Chingwen Chen

GA4 Return Visit Rate Report

Return visitor count, return visitor retention rate, duration of return visit engagement.

Trend in retention rate changes between new users vs. returning visitors.

Trend in engagement time changes between new users and returning visitors

Google Merchant Demo Account

demo data

How to Use the Google Merchant Demo Account with Demo Data

• 3- Acquiring [Data Sources]
• Please go to a search engine and enter: Google Analytics Demo Account

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How to Use the Google Merchant Demo Account Data

• 4- Data download URL:
• https://support.google.com/analytics/answer/6367342?hl=zh-Hant#zippy=%2C%E6%9C%AC%E6%96%87%E5%85%A7%E5%AE%B9

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• The demonstration account contains three resources.
• Google Analytics 4 Resources: Google Merchandise Store (Website Data)
• Google Analytics 4 Resources: Flood-It! (App and Website Data)

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Download

The first data resource hyperlink

Google Merchant Demo Account

【Data Download Method】

• Click on the first data resource hyperlink.
• Automatically download data to your GA4 account.

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【Method for Downloading Data】

• Click on the first data resource hyperlink ➜ Automatically download data to your GA4 account.

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GA4 Returning Visitor Report

Testing Google's e-commerce website

• Google Merchandise Store (URL)
• https://shop.merch.google/

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Essential Basic Data-1

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How many returning visitors has in the past 30 days?

How many returning visitors has in the past 30 days?

• Life Cycle ➜ Return Visit Rate ➜ Return Visitors: 11,000
• Please select the date: Choose the past 30 days.

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Essential Basic Data-2

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Daily "return retention rate" for new users 42 days ago

Daily [return retention rate] for new users from 2 to 42 days ago.

• (1). Life Cycle ➜ Return Rate ➜ 【User Retention Rate】 ➜ Day 1 at 100%, Day 3 return rate is 0.8%

Daily "return retention rate" for new users 42 days ago

• User Retention
• The metric for measuring whether users who visited the website during a certain period return in a subsequent period.
• Purpose:
• ✅ Understand whether your website can attract user revisits.
• ✅ Analyze whether users return to the service after X days, X weeks, or X months.
• ✅ Identify which traffic sources bring users with high retention rates, helping you optimize your marketing strategy.

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Example: Daily [return visit retention rate]

• Example 1: E-commerce website
• Assuming you operate an online store and want to understand the return visit status of new users.

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• Most people do not return after 30 days (only 5% remain), which may require:
• Use email marketing to remind users to revisit.
• 2. Offer time-limited promotions to attract repeat purchases.
• 3. Use Facebook/Google Retargeting Ads

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Essential Basic Data-4

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How to Improve "User Retention Rate"?

4- How to Improve "User Retention Rate"?

• 1. Optimize website experience
• ✅ Improve website speed and reduce loading time.
• ✅ Add "User Personalized Content" to increase user engagement.
• ✅ Make it "easier for users to find important information," reducing the bounce rate.
• 2. Retargeting Ads
• ✅ Use Google Ads/Facebook Ads to target users who have visited in the past 30 days but have not converted, and deploy retargeting ads.
• ✅ Send promotional notifications (such as Email marketing) to users who have browsed products but did not make a purchase.

How to Improve "User Retention Rate"?

• 3. Offer limited-time promotions
• ✅ Offer discount codes to returning users to enhance their purchasing motivation.
• ✅ Offer exclusive discounts to existing customers to enhance loyalty.
• 4. Using Email Marketing
• Send a reminder email to users who have not revisited in the past 7 days.
• ✅ Offer a VIP rewards program for existing customers to encourage repeat purchases.

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Essential Data Basics-5

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Daily "Return Participation Time" for new users 42 days ago

Daily "Return Participation Time" for new users who started within the last 5-42 days

• (1). Life Cycle ➜ Return Rate ➜ [User Return Engagement Time] ➜ The engagement time on the 16th day of return is 2 minutes and 23 seconds.

