Analytics

Website Analytics Simplified: The Power of Page Session Tracking

Analytics

Remember the classic question: “What’s the one thing you’d bring with you if stranded on a desert island?” Let’s apply a similar thought experiment to website analytics. If you could only have one report to manage your website, what would it be? For me, it’s a trending report that shows page session tracking by month—as illustrated in the table below.

Why Sessions Is the Right Metric

In this scenario, we’re not dealing with an eCommerce website, so metrics like revenue, orders, or units don’t apply. I also wouldn’t choose user or visitor metrics because frequent cookie deletions make unique or returning user tracking unreliable.

The sessions metric stands out as the most reliable option. It aligns closely with a “jobs-to-be-done” analysis, helping us understand how visitors are using the site. A single visitor may have multiple reasons (or “jobs”) for visiting your website in a day, and sessions capture this behavior effectively.

Why Page Title Is the Right Dimension

Understanding how your content performs is critical, and the Page Title dimension offers two distinct advantages:

  • It helps you evaluate the SEO naming of your pages alongside performance metrics.
  • It’s more concise than using the full Page URL, making reports cleaner and easier to read.

For instance, in my reports, I’ve removed the “| Marketing with Dave” suffix from all non-homepage titles for clarity.

Insights from Page Sessions Data

While data tables may not be as visually appealing as charts or graphs, they often provide deeper insights. Below are five key takeaways from the data on my website, which I’ve recently started driving more traffic to:

  1. Unusual Traffic Spikes
    The anomaly of 551 sessions on the homepage in February—and a smaller spike in March—caught my attention. After investigation, I discovered it was caused by unwanted referral traffic from Poland, which affected many websites. While I’ve filtered this traffic out in my reports, I’m showing the raw data here for transparency.
  2. Steady Growth in Monthly Traffic
    The data reveals a slow but steady increase in monthly sessions, with notable growth starting in October. A closer look shows that in November, traffic was driven by my Christmas book promotion, and in December, a blog post on SWOT analysis gained traction. This suggests that I should build out more suporting content covering SWOT
  3. Home Page Dominance
    Approximately 73% of my website traffic lands on the homepage. However, this is somewhat misleading because my homepage features a pinned blog post. Recognizing this, I’ve decided to implement a dedicated home page (coming soon).
  4. Tracking New Page Performance
    My LinkedIn book page is a great example. The data shows when the page went live, how it performed in its first month, and how it has fared since. Promotions and marketing efforts have influenced traffic spikes, but this report helps identify when a page’s performance starts to decline, prompting decisions like optimizing existing content instead of creating new pages.
  5. SEO and Visitor Interest Opportunities
    Reviewing page titles alongside session data naturally leads me to consider whether page titles could be improved for SEO or to better capture visitor interest.

Why This Report Matters
By consistently monitoring page-level session data, you can make informed decisions about content creation, optimization, and overall website strategy. This single report combines quantity (total traffic) with quality (specific page performance), making it an invaluable tool for managing your website effectively. Another way to enhance this report is by including the Page Type dimension, which categorizes pages into types like blog posts, navigation pages, or the homepage. This addition provides deeper insights into which types of content are driving the most traffic and informs where to focus optimization efforts.

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Free and Easy Ways to Export Google Analytics 4 Data to Excel or Google Sheets

Analytics

Rarely do I want to keep my data inside the source where it originates. This is absolutely the case with my Google Analytics data. I needed a solution that met the following criteria:

  • No cost: Premium tools like Coupler were not an option.
  • Simplicity: I wanted to avoid working directly inside the Google Analytics platform and Looker Studio lacked the needed flexibility.
  • Automation: Each report needed to auto-refresh on the first day of every month.

After exploring multiple options, I discovered that Google Sheets, combined with the SyncWith extension, met my needs perfectly. SyncWith offers 35 monthly refreshes for free, which is more than sufficient for my requirements. Setting this up was fairly straightforward taking minutes, not hours. Here are the steps I took to get this implemented using the Google Analytics add-on in Google Sheets.

Why Export Data from Google Analytics 4?

Google Analytics 4 (GA4) is a powerful platform, but its interface can be overwhelming for non-technical users. Exporting your data to a familiar tool like Excel or Google Sheets offers several advantages:

  • Customization: Analyze and visualize data in a way that suits your needs.
  • Ease of Use: Simplify complex data for stakeholders.
  • Offline Access: Work with your data without relying on internet connectivity.

Step-by-Step Guide to Installing the Google Analytics Add-on in Google Sheets

Step 1: Open Google Sheets

Go to Google Sheets and open a new or existing spreadsheet.

Step 2: Access the Add-ons Menu

In the top menu, click on “Extensions”. Select “Add-ons” and then click “Get add-ons”.

Step 3: Search for the Google Analytics Add-on

In the Google Workspace Marketplace, type “Google Analytics” in the search bar.

