Marketing Day: IAB Tech Lab launches ads.txt, Tobii acquires Sticky & more

Here’s our recap of what happened in online marketing today, as reported on Marketing Land and other places across the web. From Marketing Land: Enterprise Call Analytics Platforms: A Marketer’s Guide — updated for 2017 May 22, 2017 by Digital Marketing Depot The flood of mobile calls to US…

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Enterprise Call Analytics Platforms: A Marketer’s Guide — updated for 2017

The flood of mobile calls to US businesses continues unabated, changing the way enterprise brands view the telephone as an inbound marketing channel. It is also now changing the way brands view the telephone as an inbound marketing channel. As consumers increasingly use their smartphones to…

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How to Run a Cohort Analysis in Google Analytics

Traffic and page views are nice.

But they’re limited. In a few ways.

Site wide traffic looks nice on a blog post or meeting with your HiPPOs. But it’s not actionable. And it doesn’t tell you what’s going on beneath the surface.

For example, you have no idea if those users are returning. If they’re subscribin’ or buyin’. Or how they compare to peeps from a year ago.

In order to find out that detailed info that ultimately moves the needle, you need to dig a little deeper. And you need to be able to view these basic metrics through a more detailed lens that includes segment information.

Google Analytics cohort analysis tool can help. Here’s what it is, why it’s important, and how you can run your first cohort analysis report today.

What is a Cohort Analysis?

analytics-charts-orange-background

A cohort is “an ancient Roman military unit, comprising six centuries, equal to one tenth of a legion.”

Wait. That’s not right. Is it?

Ohhhh. It’s the second one.

cohort-definition-google

My bad. A cohort is simply a grouping; a subset of people brought together because of a similarity or shared value.

Think of a retail store. You have a cohort of customers who bought in the last week. And another that bought this same exact week, but last year.

A cohort analysis, then, is the number crunching. It’s the sleuthing to determine if the customers from this week are worth more or less than the ones from a year ago.

Things change over time. Maybe the products are different. Maybe you switched manufacturer’s and the quality is different. Or maybe you’re using a new layout in your retail store that affects how people ‘flow’ through it.

busy-shopping-mall

Those changes, while seemingly small, can have a big impact on the bottom line. There’s a ton of psychology behind where the eggs are in grocery store (and where they’re hiding the booze).

So analyzing trends and patterns from customers based on when they shopped (i.e. acquisition date) can provide a lot more meaningful feedback on what changes resulted in different results (and why).

Here’s why that’s important (beyond just finding out where the booze is).

Why Cohort Analyses are Better than Standard Metrics

Google Analytics provides a wealth of data.

It’s perfect for finding certain things at a glance. Like aggregate, surface level data. That’s not a knock; it’s one of the best tools to see simple site wide metrics like top visits from certain sources, or dive a little deeper on how individual pages or pieces of content are performing.

google-analytics-pageviews-metrics-codeless

But as with the retail store example earlier, websites change. A LOT.

Each time you redesign it, come out with a new product, update your service offering, and a host of other random reasons.

When those changes happen, it’s important to put these metrics in context. Comparing traffic or Time on Site of a particular blog post from now vs. a year ago might not be super relevant if it’s undergone a tremendous visual change in the meantime.

Cohorts can help. It’s like layering on a filter to add context to data you’re looking at. Viewing those details, by segments, now should produce more accurate findings. (And not just a vanity sepia filter to hide your bald spots. Just me?)

For example, let’s take a look at how tablet and mobile traffic compares to our site’s average over the course of a day.

pageview-data-comparing-mobile-traffic-google-analytics

Pretty interesting right?

Check out that massive Time on Site difference!

time-on-age-difference

This information is interesting… but not sure helpful or actionable by itself.

So let’s add a cohort. Let’s look at the number of first time visitors who’ve left our site today, and see how many of those come back the next day.

cohort-analysis-each-day-after-acquisiton-date

Now we can dive deeper into how many of those people are coming back to our site (within X number of days of their first visit).

This brings us closer to Activation, Retention, and all those other Pirate Metrics to obsess over.

