Cohort Analysis for SEO Optimization: Improving User Retention and Engagement – Next Gen SEO with Hyper-Intelligence

Cohort Analysis for SEO Optimization: Improving User Retention and Engagement – Next Gen SEO with Hyper-Intelligence

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    The project “Cohort Analysis for SEO Optimization: Improving User Retention and Engagement” helps website owners understand how visitors (users) interact with their website and how to improve their website performance.

    Cohort Analysis for SEO Optimization

    Let’s break this down step by step.

    What is This Project About?

    This project focuses on understanding user behavior on a website to improve two key areas:

    1. User Retention:
      • How many users keep coming back to the website over time.
      • Example: If 100 users visit the website this week, how many of them return next week?
    2. User Engagement:
      • How much time users spend on the website and interact with its content.
      • Example: Are users reading the content, clicking links, or leaving immediately?

    By analyzing user behavior over time, this project gives actionable insights to improve the website’s performance.

    It Does This By Analyzing:

    1. How many users come to the website.
    2. How long users stay on the website (engagement).
    3. How many users return over time (user retention).

    The project uses Cohort Analysis, which is a powerful way of looking at user behavior over time. This helps you understand:

    • Which groups of users (cohorts) are performing well.
    • Where users are dropping off or leaving the website.
    • What strategies can improve user retention and engagement.

    What Has Been Used in This Project?

    This project uses Cohort Analysis along with SEO (Search Engine Optimization) metrics to analyze user behavior. Here’s what has been used:

    1. Cohort Analysis: allows you to analyze the behavior of these groups over time.

    A cohort is a group of users who share something in common. For example:

    • Users who visited your website during a specific week or month.
    • Users who came to your website through a specific campaign.
    • Example: You can check how many users came to your website in October and see how many of them returned in November, December, etc.

    This type of analysis helps answer critical questions like:

    • Which group of users stayed on the website the longest?
    • Which group of users stopped returning?
    • What factors make users engage more with your website?
    1. SEO Metrics:
      • Data about user visits, engagement time, bounce rate, and returning visitors is collected to measure website performance.
    2. Graphs and Heatmaps:
      • Visual tools like graphs and heatmaps are used to show data clearly and make it easy to understand.

    What is SEO Optimization?

    SEO stands for Search Engine Optimization. It is the process of improving your website so it appears higher in search engine results (like Google).

    • The better your SEO, the more people visit your website.
    • When more people visit your website, your chances of getting customers increase.

    However, it’s not enough to just get visitors. You also want them to:

    • Stay longer on your website (engage with your content).
    • Keep coming back to your website over time (retain users).

    A. Understand User Behavior

    The project analyzes user behavior by looking at:

    • How many users visited the website.
    • How long users engaged with the content.
    • How often users returned to the website.

    This analysis is done over time and grouped into cohorts (user groups).

    B. Identify Problems

    By analyzing cohorts, you can identify problems such as:

    • Low retention: If users don’t return to the website after their first visit.
    • Low engagement: If users visit the website but leave quickly (high bounce rate).
    • Drop-offs: If fewer users engage over time in certain weeks or months.

    Example:
    If users from October are leaving the website quickly, but users from November are staying longer, you can compare what changed in November and replicate that strategy.

    C. Improve SEO Performance

    Once you understand the problem, you can take actions to improve your SEO and user experience. This includes:

    • Improving Content: Adding better, more engaging content.
    • Reducing Bounce Rate: Making your website user-friendly so users stay longer.
    • Retaining Users: Using strategies like email campaigns, special offers, or better content targeting.

    Example:
    If a certain page has high engagement, you can promote that page more. If a page has low engagement, you can improve it with better content or structure.

    Why is This Project Important for Website Owners?

    This project helps website owners in the following ways:

    1. Understand User Behavior

    • What it Means: Website owners can see how users are interacting with their site. Are they staying, leaving quickly, or coming back often?
    • Why it’s Important: This helps identify which parts of the website are working well and which parts need improvement.

    2. Identify Drop-offs

    • What it Means: It shows where users are leaving the website (drop-off points).
    • Why it’s Important: If too many users leave without engaging, the website owner can make changes to fix the issue (e.g., improve content or user experience).

    3. Improve User Retention

    • What it Means: Retention measures how many users return to the website after their first visit.
    • Why it’s Important: More returning users means the website is valuable and keeps visitors interested.

    4. Boost User Engagement

    • What it Means: Engagement tracks how much time users spend on the website and interact with the content.
    • Why it’s Important: Engaged users are more likely to take actions that benefit the business, such as making purchases or signing up for services.

    5. Find High-Performing and Low-Performing User Groups

    • What it Means: The project identifies user groups (cohorts) that perform well and those that don’t.
    • Why it’s Important:
      • For high-performing groups: Website owners can study what worked well and apply those strategies to other users.
      • For low-performing groups: They can identify problems and improve those areas (e.g., better content, faster website).

    6. Make Data-Driven Decisions

    • What it Means: All insights from the project are based on real data, not guesses.
    • Why it’s Important: Data-driven decisions help improve website performance, leading to more visitors, better retention, and higher conversions (users taking action).

    How Does the Project Work?

    1. Data Collection:
      • Collect data about users who visit the website.
      • Data includes how many users visit, how long they stay, and if they come back later.
    2. Cohort Grouping:
      • Group users based on their visit time (e.g., weekly or monthly groups).
    3. Analyze User Behavior:
      • Track the retention (returning users) and engagement (interaction time) for each group.
    4. Create Visuals:
      • Build charts and heatmaps to show user behavior trends in a way that’s easy to understand.
    5. Provide Insights:
      • Highlight high-performing user groups and low-performing ones.
    6. Actionable Recommendations:
      • Suggest ways to improve user retention and engagement, such as improving content, reducing bounce rate, or fixing specific problems.

    Benefits of the Project

    Here’s why this project is beneficial for website owners:

    1. Understand what works: Discover which strategies keep users coming back and engaging with the site.
    2. Fix what’s broken: Identify areas where users leave or stop interacting and improve them.
    3. Grow website traffic: Retain more users and keep them engaged for longer.
    4. Increase conversions: Engaged and retained users are more likely to become paying customers or take desired actions.
    5. Improve SEO: Better retention and engagement help improve search engine rankings, leading to more organic traffic.

    In One Sentence

    This project helps website owners understand, analyze, and improve user behavior using Cohort Analysis and SEO data. It provides insights to increase user retention, improve engagement, and make the website perform better overall.

    What is Cohort Analysis for SEO?

    • Cohort Analysis: A way of grouping users based on shared characteristics or behaviors over a defined time period. For SEO, it helps analyze how different groups of website users behave and perform, based on how they arrived at the website (e.g., via organic search, paid ads, etc.).
    • SEO Context: It segments visitors by shared traits like acquisition date, traffic source, search query, or device type, then studies their interaction trends, such as bounce rate, time on page, or conversion rate.

