When Are Leads Born? How to Do Time Analysis of Lead Generation Using GA4 + AI

There are thousands of articles about WHY visitors become leads. We’ve written many ourselves.
But there aren’t many articles about WHEN visitors become leads.

Can you answer these questions?

• What day of week do my visitors become leads?
• What time of day do my visitors become leads?
• How many days does it take for my visitors to convert into leads?

But you have the data. There’s date and time data in GA4, but it’s in a format that looks like secret code. Analysis was very difficult. But now, AI makes it very easy.

In this article, we’ll show methods for discovering the speed and timing of your lead generation by combining GA4 and ChatGPT …or any model that lets you upload spreadsheets.

Lead generation by time-of-day and day-of-week

First, let’s find out when visitors tend to become leads using GA4 data. We’ll start by exporting the relevant data from a GA4 report as a CSV file. Then we’ll clean up the data and give it to the AI with a simple prompt or two.

You may notice that this is conversion analysis without using “key event” data. This will work for any website with a thank you page, even if conversion tracking isn’t set up!

Here is the process:

• Go to the Page path report
• Set a nice long date range
• Search for the URL of your thank you page
• Click the blue plus at the top of the first column to add a secondary dimension. Select “Date + hour”
• Export! That’s the little share icon in the top right. Download to CSV.

The report should look like this. Every row should be the thank you page with a date stamp.

Next we’ll clean up the file. We really just want the date and number of sessions. This is sufficient because every session where the thank you page was loaded is a conversion, so in this case, sessions are leads!

• Remove the top nine rows from the CSV. These are just comments.
• Remove all the columns except the date and sessions. They aren’t necessary.

So now you have a two column CSV file.

• Upload the file to your favorite LLM along with the following prompts:

I’m giving you a CSV file showing the date and time of conversions on a B2B lead generation website. Perform an analysis of conversions by day of week. Visualize on a bar chart.

Now you see which days of week your visitors became leads.

Perform an analysis showing conversions by time of day. Visualize on a chart.

Now you see what time of day your visitors became leads.

Create a single heatmap matrix showing conversions by both time of day and day of week.

Here is the output for that last prompt, which combines the day-of-week and time-of-day analysis. In this dataset, the middle of day and middle of week are when our visitors are most likely to become leads for our web development and website optimization services.
How to use this data:
Now that you know when your visitors are most likely to raise their hand and ask for your help, you may want to adjust your sales and marketing in the following ways.

• Send email campaigns and do outreach at peak times
• Adjust ad budgets, reducing spend at low-conversion times and increasing spending a high-conversion times
• Make sure sales reps are ready for rapid response during peak times

If you have enough data, you can add a filter to check conversion times from various sources. It’s possible that you’re paying for traffic at times when no one is converting. This is similar to discovering that paid traffic from mobile devices isn’t converting. Fix it quick!

AJ Wilcox, B2Linked “This is especially powerful if you’re running LinkedIn Ads that generate many conversions. People tend to interact with LinkedIn at defined hours of the day when they’re at work. So if you know that your LinkedIn Ads traffic converts best at certain times, you can run your ads only during those hours to take advantage of the best-converting times of the day. LinkedIn doesn’t offer a native scheduling tool, but there are several 3rd parties that do. We’ve been able to improve efficiency for many of our ad accounts based on this new intelligence.”

Content engagement by time-of-day and day-of-week

By changing the report, you can see the timing for all kinds of other interactions, including engagement on content. Here are some examples:

When your visitors subscribe…

Repeat the analysis using the email signup thank you page to see when your visitors subscribe to your newsletter.

Here’s what that analysis looks like for our GA4 data. No surprise: there are more signups when new content is published and promoted. Our newsletter drops on Thursday morning, so email subscribers peak at the same time.

This could work just as well for any conversion: donors to a non-profit website or applications to a job site.

When your visitors watch videos…

If you repeat the analysis but this time use Events report and search for “video_start.” The report will look something like this:

Conversion time lag analysis

Now that we know when visitors become leads, let’s ask the other time-related question: How long does it take for visitors to become leads?

For this, we’ll need a GA4 report that shows two dates: the date the visitor first came to the website (“First visit date” dimension) and the date they became a lead (“Date” the lead generation event was triggered). I’m also going to include where the visitor came from (“Session default channel group”) to see if the speed of leads varies by traffic source.

We need to use a GA4 exploration, rather than a report from the reports section, because we’re looking at too many dimensions. Here’s how to create a GA4 exploration with just the data we need.