Zooming out, you can see these changes both numerically and visually.

How about the plain English version?

First, the graph depicts the percent of returning visits over a (default) seven day range.

cohort-analysis-setup-google-analytics

The colorful, blue comparative table below the graph is where things start to heat up. (Literally.)

The table shows you what percentage of people came back to your site within seven days of their initial visit.

The second column from the left, Day 0, reflects the day on the left-hand column under all users:

day-0-cohort-analysis

The next column, Day 1, represents the first day after this group of people visited your website on May 9th.

That means 2.86% of people who visited your website for the first time ever on May 9th returned the next day. Day 2 would be what percent of those visited again on Day 2, etc.

Each date under All Users starts a brand new cohort. So May 9th is one. May 10th another. And so on. And each has their own pattern of returning users.

According to the tiny sample size in this example, the oldest cohort, May 9th, has seen a majority of first-time visitors come back to the site.

Make sense? Kinda, sorta?

Well if that wasn’t nerdy enough for you, it’s about to get a whole lot more geeky.

How to Use Google Analytics Cohort Analysis Tool

Let’s do a step-by-step walkthrough to see how you can start using Google Analytics’ cohort analysis tool.

Pull up Google Analytics, click the Audience drop down in the left-hand sidebar, and look for Cohort Analysis:

cohort-analysis-google-analytics

Here’s how the Google Analytics cohort analysis report will look like at a glance:

google-analytics-cohort-analysis-layout

  • Report settings and metrics are all the way at the top
  • In the middle is a giant graph (that’s kinda useful, but more for the visual peeps out there)
  • While the final table at the bottom shows the results by cohort and date.

Here’s what that graph in the middle is showing:

day-1-2-3-acquisiiton-cohort

We selected Acquisition Date for our specific cohort type, so that’s how the information is sorted in this graph. Day 0 is your acquisition date. While Day 1 is one day after, Day 3 is three days after, etc.

You can adjust these different cohort factors up at the top:

cohort-analysis-main-page

Here are the main factors you can analyze:

  • Cohort Type: Restricted to Acquisition Date
  • Cohort Size:  Sort by day, week or month
  • Metrics by category:
    • Per user:
      • Goal completions per user
      • Pageviews per user
      • Revenue per user
      • Session duration per user
      • Sessions per user
      • Transactions per user
    • Retention:
      • User retention
    • Total
      • Goal completions
      • Pageviews
      • Revenue
      • Session duration
      • Sessions
      • Transactions
      • Users

You can access all of these in the cohort analysis drop down menus:

cohort-analysis-metric-dropdown

Here you can select to run an analysis of a group of users sorted by day, week, or month (or whatever other variable you want).

For example, if you want to know how many pageviews each user had (metric), sorted in groups by day (cohort size) for the last 7 days (date range), you simply enter the following into the drop down menu:

cohort-analysis-setup-google-analytics

Then, I am presented with the following graph:

So, what we see here is:

  • The May 9th cohort of users had 1.5 pageviews per user
  • That same May 9th cohort also had an average of 0.03 pageviews per user the next day (Day 1).

Now, let’s jump back to our original chart, showing the following data.

cohort-analysis-low-engagement

You may be asking: “How the heck do I use this information?”

“What do I do (ha – you almost said doodoo) with the fact that only a tiny percent of first time visitors are returning the next day (or the one after that)?”

day-0-1-2-users-cohort

“Why did 2.86% of the cohort visit again the next day with the may 9th sample, but then a big drop off for the May 10th cohort?”

Let’s find out.

Fortunately, Google Analytics allows you to break down these reports even further. So you’re not stuck in the proverbial analytics dark.

Notice at the top, we can add different segments to break down our report further:

add-segment-google-analytics-cohort

Now let’s go back to analyzing the Mobile and Tablet segment:

Select it, and you can now see a comparison from your original data set (all cohort users segment) vs. the Mobile and Tablet traffic:

So, this data is showing us the cohorts of people sorted by date, who visited our site the next day after visiting for the first time, sorted by mobile and tablet. (Or, the very definition of a boring example.)