Step 4: Install the Add-on

Click on the Google Analytics add-on in the search results. Click the “Install” button. I chose the SyncWith extension which has a higher rating and usage than the one from Google.

Step 5: Grant Permissions

You will be prompted to grant permissions for the add-on to access your Google Analytics account. Click “Continue”, sign in with your Google account, and allow the required permissions.

Step 6: Access Google Analytics in Google Sheets

Return to your Google Sheet. Click on “Extensions” and select “Google Analytics”. Choose “Create New Report” to start pulling data.

Step 7: Create a Report

Follow the prompts to set up a report by selecting your Google Analytics account, property, and view. Choose the metrics and dimensions you want to include. Click “Create Report” to generate the data in your Google Sheet.

Step 8: Export to Excel

Once the data is in Google Sheets, go to File > Download and choose Microsoft Excel (.xlsx) to export the data.

Final Thoughts

Finding the right solution for exporting GA4 data can take some trial and error. Whether you choose the Google Analytics add-on or SyncWith, both options are cost-effective and efficient. By sharing my experience, I hope to save you time and help you unlock the full potential of your Google Analytics data.

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Brag-Worthy Audible Listening Stats: A Case for Customer-Centric Metrics

Analytics, Books, Case Study

We all have those brands, products, and services we love so much that we naturally become advocates for them. These are the companies that deliver great value, excellent service, and memorable experiences. We don’t share because we have to—we share because we want others to enjoy what we’ve experienced.

For me, Audible is one of those brands. I’ve been a loyal customer since 2004, back when audio books were still niche and long before Amazon realized what an amazing acquisition they would be. For 10+ years I drove 3.5 hours each way over the weekend twice a month to spend time with my three daughters. I churned through a lot of audio books.

Every great company has one or two key metrics that measure customer commitment, usage, and loyalty. For hotels, it is nights stayed and for airlines, it is miles flown. For Audible, I suggest hours listened and books completed are important to both them and their listening audience.

Audible does show total listening time in their app, but here’s the problem: the information isn’t shareable, and it’s not easy for customers to digest or feel proud of. For instance, seeing 4 months, 20 days, 13 hours, and 20 minutes of listening time on my stats page doesn’t evoke any emotion. It’s too abstract, like trying to imagine what a trillion dollars looks like.

What if Audible transformed this into something simpler and more meaningful? An image like the one below could present the data in a way that feels personal, shareable, and impressive. Perhaps, I could even see how this compares to other Audible listeners. Similar to Spotify or Pocket letting you know you are in the 1% of listeners/readers. This easy-to-digest summary is something I would want to brag about and show it off.

Why This Matters
Giving customers a clear, visually engaging way to understand and celebrate their achievements builds loyalty and amplifies word-of-mouth marketing. When customers feel proud of their usage, they’re more likely to share it with others, introducing new audiences to the brand.

Audible, you’re already doing so many things great—now make it easier for us to brag about you.

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Marketing Operations Analytics

Analytics, Operations

I have listened to more than 13,000 podcasts, mostly focused on digital marketing. I recently came across a podcast called DemandGen Radio. Dave Lewis is the host and was reading a chapter from his Manufacturing Demand book. Interesting that I have had this book in my want to read list in Goodreads since 2013. If this is really the #1 book on lead management as is stated on the cover, a gross error on my part for not getting to it sooner.

So far, I have only listened to two episodes, #260 and #261, that was a two-parter called Marketing Analytics: Keeping Score of Your Success. Rarely do I take notes listening to a podcast, but this was brilliant and so much value was shared. I am sharing my notes and thoughts in hopes it’s beneficial to others.

It seems like marketing is too often trying to prove they deserve a seat at the adults table instead of being at the kid’s table making arts and crafts. Perhaps that leads to oversharing when we finally get the opportunity, or sharing vanity metrics, or just not speaking the language of the boardroom. Instead of sharing marketing activities including impressions, likes, or our recent ribbon win for the prettiest booth, let’s share metrics that lead to financial outcomes. There are three major types of marketing analytics or key performance indicators (KPIs).

The first set of analytics metrics are Executive KPIs and they need to measure the entire demand generation spectrum.

Executive KPIs

  1. Marketing sourced leads and opportunities
  2. Marketing contribution to revenue
  3. Marketing’s influence on opportunities and revenue

The second set of analytics metrics are Demand Funnel KPIs and they measure the velocity and efficiency of our demand funnel.

Demand Funnel KPIs

  1. How many prospects at each stage of the funnel
  2. Conversion rate between each of these stages
  3. Average time in each stage – this tells us the velocity of the demand funnel
  4. Lead scoring distribution – how many A, B, C, D, and E leads and does this look anything like a bell curve
  5. Campaign performance – number of leads, what channel or lead source, opportunities, revenue

The third set of analytics metrics are Campaign and Asset Performance KPIs and they measure the success of our assets driving leads to a closed stage.

