Many marketers outsource their data management.

eMarketer – March 20, 2018

By Ross Benes

As the amount of data that marketers rely on continues to grow, many are finding that they need outside help managing it.

According to a Q4 2017 survey of US marketers by Adestra and Ascend2, 43% of respondents reported that they outsource their data-driven marketing strategies. While the majority of the respondents use a combination of outsourced and in-house resources, just 5% rely strictly on in-house resources to manage their data.

Data-driven marketing isn’t exactly new. But many marketers are looking for data management assistance that comes from outside their companies because the landscape is changing rapidly, according to Stephani Estes, senior vice president director of media strategy at ad agency Cramer-Krasselt.

For example, in previous years marketers didn’t need to worry much about the EU’s General Data Protection Regulation (GDPR), which states that people’s data can only be used if they give a company explicit permission.

But now that compliance of GDPR becomes enforceable in May, marketers are having to familiarize themselves with new types of tech vendors like customer data platforms, which exclusively focus on managing and storing first-party data. These quickly evolving industry changes increase the need for data expertise, which marketing firms don’t always have a steady supply of.

The need for outsourced help with data is also reflected in the lack of advertisers that completely bring their programmatic ad buying in-house. In an August 2017 survey of brand advertisers worldwide by Infectious Media, 84% of respondents said they wanted more control over their programmatic efforts, but only 1.4% had taken steps to bring their programmatic buying in-house.

“We often hear from clients that they recognize the importance of a data-driven marketing strategy,” Estes said. “But putting one into practice can be a real challenge.”

The post Many marketers outsource their data management. appeared first on Cramer-Krasselt.

What You Need To Know About Rel Nofollow Links, Google & The Law

My notes on using the rel=nofollow attribute. Search engines like Google, ask that you adequately provide machine-readable disclosure and add the ‘Re=Nofollow’ attribute to ANY paid links on your site or any paid links you BUY that point TO your site.

Read the full article here What You Need To Know About Rel Nofollow Links, Google & The Law

Google Data Studio: The Beginner’s Tutorial

Google Data Studio is a communication tool. It brings together data you store in several places so you can visualize it on one screen. The goal of using Data Studio is to become a data communicator, not a data plumber.

There are several Data Studio beginner’s guides in the wild. I’ve created this one to get you thinking in Data Studio terms. It’s a proper tutorial, taking you through things step-by-step. Where there’s too much to say about a feature, I’ll link to relevant documentation or other blog posts. And of course, this guide is for you — please ask questions and leave feedback so that we can improve it!


Data Studio lets you visualize data from many sources

You only need a Google account to get started

Get oriented

Make your first report

Connect a sample data source

Get acquainted with the report interface

Now let’s make a chart

Add a time series

Set dimensions and metrics

Set the date range

Update the style to use bars

Label your chart

Admire your work

Add interactivity

Add a filter control

Connect your data [optional]

Share your report

What we learned

Do more, learn more

Data Studio lets you visualize data from many sources

The compelling reason to choose Data Studio is the sources from which it can pull data. With it, you can use almost any data available from Google. That includes Google Analytics, AdWords, Search Console, BigQuery, and more.

Data Studio is also easier to learn and teach than the alternatives. In particular, querying the Google Analytics API requires learning a complete vocabulary of dimensions and metrics (“Page” → “ga:pagePath”) and operators (“Match Regex” → “=~”). Data Studio renders that obsolete for many use cases.

Finally, the fact that Data Studio makes all these data sources available lets you juxtapose charts from many sources in one report. For instance, you might want to chart both organic traffic from Google Analytics and clicks from Search Console in the same report. With Data Studio, you can!

You only need a Google account to get started

Data Studio is a free product. The only way to incur expense is if there are fees associated with accessing your data source. For instance, BigQuery charges for some requests. That won’t happen in this tutorial.

All you need to get started is a Google Account. If you have personal data to work with, experimenting will be much more fun — but it isn’t required.


Note that you may have to accept some terms and conditions before creating your first report. For some reason, this process can be a bit finicky. If at first you don’t succeed, dust yourself off and try again.