    Use Cases of Cohort Analysis for SEO

    1. Traffic Source Optimization:
      • Identify how cohorts from specific traffic sources (e.g., Google search vs. Facebook ads) perform.
      • Optimize resources for the highest-performing sources.
    2. Content Performance:
      • Track engagement trends for cohorts based on content type (e.g., blogs vs. product pages).
      • Use this to create content that performs better for SEO.
    3. User Retention:
      • Measure how long users from specific cohorts stay engaged over time.
      • Optimize the website for long-term user retention.
    4. Keyword Effectiveness:
      • Understand which search queries are bringing high-quality users.
      • Refine keyword targeting strategies.
    5. Landing Page Optimization:
      • Analyze cohorts landing on specific pages.
      • Identify which pages drive deeper engagement or conversions.

    Real-Life Implementation

    1. E-commerce Website:
      • Track users who landed on product pages via SEO and group them by the search query.
      • See if they convert to buyers, and optimize for high-performing queries.
    2. Blogging Website:
      • Segment users by the month they arrived and their source (e.g., Google organic search).
      • Analyze which topics retain users over time and create similar content.
    3. Educational Websites:
      • Group visitors by the search terms they used to find courses or resources.
      • Optimize content for terms that lead to high user engagement.

    How Does Cohort Analysis Work in Context of a Website?

    For a website, cohort analysis involves:

    1. User Segmentation:
      • Group users into cohorts based on specific shared characteristics.
        • Example: Users arriving in January via “SEO tips” queries.
    2. Trend Identification:
      • Study how these groups behave over time.
        • Do they revisit? Do they convert to subscribers?
    3. SEO Strategy Refinement:
      • Optimize the website for groups performing well while addressing pain points of underperforming groups.

    Data Needed for Cohort Analysis for SEO

    The model needs data about your website traffic. This data can come in two formats:

    1. Direct Website Data via URLs:
      • Use web scraping to gather data from the website’s pages (e.g., metadata, page content).
      • Ideal when analyzing website structure or content optimization for SEO.
      • Tools like Python libraries BeautifulSoup or Selenium are used here.
    2. CSV or Analytics Data:
      • CSV files with user activity data (exported from Google Analytics or similar tools).
      • Typical columns:
        • User ID
        • Session Date
        • Traffic Source (Organic, Paid, etc.)
        • Landing Page
        • Time on Page
        • Bounce Rate
        • Search Query
      • This is the most common input type for cohort analysis as it focuses on user behavior rather than web content.

    Outputs Expected from Cohort Analysis for SEO

    The output will depend on the objective but generally includes:

    1. Retention Trends:
      • Graphs showing user retention rates over time for each cohort.
    2. Engagement Metrics:
      • Average time on page, bounce rates, or clicks per cohort.
    3. Conversion Data:
      • Which cohorts are converting and which are not.
    4. Traffic Insights:
      • Performance comparison of cohorts by traffic source.
    5. Actionable Recommendations:
      • Example: “Users from organic search on mobile devices convert 20% better than desktop users. Optimize for mobile.”

    How is it Useful for SEO Optimization?

    1. User-Centric Insights:
      • Understand specific user needs based on behaviors.
    2. Content Strategy Refinement:
      • Identify which topics or keywords work best.
    3. Focused Resource Allocation:
      • Invest in high-performing channels or content types.
    4. Retention and Growth:
      • Improve user retention by addressing pain points.
    5. Enhanced ROI:
      • Directly target strategies that maximize results.

    Step By Step Guide To Download Dataset For Cohort Analysis For Seo Model

    1. Traffic Source, Landing Page, Time on Page, and Bounce Rate (Group 1).
    2. User ID and Session Date (Group 2).
    3. Search Query (Group 3).

    Step-by-Step Guide for Downloading Group 1

    Traffic Source, Landing Page, Time on Page, and Bounce Rate

    1. Log in to GA4:
      • Open your browser and go to Google Analytics.
      • Log in using your Google account.
    2. Navigate to the “Engagement” Section:
      • On the left-hand side menu, click on Reports.
      • Under Life cycle, select Engagement > Pages and Screens.
    3. Customize the Report:
      • This report will show data about landing pages and user engagement.
      • Ensure the following columns are visible:
        • Traffic Source: Shows where the traffic is coming from.
        • Landing Page: The first page users land on.
        • Average Engagement Time per User: This is your “Time on Page” data.
        • Engagement Rate: Use this to calculate Bounce Rate (Bounce Rate = 100 – Engagement Rate).
    4. Set the Date Range:
      • At the top-right corner, click on the date selector.
      • Choose the date range for your analysis (e.g., last 28 days).
    5. Export the Data:
      • At the top-right corner of the report, click the Share Icon (a square with an arrow).
      • Select Download File and then choose CSV.
      • This will save the data to your computer in a file format you can open in Excel or Google Sheets.

    Step-by-Step Guide to Download Group 2 Dataset

    This guide explains how to download a dataset containing Date, Page Title, Sessions, Engaged Sessions, Bounce Rate, Engagement Rate, and Average Engagement Time Per Session. Follow these steps carefully.

    Step 1: Go to the “Explore” Section

    1. Open the platform you are using for your data analysis (e.g., Google Analytics 4 or a similar tool).
    2. Look at the left-hand side menu.
    3. Click on the Explore section.
      • The Explore section allows you to create custom tables and reports.

    Step 2: Start a New Report

    1. In the Explore section, you will see an option to create a new report.
    2. Click on Blank Exploration.
      • This will open a blank page where you can design and build your own table.

    Step 3: Add the Required Dimensions

    Dimensions are fields that tell you about the data categories, like Date and Page Title.

    1. On the right-hand side, you will see a section called Variables.
    2. Under Variables, look for Dimensions.
    3. Click on Add Dimension.
    4. In the search bar, type and add the following dimensions:
      • Date: This will include the dates for user sessions.
      • Page Title: This will show the title of the webpage users visited.
    5. Once you find each dimension, click Import to add it to your current exploration.

    Step 4: Add the Required Metrics

    Metrics are numerical data fields that tell you how well something is performing, like Sessions and Bounce Rate.

    1. Below the Dimensions section, you’ll find a section called Metrics.
    2. Click on Add Metric.
    3. In the search bar, search for and add the following metrics:
      • Sessions: The total number of sessions started by users.
      • Engaged Sessions: The number of sessions where users actively interacted with your website.
      • Bounce Rate: The percentage of users who left the site without interaction.
      • Engagement Rate: The percentage of engaged sessions compared to total sessions.
      • Average Engagement Time Per Session: The average time users spent actively engaging on your website.
    4. After selecting these metrics, click Import to add them to your exploration.