Campaign and Asset Performance KPIs

  1. Use of our assets – tracking downloads for PDFs or how much of the video was watched like 25%, 50%, 75%, 100%
  2. Closed/won asset utilization – what assets get read by prospects who eventually buy

The three most important things to track with any form submission are the channel, lead source, and offer. Probably the most common way to implement this tracking is to capture the UTM tracking parameters in the URL query string and make sure they are passed as hidden fields in the form submissions. That form submission is tied to the Salesforce campaign object or whatever makes sense using another CRM. It’s important to make sure you can track every stage and really everything from that first click to the close of the sale. Until you know you are accurately measuring from click to close, you have a leaky funnel and nothing should be shared until you have confidence in your data and know you won’t lost trust.

The closing part of the podcast shared the four Cs.
1. What you can count – This reminds me of two quotes. First by W. Edwards Deming who said “If you can’t measure it, you can’t manage it”. Second by Lord Kelvin who said “If you can not measure it, you can not improve it”. The last thought is one I hear often, just because you can count/measure/track it doesn’t mean you should.
2. What counts – Although the Executive KPIs clearly cover what counts, there are other metrics that we want to track within our marketing group like micro conversions. Not everything tracked needs to be shared.
3. What you can count on – how important it is to trust your data so others trust you. When trust is lost, just about all is lost.
4. How you communicate it – often marketing needs to do a better job at marketing marketing. We could learn something from our sales colleagues in sharing what is working and that we are critical to the company’s success.

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Preparing to Migrate to Google Analytics 4 (GA4)

Analytics


There has been plenty of emotions leading up to Google Analytics 4 or GA4 being released and the increased pressure to make sure we are each ready. I finally decided to try to make sense of Google Universal Analytics and GA4 and here were my key notes in case others find this helpful.

  1. Dates – Universal Analytics (UA) deprecated July 2023 and UA 360 now deprecated October 2023.
  2. Users – same term used in GA4 but it means active users instead of total users.
  3. Model – UA is session-based (a session was a group of user interactions) where GA4 is an event-based model.
  4. Engaged Session is the count of sessions that lasted longer than 10 seconds, or had a conversion event, or had two or more screen/page views. This will replace the pages per session metric.
  5. Average Engagement Time Per Session is the amount of time the user is actually engaging with the page and is the page on the primary window being viewed on screen. This will replace the average session duration metric.
  6. Engagement Rate is the ratio of engaged sessions relative to total sessions. This will replace the bounce rate metric although it can still be calculated as the inverse of the engagement rate. 100 total sessions with 15 of them being engaged sessions results in a 15% engagement rate.
  7. Four Categories of Events
    a. Automatically collected events like user engagement, in-app purchases, and Firebase app interactions.
    b. Enhanced measurement events (change in user interface; no code changes required) like page views, scrolls, form interactions, and video engagements.
    c. Recommended events that have predefined names and parameters like online sales and user behavior.
    d. Custom events that you define and create when existing events don’t exist.
  8. Segments – both in UA and GA4 you can compare up to four segments. Types of segments in GA4:
    a. User segments – subsets of users who engaged with your site/app like users from a page or channel.
    b. Event segments – subsets of events that were triggered on your site/app like purchase events.
    c. Session segments – subsets of the sessions that occurred on your site/app like a particular advertising campaign.
  9. Segmentation Conditions tell analytics what data to include in or exclude from the segment. There are three segmentation conditions:
    a. Dimension conditions like demographics, geography, and technology.
    b. Event conditions about particular details on one or more events. This is new to GA4.
    c. Metric conditions based on predictive metrics like an in-app purchase probability is above the 90th percentile.
  10. Attribution Modeling is assigning credit for conversions to different ads, clicks, and other factors. There are three types of attribution models available in the Attribution reports:
    a. Cross-channel rules-based model ignores direct traffic and attributes 100% of conversion value to the last channel that the customer clicked through or engaged view through for YouTube before converting. Other cross-channel rules-based models include:
    i. Cross-channel first click – all conversion credit to first channel that a customer clicked.
    ii. Cross-channel position based – attributes 40% credit to first and last interaction and remaining 20% credit distributed evenly to middle interactions.
    iii. Cross-channel linear – distributes credit for conversion equally across all channels a customer clicks.
    iv. Cross-channel time decay – gives more credit to touchpoints that happened closer to time of conversion. Uses a 7-day half life so a click 8 days before conversion gets half the credit of a click 1 day before a conversion.
    b. Ads-preferred rules-based model – attributes 100% conversion value to the last Google Ads channel that the customer clicked before converting. If there is no Google Ads click, attribution model falls back to cross-channel last click.
    c. Data driven attribution – uses machine learning algorithms to evaluate converting and non-converting paths. Distributes credit for the conversion based on your account data for each conversion event.
  11. UTM parameters – there are two new UTM parameters in GA4. See https://support.google.com/analytics/answer/10917952?hl=en#zippyhttps://support.google.com/analytics/answer/10917952?hl=en#zippy for more details.

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