But check out that leap in return visits from the May 11th mobile cohort!

Obviously our conclusions in this case are limited because it’s a tiny sample of a too-limited date range. However, hopefully you can see the potential here.

If that’s not enough, you can also sort by just mobile, or even traffic sources like Organic Search, Direct, and more. (If you’re masochist.)

For example, here’s what Organic Search visitors look like:

comparing-cohort-analysis-google-analytics

Hmmm. Interesting. Organic Search visitors from the May 11th cohort are returning more frequently than average.

Was there a new blog post that day that’s bringing them back?

Dunno. But you get the idea.

Conclusion

Cohort analyses allow you to view data by segments of people.

Businesses of all shapes and sizes and flavors can use them to determine what changes (if any) resulted in better overall performance.

Google Analytics cohort analysis tool can help you put otherwise generic, aggregate website data under the microscope.

In all of about five minutes, you can quickly compare how different cohorts compare with others. And then cross reference that information with your own actions or marketing decisions may have played a role.

They allow you to zero-in not only on who is your most profitable customers, but why (or what) influenced them to become your most profitable customers.

And how you can do more (or less) of the same to scale results accordingly.

About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.


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Pet Peeves from the Copyblogger Editorial Team, and What they Reveal

"No one can nurse a good peeve quite like a group of writers." – Sonia Simone

We’ve written quite a bit lately about identifying core values in your content.

Creating content around a positive value like integrity, fairness, humility, or faith will attract an audience that shares those values — and that fosters a powerful sense of unity.

But our friend negativity bias tells us that the flip side of that will probably be more compelling. In other words, talking about the things that bug you will build an even faster bond with your audience.

For today’s post, I asked our editorial team to let us know their peeves — the things that irritate, bother, and annoy them.

I’m going to try to tease those out and figure out the values behind them — and see what that might say about who we are as a company and a community.

So let’s get peevy.

Stefanie Flaxman’s peeve

Stefanie is our editor-in-chief, and as you’d expect, she has a healthy list of grammar and usage peeves.

But an editor is much more than a proofreader. It’s one thing to misplace a comma — it’s another to come at a post in a fundamentally flawed way. Here’s Stefanie’s peeve:

Hype/extremes/absolutes: Writing voices that are heavy on absolutes tend to simultaneously lack substance and speak to the reader as if they know what’s best for them … which isn’t a combination that builds credibility.

For example, earnestly referring to any flesh-and-blood human being as a “guru” is typically too vague or a sign of hype. If the person is an expert, top scholar, or highly respected professional, use those labels instead — they’re more specific.

What it reveals

Putting this post together reminded me that an Allergy to Hype has always been at the core of Copyblogger’s message. Since Brian founded the blog in 2008, Copyblogger has always stood in contrast to the hype-slingers who substitute flash for value. We believe that substance matters.

Robert Bruce’s peeve

Ten-dollar words: This is an old one, but a good one, and for good reason. Most writers have moderate-to-severe mental problems. I am, obviously, no psychologist, but the attempt to unnecessarily project one’s “intelligence” through the use of big words — when plain words can do the job — seems to be clear evidence of this.

What it reveals

Besides the obvious fact that Robert wins a lifetime “get off my lawn” achievement award, I think this shows how passionate we are about Quality. Quality of information, quality of business practices, quality of writing.

Loryn Thompson’s peeve

You’ve only seen Loryn on the blog once (so far), but she’s crucial to our editorial success. She’s the data analyst who looks at the numbers behind what we’re writing, and helps us to get our message out more effectively.

Here’s Loryn’s peeve:

Using “over” with numbers (instead of “more than”) : As Rainmaker Digital’s data analyst, this one comes up for me a lot. Every time I catch myself writing “over 5%…” in a report, I go back and change it to “more than.” 

 

Now, the Associated Press said in 2014 that both “over” and “more than” are acceptable to use with numeric comparisons — as in, “There were over two hundred people at the event.” But you know what? It still bugs the crap out of me. 