Get oriented

See interface documentation.

The next thing you’ll see is the central Data Studio interface. Data Studio is part of Google Drive — like Docs, Sheets, or Slides. The interface of Data Studio is similar to The interface lists only Data Studio documents. You’re meant to do organizing within Google Drive as opposed to Data Studio itself. That’s why you can’t make folders on this screen.

Unfamiliar territory!

One difference worth noting is that instead of a single document type, Data Studio has two. There are Reports and Data Sources. In this tutorial, we’ll be manipulating reports, and using the sample data sources provided by default.

Make your first report

See report documentation.

Make sure you’re looking at the Reports section as opposed to the Data Sources section. You should see Reports by default. Click the familiar “+” button in the bottom right corner to get the ball rolling! If everything is going as planned, you should now have a blank canvas:

Connect a sample data source

See data source documentation.

Before you can add any charts to your report, Data Studio needs to know where that data is. Fortunately, Data Studio comes with several sample data sources to help you get started. Each of these has a name that starts with “[Sample]”. For now, choose “[Sample] Google Analytics Data”:

You’ll see a request for confirmation. Click add to report.

This sample data source exposes Google Analytics data from the Google Store. We’ll show you how to add a custom data source later in the tutorial.

Get acquainted with the report interface

See report interface documentation.

At this point you’ve got a blank report in front of you — the world is your oyster! If you want to mess around a bit, feel free. Anything you add will use the data source we just added.

You can always undo anything or create a new report if you don’t like the result!

Now let’s make a chart

I don’t want to bury the lede. Here’s what we’re going to create:

see the finished product

This is a simple chart. It shows sessions to the Google Store website by month and compares each month to the same month last year. It also happens to use most of the features you’ll need to master in Data Studio, so it’s a great starting place. This is what it looks like:

Add a time series

See chart type documentation.

A time series is a type of chart. Its defining characteristic is that its x-axis is a unit of time. In Data Studio, a chart isn’t just defined by how it looks. In fact, there are multiple ways to make something that appears to be a column chart. Instead, a chart also specifies what data it accepts as inputs and how it transforms that data into a visual.

Try adding a time series to your chart now. It’s the very first chart type in the toolbar:


Dragging a box on your canvas will result in something like this:

Humble beginnings.

Congratulations — you’ve made your first chart! Let’s take a moment to analyze what just happened. You said you wanted a time series. On the other hand, you didn’t have an opportunity to specify what data you wanted to visualize. Data Studio chose some reasonable defaults for you (charting Sessions by Date). Next, we’ll see how to customize these choices so that you aren’t stuck with these defaults forever.

Set dimensions and metrics

See dimensions and metrics documentation.

Dimensions and metrics in Data Studio are conceptually the same as dimensions and metrics in Google Analytics. If you’re using Google Analytics as a data source, the same dimensions and metrics will be available to you in Data Studio.

A quick refresher: metrics are numbers, and dimensions allow you to slice and dice those numbers in different ways. Pageviews and Sessions are both metrics. Page and Landing Page and Default Channel Grouping are dimensions.

The chart we just created shows Sessions by Date. By selecting the chart, we can change these in the right-hand sidebar shown below.

We’ll keep Sessions the same, but change the time dimension to be Month of Year. Be careful here — Month of the year and Month of Year are two different dimensions. To create our example chart, we want Month of Year. The result will look something like this:

Not a great visual — a time series with only two data points? A little wonky, but never fear. We just need to inspect a wider date range.

Set the date range

See date and time documentation.

The reason that the chart only has one or two data points now is that by default Data Studio will show the last 30 days of data. Most of the time that means a chart by month will only show partial data for last month and this month.

Let’s make things more exciting. In that same sidebar, you’ll see an option to set the default date range. Use it to select “Last Year”.

Now we’ve got an appropriate time range to show off our website’s performance:

Finally, let’s compare this data against last year’s. In the same date range control panel, select a comparison period:

Which results in:

We’re most of the way there at this point. The data that we want to visualize is there; it just doesn’t look quite the same.

