of who your users are.

Why It Matters:

Demographic data helps adapt content, design, and marketing efforts to appeal to your audience’s preferences, improving user experience and engagement.

User Acquisition- First User Source/Medium

For this set of data I used first user source/medium report navigation because it allows detailed information about where users are coming. This report navigation will be used for the three following data analyses.

First I focused on the average engagement time. Identifying the average engagement time is important because it directly reflects how interested and engaged people are with the content presented. A higher average engagement time suggests that the website content is capturing and holding the attention of the target audience. Here is the prompt I used to help refine my data- “When I sorted the average engagement time in descending order in the First user source/medium classification, the following result came out. Please explain” as well as inputting the corresponding data.

Secondly, I focused on new users. Distinguishing the new users shows how effective the strategies being used are in attracting new visitors to your site. A steady increase in new users indicates that the content is reaching a wider audience. I then asked the opinion of my new users by giving Chat GPT this prompt- “When I sorted the new users in descending order in the First user source/medium classification, the following result came out. Please explain.” as well as inputting the corresponding data.

Lastly, I focused on engaged sessions per active user. Identifying the engaged sessions per active users reflects how many sessions per user meet the “engaged session” criteria. A high engaged session per active user in GA4 means that users who actively visit your website tend to engage in multiple session, in other words these users are highly engaged and frequently return to interact with content on your website. To simplify these metrics display strong user interest and loyalty. I then asked the opinion of my engaged sessions per active users by giving Chat GPT this prompt- “When I sorted the engaged sessions per active users in descending order in the First user source/medium classification, the following result came out. Please explain.” as well as inputting the correct data.

Engagement- Page Title and Screens

For this set of data I used page title and screen report navigation because it identifies specific blog posts or pages that are getting the most engagement. This report navigation will be used for the three following data analyses.

First. I focused on average engagement time per active user. Identifying the average engagement time is important because it shows how long users are spending on the content. A higher average engagement time suggests that the content is captivating and relevant, which is essential for making a positive impact on users. Here is the prompt I used to help refine my data. “When I sorted the average engagement time in descending order in the page title and screens classification, the following result came out. Please explain.” as well as inputting the corresponding data.

Secondly, I focused on views. Identifying the views is important because it indicates the reach of the content. A high number of views suggests that the content is being discovered and consumed, which is crucial for increasing visibility among users. The views also reflect the effectiveness of the promotion strategies, including the use of LinkedIn and email marketing. Here is the prompt I used to simplify my data- “When I sorted the views in descending order in the page title and screens classification, the following result came out. Please explain.” as well as inputting the corresponding data.

Lastly, I focused on active users. Distinguishing the active users is important because it shows how many different individuals are accessing the content. This is important because it can indicate the diversity of the audience. Here is the prompt I used to simplify my data- “When I sorted the active users in descending order in the page title and screens classification, the following result came out. Please explain.” as well as inputting the corresponding data.

Demographic Details- Country and City

For this set of data, I used country and city report navigation because it aligns to target specific companies in particular locations. This report navigation will be used for the three following data analyses.

For this section of data analysis, I selected five cities where I would have companies that I would want to work for. Although those cities may not be my top choices and very easily readable, they seemed fitting based upon the cities that showed in my data. The five cities I selected were Des Moines, Rapid City, Dallas, Spearfish, and Omaha. These five cities will be used for each section that is analyzed.

First, I focused on active users. Identifying the active users is important because if shows the total number of unique visitors from each location. It directly indicates how well the target audience is being reached in specific areas. Here is the prompt I used to help refine my data- “When I sorted active users in descending order in the country and city classification, the following result came out. Please explain” as well as inputting the corresponding data.

Secondly, I focused on the engagement rate. Distinguishing the engagement rate is important because it shows what percentages of users from each location are actively engaging with my content. Here is the prompt I used to help refine my data- “When I sorted engagement rate in descending order in the country and city classification, the following result came out. Please explain” as well as inputting the corresponding data.

Thirdly, I focused on the average engagement time per active user. Identifying the average engagement rate is important because it tells you how much time users from different locations are spending actively interacting with content. This metric reflects the quality of engagement and helps gauge how compelling your content is for users in each area. Here is the prompt I used to help refine my data- “When I sorted average engagement time per active user in descending order in the country and city classification, the following result came out. Please explain” as well as inputting the corresponding data.

Lastly, I focused on the engaged sessions per active user. Identifying the engaged sessions per active user is important because this metric shows how frequently users from each location engage in meaningful sessions. It provides insight into loyalty and ongoing interest, helping to identify regions where users are most actively interacting with my content. Here is the prompt I used to help refine my data- “When I sorted engaged sessions per active users in descending order in the country and city classification, the following result came out. Please explain,” as well as inputting the corresponding data.

This is the conclusion that Chat GPT came to after analyzing all of the demographic details as well as the recommendations that it had.

By focusing on key metrics such as user acquisition, page views, demographic details, and engagement rates, valuable insights can be acquired to take into consideration the effectiveness of your strategies. When regular reviews of the metrics mentioned above are done, an effective adjustment of strategies can be completed.