DaedTech

Stories about Software

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Basking in the Traffic Gains: Our Content Refresh Early Detection System

I’ve made two posts this year about content refreshes.  One was about traffic recovery, and the other was about refresh identification. Both of them were, admittedly, fairly wonky.  I got lost in my SQL statements and graphs and invited you to come along for that ride with me, if you dared.

Not today, though.  Today is the day that I heed the demand, “In English, poindexter!”

I’m going to show off the easy button for refreshing content and realizing substantial traffic gains.  I can now do this because I’ve turned my queries and graphs into a dead simple, prioritized list of content to refresh.  You can explore it for yourself here (click “refresh candidates”), and this is what it looks like.

This is a screenshot from the alpha offering of our content performance monitoring dashboard, which I announced back in June.  (Beta coming soon!)  The dashboard here features our content lab and community site, Make Me a Programmer.

If you’re unclear on what a content refresh is or why you should do it, let me explain.  A content refresh involves making updates to an existing post or article on your site.  As for why you should do it, let me present an actual anonymized field study of the impact on traffic (refreshes executed on this group of URLs at the red dot).

If you’re sold on the concept, I’ll spend the rest of the post explaining how we help you do it, using our tooling and refresh-candidate identification methodology.

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Organic Traffic Recovery: How to Win SERPs and Influence Execs

In the last few months, I’ve been burning the midnight oil.  By day, I run Hit Subscribe.  But by night and weekend, I’ve buried myself in a sea of client analytics data, building out our content performance monitoring alpha offering.

I say this not to complain.  After years of pretending to know what I’m doing in marketing, it’s fun to return to my roots of pretending to know what I’m doing in software engineering.  I mention the midnight oil because it has served as fuel for deep, interesting insights into refreshing content and traffic recovery.

And today I want to offer up those insights to you in the form of a clear, actionable traffic recovery playbook.

Setting the Scene: What Happened to Our Traffic?

Imagine that you’re responsible for content on your site and your organic traffic graph looks like this.

Sooner or later, you’re going to have an uncomfortable conversation about how and why you’ve presided over a 40% traffic decline.  From my outsider’s perspective, this most commonly occurs following an acquisition or perhaps a change in leadership.  After obligatory pleasantries, one of your first professional encounters is explaining this graph.

There’s a pretty good chance that you have many valid reasons.  Someone cut the content budget.  A staff writer quit and the backfill took forever.  The recent site redesign performs as well as a walrus on a unicycle.

But even as you say these things, they’ll sound like excuses to you.  And they’ll absolutely sound like excuses to the other party.

So here’s what you say instead:

I can do a detailed postmortem write-up on the traffic performance if you want.  But if you’re interested, I have an actionable plan for how to recover the traffic and I can show you that.

These two sentences will absolutely and completely reset the conversation.  You just need to be able to deliver on the actionable plan.  And that’s what I’m going to hand to you in the following sections, drawing on our now-unfair advantage of tons and tons of data.

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The Best Tool for Real Content Attribution? The Fermi Problem

I was recently scrolling through LinkedIn, admiring the art of the single-line hook followed by emoji bullets, when I stumbled on a really interesting question from Fio Dossetto.  The question is as follows:

As someone who self-identifies as neither a marketer nor “smarter than” anyone, I figured I’d leave the comments to the thought leaders and call it a day.  But I couldn’t get the question out of my head, especially since some variant comes up so frequently in discussions with clients.

So in this post, I’ll offer the two cents nobody asked for on the subject of challenging content attribution.

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The Flat Squirrel Barrier to Content at Scale

Editorial note: I originally wrote this post over on the Hit Subscribe blog.

Be decisive.  The road of life is paved with flat squirrels who couldn’t make a decision.

I don’t know who originally said this, so I can’t properly attribute it.  I guess that makes it some kind of piece of folk wisdom, now best suited for cheugy merchandise.

But whoever dreamed it up has a gift for impactful figurative language.  I’m sure you can picture the situation — your car barreling down on some hapless squirrel who starts left, then right, then backward, then splat.  Had the squirrel run in any direction, it would have met a better fate than it did by fretting to literal death about the decision while doing nothing.

I’m setting the stage with this gruesome metaphor to make my point here memorable.  And my point here is that collective flat squirrel syndrome is going to be your organization’s single biggest barrier to content and funnel metrics at scale.  (And I should note this only applies to customer acquisition strategies that require and substantially economize on scale: SEO, communities, parasocial followings, podcasts, etc.)

Here are some things you might think would be the problem but aren’t:

  • It’s so hard to find good writers.
  • The keywords in our space are super competitive.
  • {Insert our audience here} is such a picky audience.
  • There just aren’t any public distribution channels where our audience hangs out.
  • So few people know how to talk shop to our audience.

Nope, nope, nope, nope, and…drumroll, please…nope.

Those problems are all actually relatively easy to solve compared to flat squirrel syndrome.  The reason for that gets a bit into org theory but suffice it to say that this problem is intractable because it’s human nature and because the solution has to come from within, unlike all of the logistical issues above that can be solved with staffing and experimentation.

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Offering Client Trials: Prototypes vs Auditions

(Editorial note: I originally published this one over on the Hit Subscribe blog.)

Do you offer a trial?

I’ve run Hit Subscribe’s sales for five of our seven years of existence.  And this is by far the most common question I field at some point during discovery.

The simple answer to the question is “sure, we can do that.”

We don’t bother to set any minimums on the amount of content you can commission.  We’re not selling automobiles; economies of scale on content are marginal.  You can buy as little as a single blog post if that meets your needs.

In fact, if you read our client bill of rights, you’ll see that we encourage you to de-risk.  If you’re worried about doing something at scale, I view it as our obligation as a vendor to help you prototype success ahead of a large-scale commitment.

Where things get nuanced, however, is around the question of which risk you’re minimizing for when you ask about a trial.  And that’s at the core of what I want to document here reference for future prospects.  We’re happy to start small, but there’s a good chance we don’t think of a trial the same way you do.

Auditions vs Prototypes: What Risk Does Each Reduce?

To get specific about this, we view small batches as prototypes, and, importantly, not auditions.  Consider the difference.

Auditions and Prototypes

  • An audition is a subjective evaluation that de-risks against the judge having a large commitment to something they don’t care for.
  • A prototype is an objective evaluation that de-risks against an engagement not achieving a goal or outcome.

A simple, if mundane, example of an audition is a wine tasting.  Before purchasing an expensive bottle of wine from a winery, it makes sense to run a trial (audition) of “do I, the judge of wine, like this wine?”

In a professional context, auditions tend to, or at least should, move beyond simple aesthetics.  The judge of a piece of content (ideally) isn’t evaluating whether they personally like it but rather acting as a proxy for others’ opinions or perhaps as some kind of designated expert in the medium.  But the subjective, “do I approve of this” evaluation remains at the core.

A prototype, on the other hand, involves a measurable big-picture goal and a smaller experiment designed to provide fast feedback on an initiative’s ability to achieve that goal.

For instance, let’s go back to the winery.  But this time, let’s say you have a goal to fill your small wine cellar with 200 bottles of wine, for less than $5,000, with wine that you feel good about serving to guests.  Here your tasting becomes less important in favor of concerns like whether the bottles are in an acceptable price range, will fit in the cellar’s slots, and will appeal to unknown people.

In this world, a prototype might involve buying five bottles of wine within your budget, then confirming they fit in the cellar and guests seem to like them.  If the run of five goes well, you can scale up your buying from the winery.

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