    Step 5: Build Your Table

    Now that you have added the dimensions and metrics, you need to build the table.

    1. Look for the section that allows you to drag and drop fields into rows and columns.
    2. From the Variables section (on the right side):
      • Drag Date into the Rows section.
      • Drag Page Title into the Rows section (below Date).
      • Drag the following metrics into the Values section:
        • Sessions
        • Engaged Sessions
        • Bounce Rate
        • Engagement Rate
        • Average Engagement Time Per Session
    3. Your table will now show:
      • Each Date as a row.
      • Each Page Title corresponding to that date.
      • Metrics like Sessions, Engaged Sessions, Bounce Rate, etc., displayed in the columns.

    Step 6: Review and Customize the Table

    1. Verify that the table is showing the correct data fields:
      • Date
      • Page Title
      • Sessions
      • Engaged Sessions
      • Bounce Rate
      • Engagement Rate
      • Average Engagement Time Per Session
    2. Check that the table is organized properly and the numbers make sense.

    Step 7: Export the Table

    1. Once your table is ready, look at the top-right corner of the screen.
    2. Find and click on the Export button (it might look like a download icon).
    3. Choose CSV (Comma-Separated Values) as the format for exporting.
      • A CSV file can be opened in tools like Microsoft Excel, Google Sheets, or any other data viewer.
    4. The table will be downloaded to your computer with all the required fields.

    Step-by-Step Guide for Downloading Group 3

    Search Query

    1. Go to the “Search Console” Section:
      • On the left-hand side menu, click on Search Console under Reports.
      • Select Queries.
    2. Set the Date Range:
      • At the top-right corner, click the date selector and choose your desired range (e.g., last 28 days).
    3. Export the Data:
      • Click the Share Icon (a square with an arrow) in the top-right corner.
      • Choose Download File and select CSV.

    Part 1: Metadata Extraction Code

    What this code does:

    • It extracts metadata from a list of webpage URLs using Python libraries like BeautifulSoup and requests. Metadata includes:
      • Page title (name of the webpage).
      • Meta description (short description used by search engines).
      • Meta keywords (words that describe the page for SEO purposes).
      • H1 and H2 tags (headings on the page).
      • Word count (total words on the page).

    Why is this important:

    • It collects important SEO information about each webpage, which can help you analyze how well the content is optimized for search engines.

    Understanding the Output Step-by-Step

    This output is “Extracted Metadata and Content” from a list of URLs. It is primarily used for SEO Analysis and content understanding for the webpages listed.

    What is this output?

    This is a table that summarizes important information about the webpages listed (URLs). It extracts:

    1. Webpage Titles
    2. Meta Descriptions
    3. Meta Keywords
    4. H1 and H2 Tags (important content headings on a webpage)
    5. Word Count

    This output helps you analyze how well these pages are optimized for SEO (Search Engine Optimization).

    Detailed Explanation of Each Column

    Column NameExplanation
    URLThe webpage link or address. This shows where the content is coming from.
    Page TitleThe title of the webpage (what you see in browser tabs or search engines).
    Meta DescriptionA short summary of the webpage content shown on search engines like Google.
    Meta KeywordsKeywords added to the webpage to help search engines understand the content.
    H1 TagsThe main headings (H1) on the page. H1 tags tell what the page is about.
    H2 TagsSubheadings (H2) under the main heading. These break down the content further.
    Word CountThe total number of words on the webpage. This shows how much content is there.

    Detailed Explanation with Example

    Let’s go row-by-row and explain the sample data for each webpage:

    1. First Row

    • URL: https://webtool.co/advanced-seo-service/
      • This is the link to a webpage about advanced SEO services.
    • Page Title: maximize seo: advanced services for serp success – webtool
      • This is the title that appears on Google Search results. It talks about maximizing SEO success with advanced services.
    • Meta Description:
      Unlock the full potential of your online presence with advanced SEO services tailored for SERP success.
      • This summary explains what the page is about. It aims to attract visitors by mentioning how the service helps improve online presence.
    • Meta Keywords: advanced SEO Services, SEO Services
      • Keywords like “advanced SEO Services” and “SEO Services” tell search engines what the page is about.
    • H1 Tags: ADVANCED SEO SERVICES; Quick Enquiry
      • The main headings on the webpage. “ADVANCED SEO SERVICES” is likely the headline, and “Quick Enquiry” might be a call to action (like a button or form).
    • H2 Tags: SEO Service; Basic SEO Service; Advanced SEO Service
      • Subheadings that break down the different types of SEO services offered.
    • Word Count: 1526
      • This page has 1526 words, meaning it has substantial content which can help it rank well in search engines.

    2. Second Row

    • URL: https://webtool.co/content-optimization-using-ai/
      • A link to a page about AI-based content optimization.
    • Page Title: content optimization using ai – webtool
      • The title indicates the page focuses on optimizing content using AI.
    • Meta Description: No Meta Description
      • This page is missing a meta description. This is a problem because search engines use meta descriptions to summarize the content.
    • Meta Keywords: No Keywords
      • No keywords have been set for this page, which can negatively affect its SEO ranking.
    • H1 Tags: What is Content optimization?
      • The main heading introduces the topic of content optimization.
    • H2 Tags: Ready for Action?; FEATURES; COMPANY; MENU
      • Subheadings that likely organize the page into sections like features, actions, company details, and menu.
    • Word Count: 201
      • The page has only 201 words, which is low. Search engines prefer longer, more detailed content for better ranking.

    3. Third Row

    • URL: https://webtool.co/cora/
      • A page link related to the tool called CORA.
    • Page Title: cora – webtool
      • Simple title mentioning CORA and the website name.
    • Meta Description: No Meta Description
      • Again, the meta description is missing, which makes it harder for users to know what the page is about from Google search.
    • Meta Keywords: CORA, CORA tool
      • Keywords “CORA” and “CORA tool” are included to indicate the page focuses on this specific tool.
    • H1 Tags: Cora Analysis
      • The main heading highlights that the page is about “Cora Analysis.”
    • H2 Tags: Cora analysis and its benefits; Ideal Value for Analysis
      • Subheadings provide further details on the benefits and ideal use of the tool.
    • Word Count: 323
      • The page has 323 words, which is relatively low but still better than the previous row.

