Blogging is dead if you fail to measure it

My friend and colleague Chris Brogan recently wrote an excellent post reiterating a point many of us have been making since the earliest days of social media: build on land you own. Here’s a post from 2010 on the same topic. Blogging as a communications and marketing method certainly isn’t dead.

There’s one caveat to Chris’ argument that blogging isn’t dead: we don’t know if our specific blog is alive or dead unless we measure it. Your blog could very well be dead if no one takes any action of value.

What should we be measuring for your blog? Like all forms of content, we should be measuring three buckets:


We should measure our blog by how much audience we can grow, from subscribers to social followers. While audience isn’t the end goal, an audience of zero means we can never grow.

We should measure our blog by how much our audience engages with it. When we share our blog posts to social media, in email newsletters, in meetings, how many people engage with it?

We should measure our blog by how many people visit it, how many people take action on it, how many people convert. Does our blog generate real business results and revenue?

If the answer to any of these buckets of metrics is zero, there’s a good chance your blog is dead.

What if your blog is dying, but not dead yet? Should you be posting to rented properties instead? Before you make that leap, I recommend investigating when your blog was growing, rather than fading. What did you do differently then? What topics did you write about? How did you do your outreach? Understanding what made your blog grow, what need your blog served for your audience, is the key to the building its growth.

Here’s an easy exercise to try. Copy 10 blog posts that were popular during your blog’s ascent into a text file. Copy 10 current blog posts into a separate text file. Paste both sets of text into a word cloud generator. What is different now to what you were writing about then? Is there a difference in content?

To understand whether distribution is your problem or content is your problem, analyze your content first. Once you’ve ruled out that content is the reason for your blog’s fading popularity, then focus on potentially changing way to distribute your blog and how you distribute it.

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Use Google Analytics To Guide Social Re-Sharing for Revenue

One of the questions social media practitioners ask most often is, “What links/content should I re-share more frequently?” We have so many choices before us. How do we decide?

Prerequisites: Goal Values

Before we assess which links to share, we need to know what business goal we’re seeking. I seek revenue. As a small business owner (my ‘side hustle’ microscopic publishing empire), I want revenue first and foremost. Everything else is gravy; revenue is priority.

To that end, I set up my personal Google Analytics to track revenue generated, both direct (via Gumroad’s eCommerce integration) and inferred (Amazon net revenue divided by number of clicks to Amazon in a given timeframe) as goals and goal values.

If you haven’t ever set up goals and goal values before, here are three blog posts which can help guide you:

Sharing Referrals

The first place to check for valuable links to share in social media is in Referrals:

  • Go to the Acquisition menu in Google Analytics (1).
  • Choose All Traffic (2).
  • Choose Referrals (3).
  • Re-sort the results by revenue (4).

What we see above are someone’s newsletter (5), Scott Monty’s weekly roundup (6), Roger Dooley’s interview with me (7), and my marketing podcast, Marketing Over Coffee (8).

I can drill down into each of these results to copy the URL that generated the revenue, then re-schedule (as appropriate) revenue-generating content. If you don’t get usable results the first time you do this exercise, expand the timeframe in the upper right.

Sharing Campaigns

The second place to check for valuable links to re-share in social media is in Campaigns:

  • Go to the Acquisition menu in Google Analytics (1).
  • Choose Campaigns (2).
  • Choose All Campaigns (3).
  • Re-sort the results by revenue (4).

What we see above are the top campaigns I’ve run which generated revenue. Most of them are email marketing; a few are social media. Since I know which products were featured in those pieces of content, I can create either permanent landing pages featuring that content for re-sharing, or create new content re-using the best performing content snippets. I’d then re-share those URLs.

Sharing Content

The third place to check for valuable links to re-share in social media is in Pages:

  • Go to the Behavior menu in Google Analytics (1).
  • Choose Site Content (2).
  • Choose All Pages (3).
  • Re-sort the results by Page Value (4).

What we see above are pages on my site which have the highest page value, meaning they drove the most revenue either directly or indirectly. These are blog posts for the most part; I should include these in re-sharing to test whether they continue to drive revenue as long as the content is relatively evergreen.


Social media sharing is a powerful tactic to help achieve our business goals. Once we are clear what our primary business goal is, we can measure it in Google Analytics, identify the referring traffic, campaigns, and content which help advance that goal, and re-share it frequently.

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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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