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Sutra

Breathe the body deep
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@JamaicanMoose do you hire an editor to edit/proofread the articles your writers submit? If so, what do you pay the Editor, and what’s your hiring process like for them?
 
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@JamaicanMoose do you hire an editor to edit/proofread the articles your writers submit? If so, what do you pay the Editor, and what’s your hiring process like for them?
Nope, no editor. I only read the first couple of articles that each writer sends me. Beyond that, they get sent straight to my VA for uploading to Wordpress. I then do the final formatting touches. If there's anything really wrong, it will stick out and I can fix it then.

While an editor would be nice, there isn't much value add in my niches. It would just further create a bottleneck to my scaling.

People don't read shit. They scroll to look at headlines, pictures, bullets, and buttons.
 
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There's been some talk about how to determine if an article is worth writing based on ROI. Here's how I figure if it's worth it or not. (This is going to be a mess so bear with me). I should also note that I only do Amazon Associates. If you're targeting health / finance / etc shit where articles are expensive, just ignore this.

Let's say it costs me $15 to have a 5 product buying guide written and $1.50 to have my VA post the draft. Total cost is $16.50. I'm not including my time to find the keyword topic because that's free as far as I'm concerned.

If this $16.50 article earns me $0.50 per month, the valuation on this article when I sell my website is roughly $16.50 (using 33x valuation multiple).

Now, not every article you have written is going to earn money. Far from it. Most aren't going to rank / earn shit. However, what they do add is authority to your site and boost your profitable articles. It's known that backlinks create a rising tide effect for your whole site. I believe content does the same and it's significantly cheaper with more upside.

Back to the example. Say I have 30 of those $16.50 articles targeting LSI of a higher priority KW on my site. These 30 articles aren't earning shit (say each is $0.50/mo) and getting low traffic. However, they're linking to one another and to the higher priority KW article. The higher priority KW article goes from #4 to #2, increasing revenue on it by $500/mo.

I spent a total of $495 to increase my monthly revenue by $515/mo and added $16,995 valuation to my site when it comes time to sell (using 33x valuation multiple).

The above is really a worst case scenario. It's really hard for a page getting traffic to only earn $0.50 per month. Even at a 3% commission, selling a $17 item once per month gives you more than $0.50. The average order on Amazon is higher than that.

In a more realistic scenario, say each of those 30 articles earns $3/mo and the higher priority KW goes from #4 to #2, increasing revenue on it by $500/mo.

I spent a total of $495 to increase my monthly revenue by $90/mo and added $19,470 valuation to my site when it comes time to sell (using 33x valuation multiple).

Now instead of 30 articles, imagine 100, 200, 300, etc. That's where scale gets you. Maybe this also pushes your higher priority KWs from #2 to #1. Maybe you grab some rich snippets. More content = more chances of that.

Anyways, a simple formula to see how much you would need to earn monthly on an article to break even. In the above, Cost of Article / Valuation Multiple = Break Even Monthly Revenue. In this case, $16.50 / 33 = $0.50. If I think an article can earn me more than $0.50/mo in revenue, then I buy it.

I try and keep my content costs as low as possible. It's a crapshoot as to what will rank. The lower my content costs the less monthly profit I need to earn from it to make it worthwhile to order.

Hope that makes sense.
 

bernard

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I spent a total of $495 to increase my monthly revenue by $90/mo and added $19,470 valuation to my site when it comes time to sell (using 33x valuation multiple).
That's a very interesting way to think about content value.

I used to think content quality would add a large premium on a sale, but judging from the offers I've gotten, buyers don't really care too much. Earnings are far more important.
 
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That's a very interesting way to think about content value.

I used to think content quality would add a large premium on a sale, but judging from the offers I've gotten, buyers don't really care too much. Earnings are far more important.
Indeed. Same with PBNs. Most buyers aren't going to care. Earnings are what matters because it shows Google likes the site. That's the hardest part about SEO.
 

bernard

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Indeed. Same with PBNs. Most buyers aren't going to care. Earnings are what matters because it shows Google likes the site. That's the hardest part about SEO.
I personally wouldn't buy a site with crap content and PBN links though, because I would consider it too volatile and risky.
 

bernard

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A major challenge of scaling is losing overview of which pages perform as they should, particularly if you have more than one site.

So how do we keep track on which pages are performing and which have something wrong with them, maybe a broken link, outdated content etc?

The first thing I've done is to set up a Google Sheets script, that gets earnings from affiliate networks, sorts pr. page and pr. time period: 30 days, 90 days, 365 days. I then calculate the average 30 day earnings for each page for each time period. If one page is underperforming sub 30% for a 90 day average, I color the cell RED. If it underperforms more than 20% I color it YELLOW. If it overperforms or stays above sub 20%, I color it GREEN. I do this with conditional formatting. I sort by color, so that red is on top of my sheet list. All this is done automatically and runs everyday.

This allows me to quickly see which pages have been underperforming for a time period. I chose 90 days to get rid of variance, but I suppose 60 days, might be a better measurement. The goal is to find that middle position, between obsessing over stats and just letting things ride. I sacrifice some earnings on some pages, for scaleability, but I still get alerted of serious declines.

Next up, I'm going to set up Google Analytic alerts for pages that drop more than an percentage in visitors and those pages that drop in goal completions (such as broken scripts etc influencing stuff).
 
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This allows me to quickly see which pages have been underperforming for a time period. I chose 90 days to get rid of variance, but I suppose 60 days, might be a better measurement. The goal is to find that middle position, between obsessing over stats and just letting things ride. I sacrifice some earnings on some pages, for scaleability, but I still get alerted of serious declines.
Your idea's good, but several variables might mess with your numbers big time:
  1. Seasonality (e.g. products that are HOT in summer, but dead in winter)
  2. New & low traffic pages will skew numbers (no performance, high % because of 10 visits with 1 sale while that's not reliable statistically, etc.)
  3. Cookie period etc.
For seasonality it might be worthwhile to plot performance per month of year, while you might (partly) solve for variable #2 by grouping content based on age i.e. what is the avg. performance of 30 day old content, 90 day old, etc. You'll probably have to play around with the buckets (groups) so things don't get too granular.

I'm also trying to tackle this issue ATM, thinking through what kind of data I really need, what's just useless data p*rn, etc.
 

bernard

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Your idea's good, but several variables might mess with your numbers big time:
  1. Seasonality (e.g. products that are HOT in summer, but dead in winter)
  2. New & low traffic pages will skew numbers (no performance, high % because of 10 visits with 1 sale while that's not reliable statistically, etc.)
  3. Cookie period etc.
For seasonality it might be worthwhile to plot performance per month of year, while you might (partly) solve for variable #2 by grouping content based on age i.e. what is the avg. performance of 30 day old content, 90 day old, etc. You'll probably have to play around with the buckets (groups) so things don't get too granular.

I'm also trying to tackle this issue ATM, thinking through what kind of data I really need, what's just useless data p*rn, etc.
Good points, particularly about seasonality.

It's obviously possible to do a much more comprehensive overview, but not in Google Sheets. It would require a proper database with historic data.

My goal here isn't to get precise data, but merely to make me aware if something is awry and needs to be fixed.

That's what I'm aiming for. I don't want to have one of those sites I see around the web with tons of broken affiliate links or content that's outdated. Stuff that could be easily fixed for improved conversions.