    4. Fourth Row

    • URL: https://webtool.co/cosine-similarity/
      • A page about “Cosine Similarity,” a semantic analysis concept.
    • Page Title: cosine similarity checker | semantic seo | webtool
      • The title is optimized with keywords like “Cosine Similarity” and “Semantic SEO” for search ranking.
    • Meta Description:
      Optimimze the content with the help of cosine similarity for semantic SEO.
      • A clear and concise meta description explaining the page’s purpose: to help with content optimization using cosine similarity.
    • Meta Keywords:
      Cosine Similarity, Cosine Similarity checker, Semantic SEO
      • These keywords are very relevant to the topic.
    • H1 Tags: Cosine Similarity; Fix Cosine Similarity Issue
      • The main headings emphasize the importance and solution related to cosine similarity.
    • H2 Tags: How to use cosine Similarity in webtool? Follow below.
      • Subheadings likely explain how to use the tool step-by-step.
    • Word Count: 519
      • The page has 519 words, which is moderate and can help with ranking.

    What Does This Output Tell Us?

    1.    SEO Optimization Analysis:

    • Pages missing Meta Descriptions and Keywords may not rank well on search engines.
    • Adding these will help users and search engines understand the content better.

    2.    Content Insights:

    • Pages with fewer words (like Row 2 with only 201 words) may lack depth. Longer, detailed content performs better for SEO.

    3.    Headings Analysis:

    • The presence of clear H1 and H2 Tags is good for SEO and helps organize content for users.

    4.    Actionable Recommendations:

    • Add meta descriptions to pages where they are missing.
    • Add more detailed content to pages with low word counts.
    • Optimize keywords to target specific search queries.

    Why is This Output Important for Clients?

    This data provides a snapshot of how well their webpages are optimized for SEO. It helps clients:

    1. Identify gaps in their SEO strategy (e.g., missing meta descriptions, low content).
    2. Understand which pages are performing better in terms of content and structure.
    3. Take specific steps to improve SEO rankings by optimizing titles, descriptions, and headings.

    By improving these areas, the client can attract more visitors to their website and rank higher on search engines like Google.

    Part 2: Fuzzy Matching Code

    What this code does:

    • It matches webpage titles from two different datasets:
      1. Behavioral data (contains metrics like user activity, views, and engagement).
      2. Extracted metadata (contains SEO information such as page titles and descriptions).
    • If the titles are slightly different, it uses fuzzy matching to find the closest match.

    Why is this important:

    • Sometimes, the titles of webpages in different datasets might not match exactly due to spelling variations, special characters, or formatting differences.
    • This step ensures that relevant SEO metadata (like titles, descriptions, and word count) is linked to the behavioral data.

    Understanding the Output

    This output is a detailed summary of dataset after applying fuzzy matching to combine behavioral data (like user activity, engagement metrics) with metadata (like titles, descriptions, and headings) extracted from webpages.

    What is This Output About?

    This output provides a final combined dataset that merges two types of data:

    1. Behavioral Data – Information about user activity on webpages, like views, sessions, bounce rates, and revenue.
    2. Metadata – Information about webpage content, like page titles, meta descriptions, H1 and H2 headings, and word count.

    This is done through a matching process where page titles are compared between datasets using “fuzzy matching” (a method to find similar but not exactly matching titles).

    Explanation of Key Sections

    1. Extracted Metadata and Content

    This part shows a small sample of what the dataset contains after combining both datasets.

    ·         URL:
    The webpage link where the metadata was extracted.

    ·         Page Title:
    This is the title of the webpage. Titles are important because they appear on search engines and give users a first impression of the page content.

    ·         Meta Description:
    A short summary of what the webpage is about. This appears under the page title in Google Search.

    • For example, if it says “No Meta Description”, it means the webpage doesn’t have one, which is a problem because search engines and users rely on it.

    ·         Meta Keywords:
    Keywords that describe the webpage content. These help search engines understand what the page is about.

    • If it says “No Keywords”, it means no keywords were provided.

    ·         H1 Tags:
    The main headings of the webpage. H1 tags describe the main topic of the page.

    • Missing H1 Tags means the webpage doesn’t have clear headings, which is bad for SEO.

    ·         H2 Tags:
    These are the subheadings under the main heading (H1). They help organize the content and make it easier for readers to navigate.

    ·         Word Count:
    This shows the total number of words on the page.

    • Pages with very few words (e.g., 0.0 words) lack substantial content, which can affect their ability to rank on search engines.

    2. Rows with Missing Metadata After Fuzzy Matching

    This section tells us how many rows (webpages) still do not have metadata even after attempting to match them.

    ·         154 rows with missing metadata:
    Out of all the pages, 154 pages could not be matched with their metadata (like URLs, descriptions, headings).

    • This means that for these pages, the metadata is either missing or could not be found.

    ·         Example Rows with Missing Metadata:
    Look at these rows:

    • These rows represent page titles from the behavioral dataset that do not have metadata (like URL, descriptions, keywords, or headings).
    • As a result, fields like “URL”, “Meta Description”, “H1 Tags”, and “H2 Tags” show “No URL” or “No Meta Description”.

    3. Sample Combined Dataset

    This is the final combined dataset where the two sources of data are merged. Let’s break down what each column means:

    Key Insights from This Output

    1.    Pages Missing Metadata:

    • A total of 154 pages are missing metadata (like titles, descriptions, and URLs).
    • These pages need to be updated with proper metadata to improve their performance in search engines.

    2.    Low Word Count Pages:

    • Many pages have a word count of 0.0 or very low values. This indicates a lack of content, which can negatively impact SEO rankings.

    3.    Final Combined Dataset:

    • The “final_page_title” column combines page titles from both datasets, making it easier to identify unmatched or incomplete data.

    4.    Fuzzy Matching Process:

    • Page titles from two datasets were matched using fuzzy matching (a method to match similar words).
    • Rows where a match wasn’t found remain incomplete and need manual correction or metadata updates.

    How This Output Benefits (The Client)

    1.    Identifies Gaps:

    • This output highlights which pages lack important SEO metadata (titles, descriptions, headings).

    2.    Improves SEO:

    • By fixing missing metadata and increasing word count on low-content pages, you can boost your search engine rankings.

    3.    Focus on Action:

    • Pages with “No Meta Description” or “No H1/H2 Tags” should be prioritized for updates.
    • Adding clear titles, descriptions, and headings improves user experience and search visibility.

    4.    Clear Next Steps:

    • Use this data to update your webpages with proper content, metadata, and structure.

    Part 3: Cohort Analysis Code

    What this code does:

    ·         It performs Cohort Analysis, which divides users into groups (called cohorts) based on when they first interacted with the website (like per week or month).

    ·         It calculates:

    1. User Retention: How many users returned after their first visit.
    2. Drop-off Analysis: The week-over-week decrease in user activity.
    3. Engagement Metrics: Metrics like views, bounce rate, and average engagement time.

    ·         This part also generates visualizations such as:

    • Cohort Retention Heatmap: Shows retention percentages for different weeks.
    • Weekly Trends: Tracks active users and engagement metrics.

    Why is this important:

    • Cohort Analysis helps you identify user behavior trends over time.
      • Which user groups (cohorts) are staying engaged?
      • Where are users dropping off, and why?
    • The heatmaps and charts provide a clear visual representation of user retention and engagement.

