Web Analysis

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Cashae

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Below is a web analysis for my website CM Marketing and Design.

We used key metrics from our google analytics page which included User Acquisition, Page Views & Unique Page Views, Demographic Details. All of these metrics are important because they help us strategize by helping us engage and keep the right audience.

  • User acuqisiontion – First user source / medium
    • Explain why you use “First user source / medium”
    • Explain why you use the following metrics: Average engagement time, New users, ‘Engaged sessions per active user ‘
    • Add an image (First user source / medium) that you capture from your screen as you can see below

User Acquisition – First user source / medium

  • User Acquisition and first user source/medium is important because this shows use the amount of new users who visit the website through sources.
  • It is important to use the metrics ‘Average engagement time, New users, Engaged sessions per active user.’
    • Average Engagement Time
      • This shows how interested and engaged people and potential employers are with your content you post. A higher average engagement time indicates you are effectively maintaining interest of your target audience.
    • New Users
      • This shows if your strategies like LinkedIn promotions and emails are effective in bringing in new viewers. When there is a steady increase of new users it means your posts are connecting with a wide audience.
    • Engaged Sessions Per Active User
      • This shows the amount of sessions a user is visiting your website. A higher number typically means they visit your website multiple different times.

Using Chat GPT, I ended up using the same prompt for every single one, but I changed a few words to make sure it was with the correct data.

Average Engagement Time Prompt: “When I sorted the average engagement time in descending order in the First user source/medium classification, the following result came out. Please explain. – direct / none 57sec google / cpc 0 sec linkedin.com / referral 24sec”

Result:

The data shows the average engagement time per session for different traffic sources:

  • direct / none (57 sec): Visitors who came directly to the site (e.g., typed the URL or had it bookmarked) spent an average of 57 seconds on the site.
  • google / cpc (0 sec): Visitors who came from Google Ads (CPC) had an average engagement time of 0 seconds, which could indicate that they bounced immediately after landing or the tracking didn’t register properly.
  • linkedin.com / referral (24 sec): Visitors who came from LinkedIn as a referral spent an average of 24 seconds on the site.

The large difference in engagement time suggests that direct visitors are more engaged, while Google Ads traffic and LinkedIn referrals may be less engaging or have tracking issues.

New Users Prompt: “When I sorted the new users time in descending order in the First user source/medium classification, the following result came out. Please explain. direct / none 112 google / cpc 12 linkedin.com / referral 2“

Result:

The data shows the average time spent by new users from different sources:

  • direct / none (112): New visitors who came directly to the site spent an average of 112 seconds, indicating they might be more engaged or have more relevant content.
  • google / cpc (12): New users coming from Google Ads (CPC) spent an average of only 12 seconds, suggesting they may not have found what they expected or bounced quickly.
  • linkedin.com / referral (2): New users coming from LinkedIn spent just 2 seconds on the site, which likely means they either left very quickly or the traffic quality was low.

Overall, direct traffic seems to drive more engaged new users, while Google Ads and LinkedIn referrals have lower engagement times.

Engaged Sessions Per Active User Prompt: When I sorted the engaged session per active user time in descending order in the First user source/medium classification, the following result came out. Please explain. direct / none .92 google / cpc. 0.0 linkedin.com / referral 1.0

Result:

The data shows the engaged session per active user time for different sources:

  • direct / none (0.92): Direct visitors (e.g., typing the URL directly) averaged 0.92 engaged sessions per active user, suggesting a moderately engaged audience.
  • google / cpc (0.0): Users from Google Ads (CPC) had 0 engaged sessions per active user, indicating no meaningful engagement or very high bounce rates.
  • linkedin.com / referral (1.0): LinkedIn referral visitors averaged 1 engaged session per active user, suggesting high engagement, with users returning or interacting meaningfully.

In summary, LinkedIn referrals show the highest engagement, while Google Ads traffic has no engagement, and direct visitors have moderate engagement.

Engagement – Page Title and Screens

  • Page title and screens shows us which posts are the most popular, such as topics and types of content, to help us strategize to make the most appealing content to our viewers.
  • Average Engagement Time
    • This metric is important because it shows the amount of time views are spending on your content.
  • Views
    • Views helps us sees the amount of times a page is viewed.
  • Users
    • This indicates how many individual people visit your content/site. This shows us the diversity of the audience.

Using Chat GPT, I ended up using the same prompt for every single one, but I changed a few words to make sure it was with the correct data.

Average Engagement Time Prompt: When I sorted the average engagement time in descending order in the page title and screen class classification, the following result came out. Please explain. Priefert SEO Practice – CM Marketing and Design 3m 22s / The Boutique Hub – Marketing Content – CM Marketing and Design 2m 22s / Priefert – Marketing Content – CM Marketing and Design 2m 14s/ Review Analysis – The Boutique Hub – CM Marketing and Design 1m 12s/ Priefert – CM Marketing and Design 45s 

Result:

The data shows the average engagement time for different pages:

  • Priefert SEO Practice – CM Marketing and Design (3m 22s): This page had the highest engagement, suggesting it provided the most engaging or relevant content to visitors.
  • The Boutique Hub – Marketing Content – CM Marketing and Design (2m 22s): This page also had good engagement, but slightly lower than the Priefert SEO page.
  • Priefert – Marketing Content – CM Marketing and Design (2m 14s): Visitors spent a bit less time here, indicating it might be somewhat less engaging than the others.
  • Review Analysis – The Boutique Hub – CM Marketing and Design (1m 12s): This page had a moderate level of engagement.
  • Priefert – CM Marketing and Design (45s): This page had the lowest engagement time, possibly indicating that visitors quickly left or didn’t find the content compelling.

In summary, the Priefert SEO and Boutique Hub pages are the most engaging, while other pages have less interaction.