Essential Basic Data-6

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Four Metrics for Assessing "User Engagement"

6 - Evaluation of 4 Indicators of [User Engagement]

• Primary measures of user engagement:
• 1.Number of Engaged Sessions
• 2. Engagement Time
• 3. Engagement Rate
• 4. Returning Users

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�6 - Assessing the 4 Indicators of [User Engagement]

• 請提供具體的中文內容，以便我能夠進行準確的翻譯。

Engagement Time

• Definition: User total time spent on a website or App (excluding idle time).
• For example:
• After entering the website, Xiao Ming reads an article for 5 minutes, and this duration is counted as part of his participation time.
• After Xiaohua opened the website and did not interact with it, she closed the page after 10 minutes. GA4 will only calculate the actual time she interacted with the site (for example, 2 minutes) and will not include the 8 minutes of idle time.
• Purpose:
• This data can help analyze the "actual stay time of users on the website."
• More accurate than the traditional "stay time."

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2. Engaged Session

• Definition: In GA4, a user session is counted as an "engaged session" if it meets any of the following conditions:
• Stay for ≥ 10 seconds
• Browse more than 2 pages.
• Trigger at least one conversion event.
• For example:
• Xiaoming visited a website and stayed for 12 seconds; this visit will be counted as an interactive session.
• After entering the site, Xiao Hua left within 5 seconds; this visit will not be counted as an interactive session.
• Purpose:
• This data aids in analyzing "valuable work sessions" rather than merely counting visits.

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3. Engagement Rate

• Definition: A measure of how many work stages are "interactive," calculated as follows:

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The higher the engagement rate, the more it indicates that the website's user experience and content are more appealing and attractive.

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4. Returning Users

• Definition: A user who has previously visited the website and returns for another visit.
• Example:
• On October 1st, when Xiaoming first visited the website, GA4 recorded him as a "New User."
• On October 3rd, Xiao Ming returned to browse the website, and this time GA4 will categorize him as a "Returning User."
• Purpose:
• This indicator can be used to measure the "stickiness" of a website.
• If the ratio of returning users is low, it indicates that users do not come back after their initial visit. This may require optimizing the content or using email marketing to increase the return visit rate.

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Essential Data Basics - 7

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How to Enhance "User Engagement"?

7- How to Enhance "User Engagement"?

• Enhance the appeal of website content
• ✅ Use videos, images, and dynamic content to enhance interaction.
• ✅ Reduce unnecessary text to enhance readability.
• ✅ Optimize titles and descriptions to increase user interest in clicking.
• 2. Optimize website user experience (UX/UI)
• ✅ Ensure the website loads quickly (target <3 seconds)
• ✅ Make important buttons and CTAs (Call to Action) clearly clickable.
• ✅ Use "related recommended content" to increase page dwell time.

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7- How to Enhance "User Engagement"?

• 3. Use Email / Push Notifications to Increase Return Visits
• ✅ Send reminder emails to users who have not revisited within 7 days.
• ✅ Offer a limited-time discount to users who have visited in the past 30 days but have not converted.
• 4. Use retargeting ads to re-engage low-interaction users.
• ✅ Target Facebook/Google Ads at users with short session durations but no conversions.
• ✅ Offer personalized promotions to attract them to return and complete a conversion.

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Essential Data Basics-8

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"User Retention Trends by Similar Groupings," comparing the retention rate trends of new users versus returning visitors.

8-【User Retention Trends by Cohort】�Compare the retention rate trends between new users and returning visitors.

• (1). Life Cycle ➜ Revisit Rate ➜ [User Retention Trend]
• Blue line ➜ Day-1 retention rate for new users is 4.1%.
• Purple line ➜ Retention rate of returning old users ➜ 1.1% of old users revisit on the 7th day after their previous visit.

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8-【User Retention Trends by Cohort】�Compare the retention rate trends between new users and returning visitors.

• (1). Life Cycle ➜ Revisit Rate ➜ [User Retention Trend]
• Blue line ➜ Day-1 retention rate for new users is 4.1%.
• Purple line ➜ Retention rate of returning old users ➜ 1.1% of old users revisit on the 7th day after their previous visit.