    Output Explanation

    1. Top 5 Pages with Highest Engagement Time per Session

    This table shows the top-performing pages based on the amount of time users spend interacting with them. “Engagement time” tells us which pages are the most engaging or useful for visitors.

    What This Means:

    • High Engagement Pages: Pages where users spend more time are performing well. For example:
      • Page “马来西亚成人 seo：提升知名度 | webtool” has an average engagement time of 208 seconds.
      • The second-highest page has 106 seconds.
    • These pages are successful in capturing user attention and keeping them engaged.

    2. Top Performing URLs Based on Views and Active Users

    This table shows which pages have the most views and active users. It also includes the bounce rate, which tells us how often users leave the page without interacting further.

    What This Means:

    Graph Explanations

    1. Weekly Active Users Over Time

    ·         What the graph shows:

    • This graph displays how many users were active each week over a period of time.
    • Active users are those who interacted with your website during a given week.

    ·         What you see in the graph:

    • There is a sharp increase in active users between the first and second week.
    • Active users reached their peak in the week of November 4–10 (with 100 users).
    • After that, there is a decline in active users over the following weeks, with a small recovery around November 24–December 1.

    ·         What this means for the business:

    • The sharp peak suggests a successful event, campaign, or content release that drove user activity in early November.
    • The gradual decline afterward indicates a need for re-engagement strategies like:
      • New campaigns or content releases.
      • Promotions to bring users back to the site.

    2. Engagement Metrics Over Time

    This graph combines three key metrics over time:

    MetricExplanation
    Engaged SessionsTotal number of user sessions where users actively engaged with the content.
    Bounce RatePercentage of sessions where users left immediately without interacting.
    Avg. Engagement TimeAverage time users spent on the site during their session.

    What the graph shows:

    ·         Engaged Sessions (Blue Line):

    • This line shows how many sessions involved active user engagement.
    • It peaked dramatically in early November, suggesting a high level of interaction during that time.

    ·         Average Engagement Time (Green Line):

    • This shows how much time users spent on the site during each session.
    • There is a noticeable rise towards the end of the graph, meaning users spent more time on the website later in the period.

    ·         Bounce Rate (Orange Line):

    • This line remains low across the timeline, which is a good sign. A low bounce rate means most users interacted with the content.

    What this means for the business:

    • High Engagement in Early November: The peak in engaged sessions suggests that users found the content valuable during that time. This can be linked to successful campaigns, updates, or new content.
    • Increasing Engagement Time: The rise in average engagement time indicates that users are finding certain pages or content more interesting.
    • Low Bounce Rate: Consistently low bounce rates are a positive sign that users are interacting with the content rather than leaving immediately.

    Key Business Insights and Recommendations

    Based on the data from tables and graphs:

    1. Top Performing Pages

    • Observation: Pages with high engagement time and views are performing the best.
    • Action Steps:
      • Identify what makes these pages engaging (e.g., content quality, topics, structure).
      • Replicate these strategies for underperforming pages.

    2. Weekly Active Users

    • Observation: There was a peak in early November followed by a decline.
    • Action Steps:
      • Plan campaigns or content releases around periods of low activity to bring users back.
      • Analyze what drove the spike in early November and replicate it.

    3. Engagement Metrics

    • Observation: Engaged sessions and average engagement time show positive trends.
    • Action Steps:
      • Focus on maintaining and improving engagement time by creating more valuable, interactive content.
      • Monitor bounce rates on pages with higher traffic to ensure users stay engaged.

    Summary

    This analysis provides key insights into:

    • How users interact with your website (active users, engagement time, bounce rates).
    • Which pages perform best based on engagement and traffic.
    • Trends over time, highlighting peaks and areas needing improvement.

    By focusing on replicating successful strategies and addressing issues on underperforming pages, the website owner can improve user retention, engagement, and overall performance.

    Part 4: Conversion Rate Analysis Code

    What this code does:

    ·         It calculates the Conversion Rate for different cohorts (groups of users based on months).

    ·         It identifies:

    1. The high-performing cohort (group with the highest conversion rate).
    2. The low-performing cohort (group with the lowest conversion rate).

    ·         It generates a bar chart called “Conversion Rate by Cohort” to visually compare the performance of different cohorts.

    Why is this important:

    ·         The conversion rate measures how many users engage with the content after visiting the website.

    ·         By comparing cohorts, you can:

    • Find out what strategies worked for the best-performing group.
    • Identify issues that caused low performance and fix them.

    ·         This helps you make data-driven decisions to improve user engagement and overall website performance.

    Understanding the Output

    Output consists of three main parts:

    1. Cohort Summary Table
    2. Drop-off Analysis (Week-over-Week)
    3. Conversion Insights (High-Performance and Low-Performance Cohorts)

    Each of these sections provides meaningful insights into how users are behaving on your website over time. Let me break down each part.

    1. Cohort Summary Table

    What it Shows

    This table provides the total number of users acquired in each cohort (week) and their retention rates for Week 1.

    • Cohort: A group of users who started visiting the website during a specific period (e.g., a week).
    • Total Users: The total number of unique users acquired during that cohort period.
    • Week 1 Retention (%): The percentage of users who came back in the first week after being acquired.

    How to Interpret the Data

    • If you see a 0% retention for a specific cohort, it means that users in that cohort did not return to the website during Week 1.
    • If a cohort has “inf” (infinity) or NaN (Not a Number), it indicates missing or incomplete data for that period.

    Example from Your Output

    • 2024-10-07/2024-10-13: 100 users were acquired, but none returned in Week 1 (retention = 0%).
    • 2024-10-14/2024-10-20: Data is incomplete or unavailable (NaN, inf).

    Action for Website Owner

    • Why this is useful: It helps you identify which cohorts (weeks) have the best or worst user retention.
    • Steps to take:
      • Focus on improving Week 1 retention by re-engaging users (e.g., sending follow-up emails, showing notifications, or offering content).
      • Investigate why some cohorts have missing or incomplete data to ensure future data is accurate.

    2. Drop-off Analysis (Week-over-Week)

    What it Shows

    The drop-off analysis table indicates how many users stop visiting your website week by week after their acquisition.

    • Active Users: The number of active users in a particular week for a specific cohort.
    • A negative value (e.g., -0.0) shows no new user activity or a drop in engagement.

    How to Interpret the Data

    If users are consistently dropping off across all weeks:

    • This suggests an issue with user engagement or content quality.
    • You are not retaining your users beyond the first interaction.

    Example from Your Output

    • 2024-10-14/2024-10-20: 100 users were active initially.
    • 2024-10-21/2024-10-27: No users returned after Week 1 (drop-off).