Update the style to use bars

See time series documentation.

The most apparent difference between our chart and the target is that the target chart uses bars instead of lines. Since we want to be able to compare specific months against their performance last year, grouped bars will be more natural for our audience to interpret.

Click your way over to the Style section in the right-hand sidebar. At the top, you’ll see a conspicuous option to use Bars instead of a Line to represent your data.

This has the expected result:

So the chart seems correct, but the example also has a pretty title at the top. What about that?

Label your chart

Data studio offers bare essential drawing tools — text box, image, rectangle, and circle:

Choose one or more of these and drag away. Let your inner artist out! Take a gander at the options given in the sidebar. Foreground and background colors can be selected, in addition to basic font choices.

Here’s what I came up with:

If you’d like to have more fun — and maybe make the chart align better with your own brand — you can also play with the style controls. Select the chart and click “Style” in the sidebar. Check out the series color options, and font face and size options. Extreme changes are possible. You can even turn off the axes entirely!

Admire your work

For those following along, you’ll see that we’ve built what we had planned. If you want to scope out what your report really looks like, hit the View button:

This removes the helpful design grid and the rest of the UI elements. It brings your visualization to the foreground.

If you give someone view permission, this is what they will see.

Add interactivity


One of the great things about Data Studio is that it also accommodates interactivity. Let’s say we’re presenting this chart to our board, who is interested not only in trends in overall sessions but also in the channels from which those sessions began.

We could make a different chart for each channel — and in some cases, that might be the appropriate visualization. But for the sake of our tutorial, we can avoid creating eight different charts. Instead, we’ll add interactivity to our current chart so that we can use it to track each of these

Here’s the end product:

Add a filter control

See filter control documentation.

This pictograph (like an inverted pyramid) represents a filter control. See how the bar starts out large and gets smaller? That’s because it’s been filtered!

Just like a chart or a text box, you can draw a filter element onto your canvas:

Subtle difference — look in the upper right corner!

And, like any other component, you can configure the dimensions and metrics that the filter users. Note that the filter does its filtering on a dimension, not a metric — the metric is merely an aesthetic option. Configure your dimension like this:

By default, a filter control affects all charts on a page. You can change this by grouping the filter with the charts you want it to affect. See the documentation for details.

Now when you go to View mode, you can click on the filter control and choose what Channel Grouping you want to focus on:

Now we’ve got the same chart, but showing only organic traffic.

Connect your data [optional]

See the Google Analytics connector documentation.

I’ll assume you have access to a GA account. If not, that’s fine—you can skip ahead to “Share your report”. Let’s swap your data for the sample GA account we’ve been using.

Select the time series component and click “Data Source” in the sidebar.

This will allow you to select any data source you have. You can also create a new data source from within the report building interface:

To connect to Google Analytics, you’ll have to choose an Account, Property, and View to use. On this new screen, select “Google Analytics”.

Because we’ve chosen a Google Analytics data source, Data Studio already knows how to interpret the data. If you wanted to use another data source you might have to tell Data Studio what its schema is. I’ve written a guide to using Google Sheets as a data source that you might find useful.

When you’re satisfied with the list of dimensions and metrics that Data Studio is showing you, click “Add to Report”.

Your chart will now be using the data source you’ve created. No other configuration required!

Share your report

Sharing a Data Studio report is slightly more complicated than in Google Docs. The most important thing you need to understand is how permissions work. That blog post is a crash course on the subject. The choice you’ll be making is whether you’re letting readers see data using your credentials.

For more details about sharing, check out these three documentation pages:

  1. Sharing documentation
  2. Report sharing documentation
  3. Data source sharing documentation

What we learned

In this tutorial, we covered all of the basics of Data Studio. Here’s a quick summary of what we learned:

  • How to start using Data Studio.
  • How to create a new report.
  • How to create a time series.
  • How to choose dimensions and metrics.
  • How to expand the date range.
  • How to label a chart.
  • How to make a chart interactive.
  • How to connect a data source.

Whew — that’s quite a bit. I hope you feel a well-deserved sense of accomplishment. Nice work!

