User Behavior Analysis With Google Analytics

It’s really difficult to understand, what is the exact pattern of visitors who are converted or may convert in the future. After coming to your site, every visitor doesn’t convert, Even the conversion rate for B2B is sometimes less than 2%, now as a marketer we should be familiar with the pattern of  these  2% of visitors and for that we must segment them from the non-converted visitors, This can be easily done in Google Analytics. User behavior analysis can help marketers to plan some important strategies like-

• Which type of visitors is more likely to be converted?
• How much time do users who come with the intentions of purchasing, spend on your site?
• What is the average age of your visitors?
• Which type of visitors are considered in the remarketing list according to this data?

Moving further, let’s figure out the technicalities in analyzing the audience behavior.

Understanding User Behavior analysis to acknowledge which audience segments are accomplishing your business goals can help marketers with future targeting, ad messaging, and overall marketing. We are soon going to talk about a unique technique which will help you to understand your customers.

In Google Analytics, there are two system segments, namely, Converters and Non-Converters. Awareness about these two segments can help you understand:

• The behavior of users who converted compared to those who didn’t?
• The demographic characteristics of users who converted versus those who didn’t?
• An accurate pattern of users who converted or not after coming to your site?

Steps To Select System Segments In Google Analytics

You need to move on to your Analytics Dashboard first. Once there, go to Audience > overview. Proceed further following the steps.

1. On the top, click on “All Users” segment

2. From system view, select converters segment and then apply

3. Follow the same process(step 2) for Non-Converters Segments

   We are all set with the segments now. The next comes the step of the analysis.

Analyze The Pattern Of Visitor By User Behavior Analysis

Start with Audience section > click on the Overview > Checkout the highlighted metrics.

As expected, visitors who converted are more engaging than those who didn’t. They have a higher Pages/Session, Higher Average Session Duration, and Lower Bounce Rate.

We are going to use both the segments in some important dimensions to differentiate the user’s pattern-

1. New Vs Returning Visitors

Digging deeper, as we can see in the above image (Pie chart at left) that the Converters segment shows a much higher percentage of Returning Visitors than Non-Converters segment.

• In B2B, decision making is a long-term approach. So this percentage can be normal. This percentage is expected to be different for different sites. But returning visitors will always be ahead, especially in B2B.
• It could also indicate that all the Re-marketing efforts were successful in targeting existing users and getting them back to the site.
• The converted users are spending more time, visiting more pages as compared to the non-converting visitors

Users who didn’t convert tend to have visited only once. As in the report combined with their much shorter average session duration, after user behavior analysis there are some cases like either you are not targeting the right users or maybe your landing page is ineffective.

2. User Pattern According To Country

You can see that a much larger proportion of Converters visited from the US. Now if we go deep into it, check the last metrics (Ecommerce Conversion Rate). According to this most converted users are coming from the UK.

This is where you have to analyze your data as you can target more on the country which is having a high conversion rate & receiving more traffic from that country can increase more transactions.

3. Ads Campaign Performance

One of the most crucial segments of online marketing is Paid Ads. If your campaign is not reaching to the relevant customers, then you are wasting both time and money, especially. Creating campaigns in Google Ads is not tricky. But, to focus on how to select the relevant audience, what is the high-value time zone to display the ads, to get a maximum output is a real task. Thus, proper analysis is the only key to make your campaign better and more accurate. 

Now lets select both the segment and start analyzing the data. You can see all your campaign performance created in Google Ads in Acquisition > Google Ads > Campaign.

As you can see in the image, here is a list of campaigns comparing both the segments (Converter and Non-Converter). I have marked 2 columns –

• Sessions (1)
• No. of users (2)

Now, as I said, Non-Converter users and sessions are always high as compared to Converters. So you might be thinking that what should I analyze?

Pay close attention!

Check users section (box 1) analyze the difference between the percentage of the converted and non-converted users of every campaign.

For example– Focus on the first column (Sessions (1)) and compare campaign performance. Considering campaign 1 – You need to find the total percentage of non-converted visitors. So, for example, converted visitors are 80 and Non-converted are 329 then the total number of visitors who are not converted is approx 80% (Requires basic knowledge of mathematics).

Now you need to do this for all the campaigns and check which campaign are in the categories of high Non-converted percentage. This data will help you to recheck your campaign parameters and setup accordingly.

The same like this you have to do for all campaigns and then analyze which campaign receiving the highest percentage of non-converted visitors and take action on that and these changes will definitely show big impact on your campaign.

4. Cohort Analysis (Non-Converted Returning Visitors)

Marketing Analytics tells you what is working, what is not working, and how to adjust your marketing activities based on this feedback. Cohort Analysis does the same by focusing on the effect of each marketing activity or change in a specific audience in time.

A “cohort analysis,” then, simply allows you to compare the behavior and metrics of different cohorts over time. You can then find the highest-performing (or lowest-performing) cohorts, and what factors are driving this performance. Now go through the image. This is user retention group, after comparing both the data (converted and non-converted), we can see the highest number of visitors are returning visitors who are converted.

But, the interesting point is that there is some percentage of returning visitors who are still not converted. As you can see in the image, the yellow and orange shade percentage shows the user retention in the non-converted segment. So, if they are coming back to your site, then there are high chances that they can be converted. Let’s see how to retain them.

1. In the non-Converters segment, click on the down arrow and select copy.

2. Check the condition, according to this the visitor who hasn’t completed any goal (created by you in Google Analytics) and does not perform any transaction are considered in this segment.

3. Copy that segment and add one more condition (highlighted in the image) select user type that contains Returning Visitors, now you can see the visitors who are returning but still not converted (these conditions can be changed according to the different types of marketing fields), now as a marketer these users are showing more intention to convert as they are returning and non-converted now the point is how you can target them in your marketing list…? Very Simple!

   Build an Audience related to that segment and you can re-market them in the future by running ads or email marketing. This is a smarter way of remarketing with more accuracy and increasing the chances of conversions.

These two segments are really important if you need to understand the user activity on your site, there are other system segments also to Analyse the user and if that is not enough then you can create your own.

Data is really important resources to understand your customers, so keep analysing.