    Action for Website Owner

    • Why this is useful: It highlights weeks where users stopped visiting your website.
    • Steps to take:
      • Improve user experience and engagement strategies beyond the first visit.
      • Introduce campaigns like newsletters, notifications, or loyalty offers to reduce drop-offs.

    3. Conversion Insights (High-Performance and Low-Performance Cohorts)

    What it Shows

    This part of the output analyzes user conversion rates for each cohort (group of users).

    • Sessions: Total number of website visits from users in a cohort.
    • Engaged Sessions: Sessions where users actively interacted with your website (e.g., viewed content, clicked links).
    • Bounce Rate: The percentage of users who left the website after viewing just one page. A high bounce rate is bad.
    • Engagement Rate: The percentage of sessions where users interacted with content. Higher is better.
    • Views: The total number of pages viewed during the cohort period.
    • Conversion Rate: Percentage of sessions that led to meaningful engagement (e.g., “Engaged Sessions” / “Total Sessions” * 100).

    Example from Your Output

    ·         High-Performance Cohort:

    • Cohort Month: 2024-11
    • Sessions: 351
    • Engaged Sessions: 208
    • Bounce Rate: 33.6%
    • Conversion Rate: 59.25%

    ·         Low-Performance Cohort:

    • Cohort Month: 2024-10
    • Sessions: 171
    • Engaged Sessions: 81
    • Bounce Rate: 49.98%
    • Conversion Rate: 47.36%

    What Does This Mean?

    • The November cohort performed better, with a higher conversion rate (59%) and a lower bounce rate (33%).
    • The October cohort performed poorly, with a lower conversion rate (47%) and a higher bounce rate (49%).

    Action for Website Owner

    • Why this is useful: It helps you identify what works and what doesn’t in terms of user engagement.
    • Steps to take:
      • Replicate strategies from the November cohort to improve user engagement and conversions.
      • Investigate why the October cohort had a higher bounce rate and lower conversions. Possible issues:
        • Poor user experience (e.g., slow website speed, confusing navigation).
        • Low-quality content or irrelevant content for that period.
        • Ineffective marketing or targeting strategies.

    Summary of Steps for Website Owner

    1.    Improve Retention:

    • Focus on reducing Week 1 drop-offs by improving re-engagement strategies like personalized emails, targeted ads, or content suggestions.

    2.    Reduce Drop-offs:

    • Analyze user feedback and behavior to determine why users stop visiting after their first interaction.
    • Improve website performance, add more engaging content, and offer incentives for returning users.

    3.    Boost Conversions:

    o Study the high-performing cohort (e.g., November) to replicate its success. Identify what made users engage more:

    • Was it better content?
    • A promotional offer?
    • Better targeting or ad campaigns?

    o Fix issues in the low-performing cohort (e.g., October) such as high bounce rates, poor targeting, or irrelevant content.

    4.    Data Consistency:

    • Address missing or incomplete data (NaN, inf) to ensure accurate analysis.
    • Verify that tracking tools (e.g., Google Analytics) are set up correctly.

    Graph 1: Weekly Active Users Over Time

    What does this graph show?

    This graph shows how the number of active users changes week by week. Each point on the graph represents the total number of users who interacted with the website during that specific week.

    Breakdown of the graph:

    1. X-axis (horizontal line): Represents the weeks starting from 2024-10-07/2024-10-13 to 2024-12-09/2024-12-15. Each label shows a week-long period.
    2. Y-axis (vertical line): Represents the number of active users.
    3. Line and Dots: Each dot represents a week and its corresponding number of active users. The line connects the dots to show how the number of active users increases or decreases over time.

    Observations:

    • There was a sharp increase in active users during the week 2024-10-14/2024-10-20 (around 65 users).
    • The number of users peaked at around 100 users during 2024-11-04/2024-11-10.
    • After this peak, the number of active users started to decline, showing a downward trend in the later weeks (towards December).

    What does this mean for the client?

    • This graph helps you understand user activity trends over time.
    • Weeks with high user activity indicate that your website was performing well (e.g., better marketing campaigns, good content engagement, or SEO strategies).
    • The drop in active users after the peak suggests that something might have gone wrong:
      • Content quality may have dropped.
      • Website issues or slow response times could have discouraged users.
      • There might have been less traffic during these weeks.

    Actions you should take:

    1. Analyze the weeks with high user activity:
      • Understand what you did differently in those weeks. For example, was there a blog post, SEO campaign, or any promotion that increased engagement?
    2. Investigate the decline:
      • Check if there were any technical issues, lack of new content, or decreased marketing efforts.
    3. Focus on replicating success:
      • Use the strategies from the weeks with the highest activity to bring back more users.

    Graph 2: Engagement Metrics Over Time

    What does this graph show?

    This graph shows three key metrics for user engagement over time:

    1. Engaged Sessions (blue line): Number of meaningful user sessions where visitors interacted with the site content.
    2. Bounce Rate (orange line): Percentage of users who left the site after visiting just one page.
    3. Average Engagement Time (green line): Average time users spent actively engaging with the site content.

    Breakdown of the graph:

    1. X-axis: Represents the weeks from 2024-10-07/2024-10-13 to 2024-12-09/2024-12-15.
    2. Y-axis: Represents the metric values for each of the three metrics.
    3. Lines:
      • Blue Line (Engaged Sessions): The line increases and decreases, showing how many sessions had meaningful user engagement.
      • Green Line (Average Engagement Time): The line indicates the average time spent on the site each week.
      • Orange Line (Bounce Rate): A flat line near the bottom, showing low bounce rates across weeks.

    Observations:

    1. Engaged Sessions:
      • Sessions peaked during the week 2024-11-04/2024-11-10 (similar to the peak in active users).
      • There was a gradual decline in engaged sessions in December, indicating reduced user interest.
    2. Average Engagement Time:
      • The time users spent actively on the site also varied. There was an increase in late November to December, suggesting some content or features kept users engaged for longer.
    3. Bounce Rate:
      • The bounce rate remained low throughout, which is a good sign. It means most users did not leave the website immediately.

    What does this mean for the client?

    • High engaged sessions and low bounce rates indicate that the website content is performing well during certain weeks.
    • Average engagement time increasing is a positive sign, as it shows users are spending more time consuming content.
    • The weeks with low engaged sessions or reduced average engagement time need attention:
      • Check the type of content or SEO strategy used.
      • Identify any drop in traffic sources.

    Actions you should take:

    1. Focus on weeks with high engaged sessions:
      • Review the content or campaigns during these weeks to understand what worked.
    2. Improve engagement time:
      • Add high-quality content like videos, blogs, or interactive elements to keep users on the site longer.
    3. Analyze bounce rates:
      • Even though the bounce rate is low, continuously monitor it to ensure it does not increase in future weeks.