1. Go to the explorations section
2. Click on “Free form” exploration
3. In the first column (“Variables”) click on the plus + sign next to DIMENSIONS
4. Search for and check the following dimensions: First visit date, Date, Event name and First user default channel group. Click Import
5. Also in the first column, click on the plus + sign next to METRICS
6. Search for and check the “Event count” metric. Click Import
7. In the second column (“Settings”) drag the “First visit date” and “Date” into the ROWS box
8. Drag the “First user default channel group” into the COLUMNS box
9. Drag the “Event count” into the VALUES box
10. In the FILTERS box, click to add a filter that shows only leads. On our website, we named the lead generation event “contact_lead” so our filter is “Event name contains contact_lead.”

Optional: If there are any columns or rows you don’t want or need, right click and select “Exclude selection” to add a filter that removes it from the report.

Whew! That was a lot of steps. It’s a good thing we didn’t make a screenshot for every step! But here’s one for the completed report, with all of the settings highlighted. You can see that I removed irrelevant rows and columns (by right clicking to “Exclude selection”) and each of those filters appears in the bottom of the settings column.

This GA4 explorations shows the traffic source, the date of the first visit and the date for every lead generated.

• Export the report by clicking the download icon in the top right
• Open the CSV file and clean it up: remove the top comment rows, tidy up the headers above the columns, etc. You want a final file with one column for the date of the first visit, another column for the date of the conversion, and columns for each traffic source.
• Upload the file to AI with the following prompt:

This CSV file is an export from GA4. The first column is the date the visitor first visited.
The second column is the date the visitor converted into a lead. Both are in YYYYMMDD formats.

First, remove outliers.
Then perform a conversion timing analysis showing how long it takes from first visit to becoming a lead.

The AI will give you a summary of the number of days from the first visit that your leads took to convert. If you’ve read this blog before, you probably know that we like to have AI draw marketing charts. Here’s the prompt to make a visual for this lead generation time lag analysis.

Plot on a single bar chart with groups of bars, one for Organic Search and one for Direct.

The chart should look something like this:

I’ve done this in several accounts and found that most leads convert on their first visit. I’m not surprised. This isn’t the only clue that high-intent visitors move quickly. If you create a GA4 path exploration with the thank you page as the end point, you may find that the typical lead goes from the homepage, straight to the lead form.

Even if your sales process is long and consultative, the lead generation process might look quick and transactional. The takeaway? Work hard on your homepage. Yes of course. But maybe this is an issue with last-click attribution.

For another perspective on these seemingly instant leads, I reached out to digital strategy pro, Ashley Faus. In her experience, this only looks like a short path. But in reality, you may have been nurturing this person for years.

Ashley Faus, Head of Lifecycle Marketing, Portfolio at Atlassian “The fact that many leads convert on their first visit shows that your marketing in other channels is working. When people are ready to buy, they’ll come to you! This data shows that “nurturing” a prospect is not a single channel process. Nurturing happens in the places where your audience spends time. That might be on the weekly podcast that you produce, LinkedIn posts from an internal thought leader, or the monthly newsletter. Marketers need a holistic approach to where, when, and how they nurture prospects, because it’s not just happening in the ‘official nurture campaign’ that you set up.“

So insightful. I think she’s right for a lot of these leads. They may look fast in Analytics, but that prospect may have been planning that visit to your website for months. They may already know all about you.

It all reminds me of this quote I read in Velocity’s new B2B Brand Manifesto…

Joe Chernov, CMO at Pendo “Show me a company with runaway demand gen costs and I’ll show you a company with limited brand awareness.“

Bonus! Sales timing analysis

Let’s do one more. Why not, right? There’s room for a few more pixels on this page…

For this last method, we’ll go farther down the funnel, out of marketing and into sales. For this, we’ll use data from our CRM. Unlike GA4 data which is anonymous, the CRM is filled with company names, people names and financial data. So we’ll need to be especially careful with the exports.

Never upload sensitive data to an AI, for obvious legal and security reasons.

Create a report that includes the date of the lead and the sales closing date. Use a very long date range (several years) if possible. Export the report, remove any unnecessary columns and upload to the AI. Here’s the prompt for a monthly analysis. You can see that I’m explaining to the AI which column header has which data.

I’m giving you data from a CRM that shows when leads came in (“Conversion date”) for leads that turned into closed deals (“Closing date”). Perform an analysis showing the months-of-year and day-of-month that leads were most likely to become new clients.

Create a bar chart comparing the date in which the leads are generated to the dates in which deals close.

ProTip! If your report includes the revenue of each deal, you analyze deal value rather than deal volume.

Here’s what the report looks like for four years of our lead generation and sales data. You can see that some months are good for leads, but slow for sales. For other months, leads are slow but sales are just fine.

Why the gap between leads and sales? It’s partly a function of the sales cycle. If you have a consultative sales process for a high-consideration service with multiple decision makers (which is very common in B2B), then deals take time to close.

If nothing else, this was a fun little demo of how “Date” data from GA4 or your CRM can be used together with AI to analyze and visualize. Maybe you’ll find other, similar ways to do AI-powered analysis with data from other tools.

The insights are limitless.