Google Analytics- When Are New Vs. Returning Visitor Ratios Useful-.png

One of the things I’ve criticized Google Analytics about in the past is the new vs. returning visitor ratio metric. This ratio tends to mislead marketers, especially marketers new to web analytics.

In aggregate, the ratio tells us nothing useful; as far as marketing objectives go, we want more of both. We want more new users AND we want more returning users.

Is there a time when these ratios might be useful, might give us some insight? Yes: when we’re looking at individual channel performance.

Let’s look at an example.

Here’s my overall site new vs. returning visitor ratio.

overallnewret.png

This is not helpful; this does not guide me or suggest what I should do differently. I can see that on a big picture level, 4 out of 5 of the visitors to my website are new, but without the context of whether traffic is increasing overall or decreasing overall, this doesn’t tell me much other than my site is attracting new visitors fairly well.

However, what if I apply some segmentation and look at channels such as email, social media, organic search, and referrals? In Google Analytics, we’d add these segments from the top segment menu, dropping channels in from either the System segments or our own custom segments.

Here are four of mine:

newret1.png

Above, I see that email marketing is a loyalty tool: 1/3 of the visitors it brings in are returning visitors. It brings in more returning visitors than any other channel. The same is true for social media: it brings significantly more returning visitors than referral traffic or organic search traffic.

This tells me that if I want to increase loyalty, if I want to nurture the audience I already have, I should focus my marketing and content efforts on email and social. If I want to increase new visitors, I should focus on search and referrals. If we’re pursuing a marketing technique like account-based marketing, this is essential insight.

Let’s dig deeper into social media:

newretsoc.png

We see above that LinkedIn drives more new than returning users, so even though it’s lumped into social, it behaves more like referral and organic search, an important distinction if we care about driving new visitors.

We also see that Twitter drives an astonishing amount of returning traffic. If I care about engaging my audience more, Twitter is the place to do it for me. Conversely, if I care about getting new audiences, Twitter may not be the place to be, not as much as LinkedIn.

These charts can now inform my social media strategy, helping me to understand what I should be doing on each channel.

Let’s kick it up a notch and look at these channels over the last year.

Channel Ratios

What we see above are new and returning ratios over time. Rather than just pie charts, we see the trends of each channel over time and glean insight from those trends. I have two concerns: email’s returning user growth is declining when it should be increasing, and organic search’s new user growth isn’t the strongest when it should be. This tells me I need to rethink how I use both these channels to improve their performance.

Let’s dig down even further. For channels which delivered at least 100 users, what did the year look like for new and returning users?

Users by Source

We see that organic search, despite the ratios in the line graph previous, is still a huge driver of both new and returning users; for returning users, Twitter, email, and Feedburner bring back people. Thus, in terms of prioritizing which channels to address, I should first fix organic search (largest impact), then Twitter, then email.

Drill down into each of your channels and understand what’s contributing to your website traffic, using the new vs. returning ratio. While we always want more of both, it’s helpful to dig into our traffic composition to gain more insight about how people are finding us on any specific channel. Once we understand new and returning user ratios and absolute numbers, we prioritize what to do better in our marketing.

Disclosure: this post was originally written in 2015 and has been updated several times with new data and new methods.


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