    Top 5 Pages with Highest Engagement Time per Session

    What does this table show?

    This table lists the top 5 pages where users spent the most time per session.

    Breakdown of the table:

    1. Average Engagement Time per Session: Shows the average time (in seconds) users spent actively on a page.
    2. Views: Total number of times the page was viewed.
    3. Active Users: Number of unique users who interacted with the page.

    Observations:

    • Top Page: The page “马来西亚成人 seo：提升知名度 | webtool” had the highest average engagement time (208 seconds), but no views or active users. This could indicate a data inconsistency or an isolated session.
    • Pages like:
      • “seo-dienst für immobilien” and
      • “thailand adult seo: maximize visibility | webtool” had good engagement times, suggesting they were valuable for users.

    What does this mean for the client?

    • Pages with high engagement times are performing well. Users find these pages valuable and are spending time exploring the content.
    • Pages with high engagement but low views need more traffic. Promote these pages through SEO, social media, or marketing campaigns.

    Actions you should take:

    1. Promote high-performing pages:
      • Drive traffic to these pages through ads, newsletters, or social media.
    2. Analyze the content:
      • Understand why users are spending more time on these pages. Replicate the successful strategies for other pages.

    Top Performing URLs Based on Views and Active Users

    What does this table show?

    This table lists the top 10 URLs based on their performance in terms of:

    1. Views: Total number of times the URL was visited.
    2. Active Users: Number of unique users who interacted with the URL.
    3. Bounce Rate: Percentage of users who left the website after visiting that URL.

    Observations:

    What does this mean for the client?

    • These URLs are performing well in attracting and engaging users.
    • Pages with low bounce rates (like cosine-similarity) are especially successful because users explored more than one page.

    Actions you should take:

    1. Focus on high-performing URLs:
      • Keep promoting these pages through SEO, ads, or campaigns.
    2. Improve low-performing pages:
      • If a page has low views but a good bounce rate, optimize it for better visibility.
    3. Lower bounce rates further:
      • Add internal links, call-to-actions (CTAs), or related content to encourage users to stay on the site longer.

    Summary

    This analysis provides insights into:

    1. User engagement trends over time (active users, engaged sessions).
    2. Top-performing pages with high engagement times.
    3. Best-performing URLs that attract the most traffic.

    Next Steps:

    • Identify what worked well during peak weeks and replicate those strategies.
    • Promote high-performing pages to attract more traffic.
    • Improve pages with low views but good engagement potential.

    By following these steps, you can increase user engagement, reduce drop-offs, and improve the overall performance of the website.

    1. Cohort Retention Heatmap

    What is it?

    The Cohort Retention Heatmap shows how many users remain active over several weeks after their initial interaction with your platform.

    What does this heatmap show?

    • Acquisition Week (on the Y-axis): The week users first visited or started interacting with your website.
    • Week of Activity (on the X-axis): The following weeks after the acquisition week.
    • Retention %: Each value in the grid represents the percentage of users still active in that specific week.

    Key Observations:

    • 100% in the first cell (2024-10-07/2024-10-13): This indicates all users (100%) were active during their first week of acquisition.
    • 0% for subsequent weeks: Retention drops to 0, meaning no users remained active in the following weeks.
    • For later acquisition weeks, there are – or empty values. This means no data is available yet because those acquisition periods are recent.

    Why does this matter?

    • Retention analysis helps you understand how long users stay engaged. If retention is low, it means users are leaving your website quickly.
    • A website owner can improve retention by providing better content, user experiences, or strategies to keep users returning.

    2. Cohort Summary Table

    What is it?

    The Cohort Summary Table provides:

    1. Total Users: The number of users acquired in each week.
    2. Week 1 Retention (%): How many of those users returned or stayed active in the first week after their acquisition.

    Key Observations:

    • First Cohort (2024-10-07/2024-10-13):
      • Total Users = 100
      • Week 1 Retention = 0.0% → None of the 100 users returned after the first week.
    • Other Cohorts: Total Users are NaN (Not Available), meaning no user data has been recorded yet.

    Why does this matter?

    • This table helps you identify which acquisition weeks bring in the most users and how many of them stay engaged.
    • Low retention rates (like 0%) suggest a need to improve strategies for keeping users engaged.

    3. Drop-off Analysis Table

    What is it?

    The Drop-off Analysis Table shows the week-over-week decline in active users. Drop-off means how many users stopped being active compared to the previous week.

    What do the numbers mean?

    • For the first cohort (2024-10-07/2024-10-13), the active users dropped from 100 to 0 in subsequent weeks.
    • Other cohorts have -0.0 for all weeks, meaning no data or no users were active at all.

    Why does this matter?

    • High drop-off rates indicate users are not finding value in their first interaction, so they don’t return.
    • Next steps: Improve content, fix user experience issues, or engage users with follow-up strategies like emails or recommendations.

    4. Weekly Active Users Graph

    What is it?

    This graph shows how many users actively visited or interacted with your website week by week.

    Key Observations:

    1. Sharp rise (Week 2): Active users increased from 5 to 65 users.
    2. Drop (Week 4): Active users fell significantly to 30 users.
    3. Peak at Week 5: The highest point shows 100 active users.
    4. Steady decline afterward: Active users gradually decreased to around 40 users by the final weeks.

    Why does this matter?

    • A website owner can see when user activity peaks and identify weeks of high or low performance.
    • Focus on weeks with drops to understand why users left.
    • Leverage strategies that worked during the peak to bring back users.

    5. Engagement Metrics Graph

    What is it?

    This graph compares three metrics over time:

    1. Engaged Sessions (blue line): Sessions where users actively interacted with your website.
    2. Bounce Rate (orange line): The percentage of users who left your website without interacting.
    3. Average Engagement Time (green line): How much time users spent on the website on average.

    Key Observations:

    • Engaged Sessions:
      • A clear peak in Week 5 (85 sessions), followed by a decline.
      • This aligns with the spike in active users.
    • Bounce Rate: Remains consistently low, indicating most users engaged with the content.
    • Average Engagement Time:
      • Steady until Week 10, where it peaked at 45 seconds.
      • Engagement time decreases in the later weeks.

    Why does this matter?

    • Engaged Sessions: Identify weeks with the highest engagement. Study the content or events that worked.
    • Low Bounce Rate: A good sign that users are exploring your website.
    • Average Engagement Time: Longer engagement time indicates better content value. Focus on improving engagement in weeks with low values.

    6. Top 5 Pages with Highest Engagement Time

    What is it?

    This table lists the pages where users spent the most time per session.

    What does this mean?

    • These are the most engaging pages on your website. Users are spending a lot of time here.
    • Pages with higher views and active users (like “thailand adult seo”) are performing particularly well.

    Why does this matter?

