Marketing Technology Ecosystems, Part 1 of 6: Introduction

Scott Brinker’s Marketing Technology Landscape is appropriately bewildering, with 3,500+ vendors in the marketing technology space:

marketing_technology_landscape_2016.jpg

It can be difficult to decide which vendors to do business with; all their sales claims sound identical. They all say they’ll add multiples to our ROI, save us valuable time, and make us wheelbarrows full of cash.

How do we know which companies are the real deal?

Will any of them truly benefit our business?

The dirty secret of many of these vendors is that they are all based on a handful of technology ecosystems. They share, for lack of a better analogy, similar or identical technology DNA. If we understand the DNA of marketing technology, if we learn the technologies vendors build their systems upon, we can better evaluate their claims.

For example, a marketing technology vendor can claim they have predictive analytics. Which prediction technology is it based on? If we know that two vendors both use Google’s Predictive API, then we know much more about their service offering – and its limitations.

In this series, we’ll examine the marketing technology ecosystem. We’ll look under the hood at the platforms marketing technology is built upon, and examine whether you could build instead of buy, since many marketing technology vendors charge significant premiums beyond what their costs are.

Marketing Technology Ecosystems

  • Part 1: Introduction
  • Part 2: Essential Marketing Technologies for the Future
  • Part 3: Google Cloud
  • Part 4: Amazon AWS
  • Part 5: IBM BlueMix
  • Part 6: Independent Platforms

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Marketing Automation Migration, Part 8: Finishing Up

In Episode 8, the final episode of the Marketing Automation Migration show, we tackle sending a test message, validating it, and then sending a production test:

Thanks for sticking with me and this video series! We’re at the end now – the last few items are mainly administrative cleanup, so in the span of 8 episodes, we’ve deployed a new marketing automation system, a significant leap forward from an email-only system. I hope you’ve found these videos to be useful and insightful; marketing automation technology can be a great boon to your company’s growth.

If you’re watching this video series because marketing automation is a technology that your organization needs, I’m available for hire through my employer, SHIFT Communications.

In the migration map, we’re now in the lighter blue section:

Marketing Automation Migration.png

Marketing Automation Migration Series


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Calculating Non-Response Rate (RR1) in Google Analytics

Tom Webster recently wrote a terrific piece on boiler room-style content marketing. He was 100% right about everything until the last paragraph.

“And here is what I think these content “boiler rooms” don’t consider fully: the non-response bias of the people this content doesn’t convert. … Ultimately what I am getting at, as I often do, is the limits of clickstream data for things like this. Boiler Room content is very easy to quantify if you look at leads per post. But damage per post? Google Analytics won’t tell you that.”

Actually, Google Analytics can tell you that – sort of. Google Analytics + some top of funnel data (which you can inject through Measurement Protocol) can yield a reasonable approximation of what, in Tom’s world, would be a non-response rate. Then a firm like Edison Research could go research, survey, and determine that crappy content marketing was the cause of the non-response rate, which is the non-response bias.

I’ll reinforce what Tom said, that the clickstream (digital marketing data) has limitations. We can’t see people who swear aloud to their coworkers about yet another terrible blog post from a terrible marketing company. What can we see, however?

We’ll use the AAPOR RR1 definition as our starting point to model our content marketing response rate and non-response rate. Feel free to download the full Excel spreadsheet from AAPOR to see the whole model.

Consider the official pieces of RR1:

Screen Shot 2016-05-06 at 7.21.50 AM.png

I/(I+P) + (R+NC+O) + (UH+UO)

This formula is completed telephone or mail interviews – people who did what you wanted them to do – divided by everyone else.

Consider what data our digital marketing tools deliver to us. Social media, earned media, email marketing, search marketing, and advertising data can provide a reasonably complete analogue of a respondent pool. We have the total number of people who have seen our content or an invitation to view our content, such as:

  • Social media post impressions
  • Email messages delivered
  • Display ad viewability
  • Appearances in organic search
  • Search ads displayed
  • Earned media coverage/appearances
  • Native content ad displayed
  • Storefront passersby (via a Placemeter or foot traffic counter)

While not in Google Analytics natively, we can connect to most of these data sources via API and push the data into Google Analytics with the Measurement Protocol.

The people who click through to read our content at least once are an analogue of a partial respondent. They’ve engaged with us at least once. This is data available in Google Analytics as new website visitors.

The people who come back – our returning website visitors – are our completions. Our content was not offensive enough to prevent them from coming back for more. This is data available in Google Analytics.

Even if we just examined total new visitors to our website and total returning visitors, we’d have a sense of how good or bad our content marketing is. If returning visitors are on the decline, our content marketing isn’t working for us. We’re alienating our audience.

Screen Shot 2016-05-06 at 7.41.19 AM.png

If we put all three series of data together in a spreadsheet, we have a reasonably complete picture of our digital effectiveness: total number of people reached (including non-responses from ads, social posts, etc.), people who have engaged once (new website visitors), and people who have returned. A boiler room focus on content marketing only looks at the first and second series. A focus on quality content marketing focuses on all three, but weights returning visitors most heavily.

And with enough effort, focus, time, and resources, all this data can be made available in Google Analytics.


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