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8-【User Retention Trends by Cohort】�Compare the retention rate trends between new users and returning visitors.

• (1). Life Cycle ➜ Revisit Rate ➜ [User Retention Trend]
• Blue line ➜ Day-1 retention rate for new users is 4.1%.
• Purple line ➜ Retention rate of returning old users ➜ 1.1% of old users revisit on the 7th day after their previous visit.

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8-1. Blue curve: New User Retention Rate

• Definition:
• The proportion of new users acquired during a certain period who return in subsequent periods.
• Typically, this curve will gradually decline, as most new users do not return after their first visit.
• Example:
• On October 1st, your website added 1,000 new users.
• On October 2nd, 300 people revisited, so the Day 1 retention rate = 30%.
• On October 7th, only 100 people revisited, so the Day 7 retention rate = 10%.
• On October 30th, there may only be 50 people revisiting, so the Day 30 retention rate = 5%.

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8-1. Blue curve: New User Retention Rate

• Representative Meaning: If the blue curve declines too rapidly (for example, falling below 10% within 3 days), it may indicate:
• ✅ The website content lacks appeal to new visitors, resulting in users leaving after a single visit.
• ✅ The traffic for the marketing campaign is not precise enough; users find that it does not meet their needs after visiting the site.
• How to Optimize Retention Rates:
• ✅ Use email marketing to remind new users to revisit.
• ✅ Offer coupons or personalized recommendations to new visitors within one week.
• ✅ Use retargeting ads to re-engage users who have drifted away.

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8-2. Purple Curve: Returning User Retention Rate

• Definition:
• This curve represents the proportion of "return visits by former users who have visited the website before" over subsequent periods of time.
• The curve usually declines slowly because the stickiness of existing users is high.
• Please provide the content you would like translated into English.
• 5,000 users visited the website in September.
• In the first week of October, 1,500 people revisited, so the weekly retention rate = 30%.
• In the fourth week of October, 1,000 people revisited, so the monthly retention rate = 20%.

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8-2. Purple Curve: Returning User Retention Rate

• Representative Meaning:
• If the purple curve is above the blue curve, it indicates that your existing customers are more likely to return than new visitors, which is a normal scenario.
• If the purple curve declines too rapidly (for example, falling below 10% after one week), it may indicate:
• ✅ Your content lacks novelty for existing customers, giving them no incentive to return.
• ✅ Your product or service lacks ongoing value, leading users to not return after a single use.
• How to Optimize Retention Rates:
• ✅ Launch a membership program, offering exclusive benefits to existing customers (such as VIP discounts and loyalty points).
• ✅ Use marketing automation to regularly push valuable content to returning users.
• ✅ Establish a loyalty program to provide returning users with a higher value return.

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Blue represents the retention of new users ➜ low ➜ new customers lack motivation to return�Purple represents the return visits of existing users ➜ low ➜ existing customers lack loyalty

Essential Data Basics-9

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"User Engagement Trend Changes (Segmented by Similar Groups)", comparing the engagement time trend changes between new users vs. returning visitors.

9- [User Engagement Trend Changes (Grouped by Similar Categories)]�Compare the engagement time trend changes between new users and returning visitors.

• (1) Life Cycle ➜ Return Visit Rate ➜ [Trend of Changes in User Engagement Time]
• Blue line ➜ The participation time of new users on the second day is 3 minutes and 21 seconds.
• Purple line ➜ Existing users re-participate after an interval of 7 days, duration 1 minute 58 seconds

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9-【User Engagement Trend Changes (Grouped by Similar Categories)】�Compare the engagement time trend changes between new users and returning visitors.

• (1). Life Cycle ➜ Return Visit Rate ➜ [Trend of User Engagement Time Changes]
• Blue line ➜ The participation time of new users on the second day is 3 minutes and 21 seconds.
• Purple line ➜ Existing users participate again after an interval of 7 days, duration 1 minute 58 seconds

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9-【User Engagement Trends by Similar Groupings】�Compare the engagement time trends between new users and returning visitors.