    • Focus on these pages to understand why users find them engaging.
    • Replicate the successful strategies (content quality, visuals, etc.) across other pages.
    • Optimize pages with zero views to attract traffic.

    7. Top Performing URLs by Views and Active Users

    What is it?

    This table shows the URLs with the highest views and active users.

    Key Insights:

    1. Top URLs drive the most traffic and user activity.
    2. Bounce Rate is low for these URLs, which is a positive sign.

    Why does this matter?

    • Identify your most successful pages and optimize them further to maintain high traffic.
    • For pages with low bounce rates, ensure you continue delivering quality content.
    • Use this data to guide SEO efforts: promote these URLs further, improve similar content, and analyze their success factors.

    Key Steps for the Website Owner

    1. Improve Retention: Since retention drops to zero, analyze why users don’t return. Enhance content or provide incentives for repeated visits.
    2. Engage Users Longer: Pages with higher engagement time should be studied to replicate success across the website.
    3. Focus on High-Performing Pages: Promote top URLs to drive more traffic and leverage their success.
    4. Optimize Drop-off Weeks: Identify why user activity declined in specific weeks and take corrective actions.
    5. Track Trends: Regularly monitor graphs to identify performance changes and adapt strategies accordingly.

    This output provides clear insights into user behavior, helping website owners make data-driven decisions to improve user retention, engagement, and overall performance.

    1. What is a Cohort?

    A cohort refers to a group of users who started their activity during the same period (like a specific month or week). Cohort Analysis helps analyze their behavior over time and compares user groups.

    In this analysis:

    • Each Cohort Month (like 2024-10, 2024-11, and 2024-12) represents users who started engaging with your platform in that specific month.
    • You are analyzing how well these users convert into engaged users over time.

    2. Conversion Rate by Cohort Chart

    What does the chart show?

    This bar chart shows the conversion rate (%) for users acquired in different cohort months:

    • X-axis: Represents Cohort Month (e.g., October 2024, November 2024, December 2024).
    • Y-axis: Represents the Conversion Rate (%), which indicates the percentage of sessions where users actively engaged with the content.

    Key Observations from the Chart:

    1. 2024-10 (October): Conversion Rate is approximately 47%.
    2. 2024-11 (November): Conversion Rate is the highest, approximately 59%.
    3. 2024-12 (December): Conversion Rate is slightly lower than November but still higher than October.

    Why does this matter?

    • This chart helps you compare the performance of different cohorts (groups of users).
    • Higher conversion rates mean users are more engaged and performing desired actions (like interacting, reading content, or taking specific actions).
    • If a specific cohort performs well (like November 2024), you can identify and replicate the strategies used to achieve success.

    3. High-Performance Cohort Insights

    What is it?

    The high-performing cohort refers to the group of users (from November 2024) that achieved the best conversion results.

    Metrics for November 2024:

    1. Sessions: 351 → Total number of user visits in this cohort.
    2. Engaged Sessions: 208 → Number of sessions where users actively interacted with the website.
    3. Bounce Rate: 33.6% → Percentage of users who left the website without interacting further (lower is better).
    4. Engagement Rate: 66.3% → Percentage of sessions where users actively engaged with content.
    5. Views: 774 → Total number of page views generated by this cohort.
    6. Conversion Rate: 59.3% → This is the percentage of engaged sessions compared to total sessions.

    What does this mean?

    • The November 2024 cohort had a high conversion rate because:
      • Many users interacted and engaged with the content.
      • The bounce rate was relatively low.
      • Engagement rate was high, showing that users were active on the platform.

    How can this help?

    • Analyze what strategies worked well in November:
      • Was it better content?
      • Did specific pages or campaigns attract users?
    • Replicate these successful strategies for future months to maintain or increase conversion rates.

    4. Low-Performance Cohort Insights

    What is it?

    The low-performing cohort refers to the group of users (from October 2024) that performed the worst in terms of conversion.

    Metrics for October 2024:

    1. Sessions: 171 → Total number of user visits in this cohort.
    2. Engaged Sessions: 81 → Users who interacted with the content.
    3. Bounce Rate: 49.9% → Nearly half the users left the site without interacting (this is a red flag).
    4. Engagement Rate: 50.0% → This is significantly lower than the high-performing cohort.
    5. Views: 362 → Total page views (much lower than November).
    6. Conversion Rate: 47.3% → The percentage of engaged sessions was much lower compared to November.

    What does this mean?

    • The October 2024 cohort underperformed because:
      • Fewer users engaged with the content.
      • The bounce rate was higher, meaning many users left the site without interacting.
      • Engagement was low compared to the high-performing cohort.

    How can this help?

    • Investigate why users in October didn’t engage well:
      • Was the content less appealing or relevant?
      • Were there technical issues (like slow loading pages)?
      • Did marketing or promotional efforts fail to attract the right audience?
    • Fix these issues to improve user engagement for future cohorts.

    5. Actionable Recommendations

    Based on the observations above, here are the steps a website owner should take:

    For High-Performing Cohort (November 2024):

    • Identify what worked well in this month:
      • Analyze the content users engaged with the most.
      • Look for successful marketing campaigns or user acquisition strategies.
      • Check the pages or features that received the most engagement.
    • Replicate these strategies:
      • Use similar campaigns, content styles, or promotional activities for other cohorts.

    For Low-Performing Cohort (October 2024):

    • Identify and fix issues:
      • Improve content quality or relevance to match user expectations.
      • Reduce bounce rate by improving website navigation, content flow, and speed.
      • Investigate user behavior using tools like heatmaps to understand where users are dropping off.
    • Test new strategies:
      • Run A/B tests for different types of content or calls-to-action to see what works better.

    6. Overall Benefit to the Website Owner

    This output and analysis are highly beneficial for the website owner because:

    1.    Improved Decision Making:

    • It identifies which groups of users (cohorts) perform better and why.
    • Data-backed insights help prioritize improvements.

    2.    Increased User Engagement:

    • Understanding user behavior allows you to improve engagement rates and reduce bounce rates.

    3.    Higher Conversion Rates:

    • By replicating strategies that worked for the high-performing cohort, you can improve overall conversion.

    4.    Identify Weaknesses:

    • The low-performing cohort highlights areas where you need to make improvements.

    5.    Better Resource Allocation:

    • Focus efforts (time, budget, and content) on strategies that are proven to work.

    Summary

    • The Conversion Rate by Cohort Chart compares performance between cohorts.
    • The November 2024 cohort performed the best, with a conversion rate of 59.3%.
    • The October 2024 cohort underperformed, with a conversion rate of 47.3%.
    • Actionable steps include replicating success from November and addressing issues from October to improve future performance.

    By following these insights, the website owner can retain more users, increase engagement, and ultimately grow their business.

    Tuhin Banik

    Thatware | Founder & CEO

    Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker..

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