Do more, learn more

That’s it for our tutorial, but not for your Data Studio journey. Here are a few things to try with your newfound skills:

  • Add a second chart type.
  • Add more pages to your report.
  • Change the size of a page.
  • Add another data source.
  • Make a component report-level instead of page-level.

As you iterate, check out these resources — they all helped me as I learned Data Studio.

Happy visualizing!

Deep Learning Aims to Upgrade Your Smartphone’s Brain

The advertising world loves big, shiny, techy things. Agency and client ears perk right up when they hear about virtual reality kiosks, gadget-filled activations and holograms of dead rock stars. But then there are the tech innovations that sound a bit, or a lot, less sexy. Things like deep learning.

Deep learning is a subset of machine learning that essentially teaches computers to find patterns in sounds, images and other data. And while that may not seem like much fun to your average social marketer or copywriter, the tech giantsthe Facebooks, Apples, Googles, Netflixes, Microsofts and Baidus of the worldare investing massive sums of money in it. For instance, Google reportedly spent more than $500 million to acquire deep learning firm DeepMind in 2014. Baidu, the Chinese smartphone giant, runs deep learning and artificial intelligence-centric R&D centers in Silicon Valley and Beijing. Apple hires deep learning experts at fever pitch.

Why the frenzy? Deep learning technology lets you unlock your phone with your thumbprint. It enables Facebook and government agencies to identify your face in pictures. And it helps Siri and Alexa understand just what the hell you’re saying. Advertisers are experimenting with using deep learning to count how many passersby stare at billboards. The self-driving cars that we’re told are just around the corner rely on deep learning to avoid hitting other cars. Or people.

Continue reading at

Cramer-Krasselt and Carbon bring Paper and Packaging animated characters to life.

Little Black Book – March 9, 2018

Carbon animators brought the new cornerstone paper and packaging characters for the national marketing campaign Paper & Packaging – How Life Unfolds® to life just in time for their launch in February. The new campaign creative, developed in conjunction with integrated marketing and communications agency, Cramer-Krasselt (CK), for client, the Paper and Packaging Board, includes two :30 TV commercials and five self-directed fully CG films designed specifically for social media, and several print ads. These characters, a corrugated box and a ream of paper, highlight the authentic properties and value of paper and paper-based products in everyday life.
To ensure these characters looked, acted and felt real, Carbon filmed scenes that aligned to initial scripts and used lighting and tracking information to put the characters into their respective environments before creating the detailed animation tests that gave each character its own distinct and memorable personality. These early tests ultimately set the look for character behavior and helped the client and agency understand everything from range of movement and physicality to the potential for emotional expression, helping inform the characters’ personalities even before the storyboards and scripts were finalized.
“It was immediately clear to everyone involved that these characters were not cartoons,” says Carbon Chicago’s Managing Director, Phil Linturn. “We were tasked with creating 100 percent believable characters that genuinely belonged in a variety of scenarios where paper and packaging naturally exist and who were subject to the real-world physics and lighting as if captured in-camera.”
“Our characters needed to look and react authentically, staying true to their materials, but they have fixed facial features and don’t speak to express themselves,” noted Jim Root, vice president, creative director at Cramer-Krasselt “Carbon’s characters surprised us round after round with unexpected moments of humanity. Even the subtlest movements communicated so clearly. We are so proud of how these characters turned out and look forward to what we can do with them next.”
Sister companies Carbon and Whitehouse worked together from edit through animation, lighting, rendering, composite and color, all on the same floor of their offices in Chicago. Whitehouse Post editor, Matthew Wood, created parallel edits, layering the puppeteering footage over the plates to be used in the final film. Overlapping the edits allowed everyone to understand the timing of the spots before the edits were locked and the animation began. Carbon’s team of animators provided a stream of rough animation updates, making the feedback process faster and more efficient. Once edit was locked, the animation style formed by the initial tests was refined and rendered using lighting references from set to create the final photo-real character animations. Finally, Nuke and Flame compositors, part of the Carbon team, worked closely with CGI and color to ensure that these characters truly occupied each real-world scenario.

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