Using Regression Analysis to Understand Your Blog‘s Traffic Growth

As a data-driven content marketer or business owner, tracking and analyzing your blog‘s traffic growth is crucial for making informed decisions about your content strategy and promotional investments. While keeping a pulse on daily or weekly traffic fluctuations is important for catching issues or capitalizing on trending topics, taking a step back to assess your blog‘s overall growth trajectory can provide powerful insights to guide your long-term strategy.

One highly effective method for analyzing your blog‘s traffic growth over time is regression analysis. In this post, we‘ll break down exactly what regression analysis is, the different types of regression models, and how you can easily run this analysis yourself using Excel. We‘ll walk through an example together so you feel confident testing this on your own blog traffic data. Let‘s dive in!

What is Regression Analysis?

In statistics, regression analysis is used to estimate the relationship between a dependent variable and one or more independent variables. When it comes to analyzing blog traffic, regression allows us to understand how traffic (the dependent variable) has changed over time (the independent variable).

By plotting traffic data points on a graph and calculating a "line of best fit", we can see the general trend or trajectory of growth. Regression analysis not only shows us whether traffic has been increasing or decreasing over a period of time, but how quickly it has been changing based on the shape of the line.

Types of Regression Models

There are a few common types of regression models that show different traffic growth trends:

  1. Linear regression
    A straight line indicates steady, consistent growth over time. With linear growth, you‘re gaining traffic at the same rate throughout the time period.

  2. Exponential regression
    A sharp upward curve signals that growth is accelerating; you‘re gaining traffic at a faster and faster rate as time goes on. The curve will get continually steeper.

  3. Logarithmic regression
    This curved line starts steep but flattens out over time, meaning growth is slowing and potentially hitting a plateau. You may be reaching a saturation point.

When running a regression analysis, the model with the "line of best fit", or the shape that most closely matches the path of your actual data points, is likely the most accurate representation of your blog‘s traffic growth. We‘ll talk more about how to determine this later on.

While these are three of the most common regression models for growth, there are other shapes that may arise like polynomial regressions with curves and dips. The key is to identify the model that aligns with your real-world data.

How to Run a Regression Analysis (With Example)

Now that you understand conceptually how regression analysis works, let‘s walk through how to actually run one yourself. We‘ll use Excel since it‘s widely accessible and has built-in tools that make regression analysis quick and easy.

We‘ll use a hypothetical set of monthly blog traffic data, but you can export and use your own data from Google Analytics or HubSpot (if you‘re a HubSpot customer, you can find the traffic report for your blog under the "Reports" dropdown).

Here‘s the step-by-step process:

Step 1: Plot your data as a scatter plot.

First, you‘ll organize your data in two columns: "Month" (or whatever time interval you‘re using) and "Traffic". Highlight both columns, click "Insert" and select "Scatter" from the dropdown of charts.

This will generate a scatter plot with a single dot for each time period, giving you a visual representation of how traffic has changed over time simply by looking at where the dots fall. Are they rising steeply or gradually tapering off?

Step 2: Add trendlines.

Here‘s where the regression magic happens. Right click on any data point and select "Add Trendline". This will open a menu where you can specify the type of regression you want to test.

Start with "Linear", which is the default. Once the line appears, right click it and select "Format Trendline". Under the "Trendline Options" tab, check the box for "Display R-squared value on chart".

Repeat this process two more times to add an exponential regression curve and a linear regression curve to your scatter plot so you can compare all three models visually.

Step 3: Evaluate the R-squared for each model.

The R-squared value measures the percentage of variation in the dependent variable (traffic) that can be explained by the independent variable (time). In regression analysis, the R-squared value determines how well the model fits your data. The higher the R-squared value, the better the model explains the relationship between traffic and time for your data set.

As a general rule of thumb, an R-squared of 0.75 or higher indicates a solid model for making predictions, while a score below 0.5 means the model isn‘t a great fit. Compare the R-squared values for the linear, exponential, and logarithmic trendlines you created. Go with the shape of the model that produces the highest R-squared to select the regression type that most accurately matches your blog‘s traffic growth.