• (1) Life Cycle ➜ Return Visit Rate ➜ [Trend of Changes in User Engagement Time]
• Blue line ➜ The participation time of new users on the second day is 3 minutes and 21 seconds.
• Purple line ➜ Returning user, participates again after an interval of 7 days, duration 1 minute 58 seconds
• Blue curve
• New User Engagement Time
• Measuring the engagement time of new visitors (users visiting for the first time)
• Purple curve
• Returning User Engagement Time
• Measuring the Engagement Time of Returning Users

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9-1. Blue curve: New User Engagement Time

• Definition: New User Average Engagement Time

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• Example:
• On October 1st, your website had 1,000 new visitors.
• The cumulative participation time for these users is 50,000 seconds.
• The average engagement time for new users = 50,000 ÷ 1,000 = 50 seconds.

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9-1. Blue Curve: New User Engagement Time

• Representative Meaning: If the average participation time of new users is very short (e.g., < 30 seconds), it may indicate:
• ✅ The homepage or landing page of the website is not engaging enough.
• ✅ Visitors leave quickly when they cannot find valuable content.
• ✅ The traffic sources are not precise enough, attracting visitors who are not genuinely interested in your website.

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• How to optimize?
• ✅ Enhance the landing page design to ensure key information is easily accessible.
• ✅ Increase interactive content, such as videos and CTAs (Call to Action buttons), to encourage new visitors to engage more willingly.
• ✅ Analyze different traffic sources, eliminate low-quality traffic, and enhance accuracy.

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9-2. Purple Curve: Returning User Average Engagement Time

• Definition:
• Measure the average duration of stay (interaction time) for "returning users" during their subsequent visits.
• This curve is generally more stable and tends to be higher than that of new visitors, as returning customers are already familiar with the website and may spend more time browsing.
• Please provide the specific content you would like translated into English. The term "舉例" translates to "for example," but I need more context or content to provide an accurate translation.
• From September 1 to September 30, your website had 5,000 returning visitors.
• The cumulative participation time for these users is 400,000 seconds.
• Then, the average engagement time for returning users = 400,000 ÷ 5,000 = 80 seconds.

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9-2. Purple curve: Returning User Average Engagement Time

• Representative Meaning: If the average engagement time of returning users is longer (e.g., > 60 seconds), it indicates:
• ✅ The website offers ongoing value to returning visitors, who are willing to spend time exploring more content.
• ✅ Your membership system, loyalty program, or email marketing strategy is effective in attracting returning customers and encouraging them to spend more time.
• If the average engagement time of returning users decreases, it may indicate:
• ❌ The website content is not updated frequently enough, causing returning customers to find no new content.
• The marketing strategy failed to maintain user engagement, as users did not browse additional pages after returning once.
• How to Optimize Participation Time:
• ✅ Add exclusive content for members to ensure returning users have new material to view.
• ✅ Remind returning users to check out the latest promotions or new articles through email marketing or push notifications.
• ✅ Offer a Loyalty Program to encourage existing users to stay longer.

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Blue represents the engagement level of new users ➜ Low ➜ New customers are not attracted�Purple represents the engagement level of existing users ➜ Low ➜ Existing customers lack motivation to revisit

Essential Basic Data-10

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What is the average value of your e-commerce website over the past 30 days and 120 days?

What is the average value of your e-commerce website over the past 30 days and 120 days?

• (1) Life Cycle ➜ Return Visit Rate ➜ Average Value Over 120 Days
• Please select a date: Choose the past 30 days.

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What is the 120-day average value?

• Purpose: 120-Day Average Revenue
• Measure the average revenue generated by each user for the business over the past 120 days.
• 2. Applicability:
• This indicator is primarily used to analyze the Lifetime Value (LTV) of customers.
• Helps you understand the average contribution profit of each user within 120 days.
• This indicator can help you assess the user value from different sources and optimize the allocation of your marketing budget.

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Example: What is the 120-day average value?

• 3. Calculation Example: You operate an e-commerce website and you want to understand the average spending per customer over the past 120 days.

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