When are new vs. returning visitor ratios useful?

One of the things I’ve lambasted over the years in Google Analytics is the new vs. returning visitor ratio. In aggregate, it tells you nothing useful, because as far as marketing objectives go, you want more of both.

However, is there a time when these ratios might be useful, might give you some insight? The answer, unsurprisingly, is yes: when you’re looking at individual channel performance. Let’s look at an example.

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


This is not super-informative. 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?


Now we’re getting somewhere. Above, I see that email marketing is a loyalty tool: 1/3 of the visitors it brings in are returning visitors. 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, I should focus on email and social. If I want to increase new visitors, I should focus on search and referrals.

Let’s dig deeper into social media:


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.

Drill down into each of your channels and understand what’s contributing to your website traffic, using the new vs. returning ratio. While you always want more of both, it’s helpful to dig into your traffic composition to gain more insight about how people are finding you.

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IBM Watson Analytics Does Twitter Hashtags

Disclosure: IBM sponsored my attendance at their Analytics For All event. This post was written prior to the event.

I’ve written and spoken about IBM’s Watson (yes, the supercomputer that won Jeopardy) Analytics, and how it’s affordable Big Data analytics for marketers (for most small and medium businesses, $30 a month). IBM has upped the ante with its integration of Twitter hashtag analysis, which most social media marketers will find fascinating.

You begin by choosing Twitter as a data source:


From there, you can use any of the modules Watson is known for. As I spoke recently at Social Media Success Summit, I decided to use that hashtag to explore potential queries:


For example, how does sentiment influence retweets?


Above, we see that Watson’s sentiment detection shows that no, sentiment doesn’t seem to influence retweets.

Is there a pattern in retweets by sentiment over time?


So what does drive retweets? We jump over to the prediction module to ask:


For the #SMSS15 hashtag, geography and follower count are what influence retweets most, albeit weakly.

Watson’s Twitter import has restrictions; for the basic account, you’re limited to 25,000 tweets at a time, so if you wanted to tackle something like the 2016 presidential election, you’d need to upgrade. However, for most marketers, Watson will do fine out of the box. Give it a try with the hashtags you care about!

Disclosure: IBM sponsored my attendance at their Analytics For All event by paying my travel and expenses to the event. This post was written prior to the event. Full disclosures including potential financial conflicts of interest here.

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Exclude marketers for better social media influencer analytics

In social media marketing, we often want to identify social influencers to understand who we need to be interacting with. However, our fellow marketers often obscure the landscape by filling it with marketing and promotion. Do a simple search in the social media monitoring tool of your choice and you’ll likely see 500% more promotion than conversation on public channels.

Here’s an example chart of conversations in social media about Google Analytics, with everything and with marketing messages taken out.

GA with and without marketing.jpg

What a huge difference. Marketing is clogging the airwaves.

Yet when we hear common social media advice, one of the most frequently repeated pieces of advice is to engage, engage, engage with our communities. Top influencers engage often.

Wouldn’t it be nice to know who was actually engaging, versus who was just mindlessly promoting all the time? What if we could dig into just the conversations and exclude the marketers from the party?

This matters more than you think. In a very, very simple word concordance (the counting method that forms the basis for word clouds), take a look at the difference in importance between words marketers use versus words used in non-promotional conversation:

Understanding meaning.jpg

The orange bars are the words that people use in conversation about Google Analytics. The blue bars are what marketers use, most often in promotional content. If you’re trying to reach influencers who engage, but you target your outreach using search terms that are blue bars, who are you going to recruit? Who are you going to reach? You’ll get marketers.

Bad marketing ruins everything.

Luckily, any decent social media monitoring tool should support boolean logic, boolean queries (such as OR, AND, NOT queries) that can help weed out your fellow marketers to identify actual engagement. In the monitoring tool of your choice, build in exclusions for:

  • Links and URLs
  • Sharing requests
  • Shares
  • Appeal language/calls to action

What you’ll be left with are the actual conversations.

For example, the above bar chart was generated with two queries, the first with everything:

(“google analytics” OR “googleanalytics” OR “@googleanalytics”)

and the second, without marketing:

(“google analytics” OR “googleanalytics” OR “@googleanalytics”) NOT (“pls share” OR “RT” OR “http” OR “https” OR “get your” OR “download now” OR “click here” OR “whitepaper” OR “webinar” OR “sign up” OR “subscribe now”)

In short, take all the marketing knowledge you have, all the best practices you’ve collected – and search for the opposite. Remove them. Clean them out. What you’ll be left with is actual conversation.

Apply this to your searches, to your conversational analysis, and most especially to your influencer identification, and you’ll see drastically different results for who is really influential in the industry or topic of your choice.

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