Views Prompt: When I sorted the views in descending order in the page title and screen class classification, the following result came out. Please explain. Home – CM Marketing and Design 177 / Blog – CM Marketing and Design 67 / About – CM Marketing and Design 28 / Contact – CM Marketing and Design 28 / Priefert – CM Marketing and Design 27 

Result:

The data shows the number of views for different pages:

  • Home – CM Marketing and Design (177 views): The homepage has the highest number of views, which is typical as it serves as the main entry point for most visitors.
  • Blog – CM Marketing and Design (67 views): The blog page also gets a significant number of views, suggesting good traffic to content or articles on the site.
  • About – CM Marketing and Design (28 views): The About page has fewer views, indicating it may be less frequently visited or considered less essential by users.
  • Contact – CM Marketing and Design (28 views): Similar to the About page, the Contact page also has relatively low views, possibly because users don’t often visit this page unless they need to reach out.
  • Priefert – CM Marketing and Design (27 views): This page receives the lowest number of views, suggesting that it might be more specific or less visited than other pages.

In summary, the homepage and blog pages drive the most traffic, while other pages like About, Contact, and Priefert have lower view counts.

Users Prompt: When I sorted the users in descending order in the page title and screen class classification, the following result came out. Please explain. Home – CM Marketing and Design 95 / Contact – CM Marketing and Design 23 / About – CM Marketing and Design 21 / Blog – CM Marketing and Design 12 / The Boutique Hub SEO Practice – CM Marketing and Design 12

Result:

The data shows the number of users for different pages:

  • Home – CM Marketing and Design (95 users): The homepage had the highest number of users, indicating it’s the main entry point for visitors.
  • Contact – CM Marketing and Design (23 users): The Contact page had a decent number of users, suggesting that many visitors are interested in reaching out.
  • About – CM Marketing and Design (21 users): The About page had fewer users, which is typical as it may be of less immediate interest to visitors.
  • Blog – CM Marketing and Design (12 users): The Blog page attracted a relatively smaller group of users, indicating niche interest or less frequent visits.
  • The Boutique Hub SEO Practice – CM Marketing and Design (12 users): This specific page also attracted a smaller number of users, suggesting it may be more specialized content.

In summary, the Home page leads in user traffic, with Contact and About pages having moderate interest, while Blogand the SEO Practice pages have lower user counts.

  • User attribute – Demographic details
  • It is important to use “Country and City” metrics because it helps us to figure out particular locations and shows us how effective our content is to attract target areas.
  • Explain why you use the following metrics: Users, Engagement rate, Average engagement time, Engaged session per user.
    • Users
      • Shows the number of unique visitors and shows us if we are reaching our specified audience.
    • Engagement Rate
      • This is the percentage of users who are active, such as clicking, scrolling, etc.
    • Average Engagement Time
      • This metric shows the amount of time users are spending interacting with your content.
    • Engaged Sessions Per User
      • Shows us how much users in an area are interacting frequently with your content.

Using Chat GPT, I ended up using the same prompt for every single one, but I changed a few words to make sure it was with the correct data. I ended up picking two cities that could potentially work for the companies as I was not able to find any that were in the exact location of each companies headquarters. I ended up choosing San Antonio, since it is located in Texas. Then I chose Spearfish, because there was quite a bit of engagement, and The Boutique Hub mainly does remote work. My goal was to choose the two best options for the cities in my opinion.

Users Prompt: When I sorted the users in descending order in the country and city classification, the following result came out. Please explain. San Antonio 9 / Spearfish 9 

Result:

The data shows that San Antonio and Spearfish each had 9 users. This indicates that both cities are tied for the same number of users visiting the site. It suggests that these two locations are equally represented in terms of traffic, at least for this particular dataset.

Engagement Rate Prompt: When I sorted the engagement rate in descending order in the country and city classification, the following result came out. Please explain. San Antonio 33.33% / Spearfish 43.33%

Result:

The data shows the engagement rates for San Antonio and Spearfish:

  • San Antonio (33.33%): The engagement rate for users from San Antonio is 33.33%, indicating that about one-third of the visitors from this city interacted meaningfully with the site.
  • Spearfish (43.33%): The engagement rate for users from Spearfish is 43.33%, which is higher than San Antonio, suggesting that users from Spearfish are more engaged or spend more time on the site.

In summary, Spearfish has a higher engagement rate than San Antonio, meaning users from Spearfish are more likely to interact with the content.

Average Engagement Time Prompt: When I sorted the average engagement time in descending order in the country and city classification, the following result came out. Please explain. San Antonio 11sec / Spearfish 2m 42s

Result:

The data shows the average engagement time for users from San Antonio and Spearfish:

  • San Antonio (11 seconds): Users from San Antonio spend a very short amount of time on the site, which suggests low engagement or a high bounce rate.
  • Spearfish (2 minutes 42 seconds): Users from Spearfish spend significantly more time on the site, indicating higher engagement or more meaningful interactions with the content.

In summary, Spearfish shows much higher engagement compared to San Antonio, where users quickly leave the site.

Engaged Sessions Per User Prompt: When I sorted the engaged sessions per user in descending order in the country and city classification, the following result came out. Please explain. San Antonio .33 / Spearfish 1.44

Result:

The data shows the engaged sessions per user for users from San Antonio and Spearfish:

  • San Antonio (0.33 engaged sessions per user): Users from San Antonio are engaging with the site less frequently, averaging only about one-third of a session per user.
  • Spearfish (1.44 engaged sessions per user): Users from Spearfish are more engaged, averaging 1.44 sessions per user, indicating they return or interact with the site more often.

In summary, Spearfish has higher engagement, with users returning or interacting more frequently than those from San Antonio.

Posted by

Cashae

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