Step 4: Interpret your growth curve.

Let‘s say our example blog data produces the following R-squared values:
– Linear: 0.72
– Exponential: 0.89
– Logarithmic: 0.64

With exponential having the highest R-squared at 0.89, we can conclude that this blog‘s traffic has been growing at an exponential rate over the time period measured. The curve is your "line of best fit" and represents the trajectory and speed of growth.

A sharp upward curve like we see with exponential regression is great news – it means growth is accelerating over time. Each time period you‘re gaining more and more new traffic compared to the previous period. This trajectory would suggest that your current content strategy and promotion tactics are working very well, and you‘d likely want to double down on what you‘re doing.

In contrast, if the logarithmic model was the best fit, it would indicate a slowdown in growth that may continue to flatten out if you don‘t make adjustments. In this case, you‘d want to dig deeper into what has changed and consider experimenting with new content formats, topics, or distribution channels to reignite growth.

When To Use Multiple Regression Analysis

The example above focused on a simple linear regression model with just two variables: traffic and time. But what if you suspect there are other factors besides time that are significantly impacting your blog‘s traffic? This is where multiple regression analysis comes into play.

Multiple regression measures the relationship between a dependent variable (traffic) and two or more independent variables (e.g. time, number of new posts published, advertising spend, referral sources). Basically, it allows you to test if and how much impact each factor has on your blog‘s growth.

Let‘s say you‘re investing in paid social ads and sponsored content to drive traffic to your blog. You‘d probably want to know how much of your blog traffic growth can be attributed to those efforts vs. organic sources. Multiple regression analysis can help you isolate and measure those relationships.

To run a multiple regression analysis in Excel, you‘ll plot each independent variable in its own column beside your traffic data. Then, instead of charting a scatter plot and trendlines, you‘ll use the "Data Analysis" toolpak (found under the "Data" tab) and select "Regression" to generate a summary output with key regression statistics for each variable. The adjusted R-squared will indicate how well the model as a whole explains variation in blog traffic.

Regression Analysis Best Practices and Limitations

We‘ve covered the basics of how to use Excel to run a single and multiple variable regression analysis on your blog traffic data. Before you start plugging in numbers, here are a few things to keep in mind:

  1. Use clean, accurate data: The quality of your analysis depends on the quality of the data you put in. Make sure to QA your data export and remove any outliers or inaccurate data points that could throw off the model.

  2. Gather sufficient data points: To create a reliable model, you‘ll typically want at least 2-3 data points per independent variable, if not more. For example, if you‘re measuring blog traffic monthly, you‘ll want to include at least two years‘ worth of data in your analysis (24 data points). The more data you have, the more statistically significant your results will be.

  3. Correlation vs. causation: If you find a strong regression model, it‘s easy to jump to conclusions about causation, but remember: regression measures correlation, not causation. Just because two variables trend together, it doesn‘t necessarily mean one is a significant cause of the other.

  4. Keep external factors in mind: Regression analysis can tell you a lot about the relationships in your data set, but it can‘t account for all the context that may impact your blog‘s growth. Changes to your overall brand awareness, website UX, or even the competitive landscape can impact traffic and shouldn‘t be overlooked.


Regression analysis is a powerful tool for understanding your blog‘s overall traffic growth trends and trajectory. It allows you to visualize your growth curve, quantify the rate of change, and test relationships between key variables like time, content output, and promotion.

By regularly monitoring your R-squared values and learning to interpret the different regression shapes, you can keep your finger on the pulse of your blog‘s health and progress. Regression insights can help you catch issues (like stalling growth) early, make a data-backed case for more budget and resources, or even predict where your traffic is headed if all else remains constant. Most importantly, understanding your growth empowers you to keep doing what‘s working and adjust your approach when it‘s not.

While this tutorial focuses on blog traffic growth, keep in mind that regression analysis can be applied to many other important business metrics like lead flow, revenue, and customer churn. By adopting this analysis technique in your day-to-day work, you‘ll be well on your way to becoming a more data-driven marketer and positioning your brand for success.