In my mind, “over” mixes the abstract world with the physical realm. For example, if you were to say, “We flew over 6,000 miles …” you could be saying that you flew more than 6,000 miles. Or, you might mean that you were literally above the earth for 6,000 miles.

What it reveals

I picked this one precisely because the team doesn’t agree on it. Some of us are “more than” folks (me, Loryn) and some aren’t. Stefanie tries to remain agnostic.

While it can be fun to give in to that eye twitch when someone makes a style choice we don’t like, I think it’s smart to keep some perspective. There are usually good arguments to be made for different usage choices, so I’ll go with Diversity as a value for this one.

My take is that it’s more important to be thoughtful about your choices than it is to be didactic. Although alot is never going to be a word and you can’t make it one.

Twitch, twitch.

Jerod Morris’s peeve

Jerod’s a person with a strong moral compass, and I was interested to see his peeves. Here’s the one I chose from his:

Misspellings of names: It’s especially bad when the name is a common one that’s misspelled in an obvious way. But any name misspelling shows a lack of basic respect for the subject you’re writing about. It’s not really grammar, but it still makes me cringe. Find out for sure.

What it reveals

Misspelling a name in content is a classic example in failure of what Jerod calls Primility (the intersection between pride and humility). It’s both sloppy (lack of pride) and disrespectful (lack of humility). I think it’s fair to say that Primility is a core value for Jerod, and that’s probably one of the reasons he’s been such a great asset to our company.

We are, make no mistake, proud of the work we do at Copyblogger. We love producing the blog, and we try hard to make it excellent. But we know that humility’s important, too. We’re under no illusion that this blog is perfect, and we try to challenge each other to always make it more relevant, more useful, and more interesting.

Sonia Simone’s peeve

You may feel like you already know more than you need to about my peeves. For today, I revisited a favorite:

Boring content: This one just makes me sad … seeing site after site after site that utterly fails to stand out in any way.

When I see a site with a genuine, passionate voice — even if there are a few usage errors — I may cringe a little, but mostly I cheer. I’d much rather see a site with plenty of G.A.S. than a grammatically perfect one that has no soul.

What it reveals

Individuality is absolutely a core value at Copyblogger. We’ve never endorsed the paint-by-numbers approach to marketing and online business … partly because that would be very boring, and mostly because it just doesn’t work.

And then there’s the Oxford comma

If you aren’t familiar with the Oxford comma (also known as the serial comma), it’s that final comma in a collection of items in a sentence.

Here’s a visually amusing example of the same sentence with and without one.

I like the Oxford comma because it’s always clear. Jerod gets downright fierce about his support. That renegade Loryn, though, has come to prefer dropping it.

“I used to be a staunch Oxford Comma advocate, but now I prefer the way short lists flow without it.” – That Renegade Loryn

Either is correct, but do be consistent. (Although the late Bill Walsh, noted Washington Post usage stickler, advises that if a serial comma is important for clarity, go ahead and put one in there, even if it’s not your usual style.)

A note about peeves and unity

I mentioned when we started that talking about the negatives will build a connection with your audience more quickly — and it will. But keep in mind that a steady diet of negativity will give almost anyone indigestion.

Don’t shy away from talking about the good stuff, too. An honest values system includes both positive and negative points of view.

How about you?

What sets your teeth on edge when you see it in a blog post or hear it in a podcast? What do you think that says about you and your values?

Let us know in the comments!

The post Pet Peeves from the Copyblogger Editorial Team, and What they Reveal appeared first on Copyblogger.


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5 strategies to improve your ad copy

Need some ideas to make your search ads really shine? Columnist Mona Elesseily provides tips for improving your ad copy to increase conversions.

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MarTech, conversational UI, and the future of connecting with customers

Contributor Jeff Eckman recaps a session by MarTech San Francisco 2017 speaker Nick Pandolfi of Google’s Global Product Partnerships team in which he shares Google’s view on this emerging topic, as well as five tips on designing for the conversational interface.

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Using search and social to support TV advertising

Are you investing in TV advertising? Columnist Justin Fried explains how search and social can work in together to help capture consumers activated by your television ads.

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