Analysing User Behaviour: Frequency and Recency

2019/04/01 08:00

analytics user behavior

Wondering how loyal your website visitors are? These stats will tell you.

Google Analytics is a platform that’s rich with data, providing businesses with key information on how users interact with their websites.

However, your GA data is only as valuable as how well you’re able to interpret it. Regardless of your role in your organisation, it’s always helpful to know how to interpret the data that appears in your GA reports.

Today, we will be looking at frequency and recency and why these stats are important.

Frequency & Recency: A Brief Definition

Frequency and recency will give you a good idea of how loyal your website visitors are.

Frequency refers to how often visitors return to your website. By using the calendar section in your dashboard, you can set a specific time frame to determine how often visitors returned within a specific period.

Recency shows when last a specific visitor came to your website. The recency metric is measured in days.

Where to Find Recency & Frequency Data

Next, let’s look at where you would find this data.

Step 1: Once you’ve logged in to Google Analytics, click on Audience > Behaviour > Frequency & Recency

setup user behavior

Step 2: By default, you will see ‘Count of Sessions’ data, which is your Frequency data. Count of Sessions shows the order of visitors sessions, the total number of sessions within a specific date range and how many pages were viewed during each session.

Frecuency Google Analytics

Step 3: To view Recency data, click on the Days Since Last Session tab at the top of the dashboard. You will now see data on how many days passed between each user session, the total number of sessions within a specific date range and how many pages were viewed during that session.

Interpreting Your Recency & Frequency Stats

Overall, recency and frequency will offer insights into the return value of your website. Since every website is different, there are no specific benchmarks that you can use to compare your data.

A website that produces high volumes of content wants a high frequency and low recency. However, a site that is simply providing users with general information such as their operating hours won’t need to be too concerned with low frequency. An eCommerce website would want their frequency and recency stats to fall somewhere in the middle since conversions don’t always happen during the first visit.

Taking It Further Using Segments

To really make your frequency data valuable, you will need to incorporate segments into the mix.

Segments refer to specific audience groups, allowing you to discover how different types of customers are interacting with your website. Segments can be based on data such as age and location so that you can optimise your conversion funnel accordingly.

If you view your Frequency and Recency report again, you will see that GA bases it on All Users. If you would like to create segments, here are the steps that you would follow:

Step 1: Once you are logged in to Google Analytics, click on the Admin icon in the bottom left-hand corner of the screen.

Step 2: In the last column, scroll down until you see the Segments menu item. Click on Segments to start creating them.

Step 3: Click on the red +New Segment button. You can now name your segment and select your criteria. Under the Behaviour tab, you can segment users based on how often they visit your website.

Step 4: Once you are happy with the segment click Save. You can continue setting up additional segments or navigate back to your reporting dashboard where you will be able to apply your newly created segment.

For example, if you had to click on the Acquisition menu item, you will have the option to apply a segment at the top of the page. You will now be able to see how your frequent customers compare to your average customer and what changes need to be made to your site to increase conversions based on these criteria.

This reporting adjustment is basic and straightforward but it can provide your marketing department with valuable data that can ultimately be used to determine who is converting and how long the average conversion takes.