This could cost you.

Content creators may have heard about Google's "Helpful content upgrade."

This change is another Google effort to remove low-quality, repetitive, and AI-generated content.

Why should content creators care?

Because too much content manipulates search results.

My experience includes the following.

Website admins seek high-quality guest posts from me. They send me AI-generated text after I say "yes." My readers are irrelevant. Backlinks are needed.

Companies copy high-ranking content to boost their Google rankings. Unfortunately, it's common.

What does this content offer?

Nothing.

Despite Google's updates and efforts to clean search results, webmasters create manipulative content.

As a marketer, I knew about AI-powered content generation tools. However, I've never tried them.

I use old-fashioned content creation methods to grow my website from 0 to 3,000 monthly views in one year.

Last year, I launched a niche website.

I do keyword research, analyze search intent and competitors' content, write an article, proofread it, and then optimize it.

This strategy is time-consuming.

But it yields results!

Here's proof from Google Analytics:

Proven strategies yield promising results.

To validate my assumptions and find new strategies, I run many experiments.

I tested an AI-powered content generator.

I used a tool to write this Google-optimized article about SEO for startups.

I wanted to analyze AI-generated content's Google performance.

Here are the outcomes of my test.

First, quality.

I dislike "meh" content. I expect articles to answer my questions. If not, I've wasted my time.

My essays usually include research, personal anecdotes, and what I accomplished and achieved.

AI-generated articles aren't as good because they lack individuality.

Read my AI-generated article about startup SEO to see what I mean.

It's dry and shallow, IMO.

It seems robotic.

I'd use quotes and personal experience to show how SEO for startups is different.

My article paraphrases top-ranked articles on a certain topic.

It's readable but useless. Similar articles abound online. Why read it?

AI-generated content is low-quality.

Let me show you how this content ranks on Google.

The Google Search Console report shows impressions, clicks, and average position.

Low numbers.

No one opens the 5th Google search result page to read the article. Too far!

You may say the new article will improve.

Marketing-wise, I doubt it.

This article is shorter and less comprehensive than top-ranking pages. It's unlikely to win because of this.

AI-generated content's terrible reality.

I'll compare how this content I wrote for readers and SEO performs.

Both the AI and my article are fresh, but trends are emerging.

My article's CTR and average position are higher.

I spent a week researching and producing that piece, unlike AI-generated content. My expert perspective and unique consequences make it interesting to read.

Human-made.

In summary

No content generator can duplicate a human's tone, writing style, or creativity. Artificial content is always inferior.

Not "bad," but inferior.

Demand for content production tools will rise despite Google's efforts to eradicate thin content.

Most won't spend hours producing link-building articles. Costly.

As guest and sponsored posts, artificial content will thrive.

Before accepting a new arrangement, content creators and website owners should consider this.

900K impressions, 8K clicks, and $$$ orders…

I recently started dropshipping. Now that I own my business and can charge it as a business expense, it feels less like money wasted if it doesn't work. I also made t-shirts to sell. I intended to open a t-shirt store and had many designs on a hard drive. I read that Tiktok advertising had a high conversion rate and low cost because they were new. According to many, the advertising' cost/efficiency ratio would plummet and become as bad as Google or Facebook Ads. Now felt like the moment to try Tiktok marketing and dropshipping. I work in marketing for a SaaS firm and have seen how poorly ads perform. I wanted to try it alone.

I set up $250 and ran advertising for a week. Before that, I made my own products, store, and marketing. In this post, I'll show you my process and results.

Setting up the store

Dropshipping is a sort of retail business in which the manufacturer ships the product directly to the client through an online platform maintained by a seller. The seller takes orders but has no stock. The manufacturer handles all orders. This no-stock concept increases profitability and flexibility.

In my situation, I used previous t-shirt designs to make my own product. I didn't want to handle order fulfillment logistics, so I looked for a way to print my designs on demand, ship them, and handle order tracking/returns automatically. So I found Printful.

I needed to connect my backend and supplier to a storefront so visitors could buy. 99% of dropshippers use Shopify, but I didn't want to master the difficult application. I wanted a one-day project. I'd previously worked with Big Cartel, so I chose them.

Big Cartel doesn't collect commissions on sales, simply a monthly flat price ($9.99 to $19.99 depending on your plan).

After opening a Big Cartel account, I uploaded 21 designs and product shots, then synced each product with Printful.

Developing the ads

I mocked up my designs on cool people photographs from placeit.net, a great tool for creating product visuals when you don't have a studio, camera gear, or models to wear your t-shirts.

I opened an account on the website and had advertising visuals within 2 hours.

Because my designs are simple (black design on white t-shirt), I chose happy, stylish people on plain-colored backdrops. After that, I had to develop an animated slideshow.

Because I'm a graphic designer, I chose to use Adobe Premiere to create animated Tiktok advertising.

Premiere is a fancy video editing application used for more than advertisements. Premiere is used to edit movies, not social media marketing. I wanted this experiment to be quick, so I got 3 social media ad templates from motionarray.com and threw my visuals in. All the transitions and animations were pre-made in the files, so it only took a few hours to compile. The result:

I downloaded 3 different soundtracks for the videos to determine which would convert best.

After that, I opened a Tiktok business account, uploaded my films, and inserted ad info. They went live within one hour.

The (poor) outcomes

As a European company, I couldn't deliver ads in the US. All of my advertisements' material (title, description, and call to action) was in English, hence they continued getting rejected in Europe for countries that didn't speak English. There are a lot of them:

I lost a lot of quality traffic, but I felt that if the images were engaging, people would check out the store and buy my t-shirts. I was wrong.

• 51,071 impressions on Day 1. 0 orders after 411 clicks

• 114,053 impressions on Day 2. 1.004 clicks and no orders

• Day 3: 987 clicks, 103,685 impressions, and 0 orders

• 101,437 impressions on Day 4. 0 orders after 963 clicks

• 115,053 impressions on Day 5. 1,050 clicks and no purchases

• 125,799 impressions on day 6. 1,184 clicks, no purchases

• 115,547 impressions on Day 7. 1,050 clicks and no purchases

• 121,456 impressions on day 8. 1,083 clicks, no purchases

• 47,586 impressions on Day 9. 419 Clicks. No orders

My overall conversion rate for video advertisements was 0.9%. TikTok's paid ad formats all result in strong engagement rates (ads average 3% to 12% CTR to site), therefore a 1 to 2% CTR should have been doable.

My one-week experiment yielded 8,151 ad clicks but no sales. Even if 0.1% of those clicks converted, I should have made 8 sales. Even companies with horrible web marketing would get one download or trial sign-up for every 8,151 clicks. I knew that because my advertising were in English, I had no impressions in the main EU markets (France, Spain, Italy, Germany), and that this impacted my conversion potential. I still couldn't believe my numbers.

I dug into the statistics and found that Tiktok's stats didn't match my store traffic data.

Looking more closely at the numbers

My ads were approved on April 26 but didn't appear until April 27. My store dashboard showed 440 visitors but 1,004 clicks on Tiktok. This happens often while tracking campaign results since different platforms handle comparable user activities (click, view) differently. In online marketing, residual data won't always match across tools.

My data gap was too large. Even if half of the 1,004 persons who clicked closed their browser or left before the store site loaded, I would have gained 502 visitors. The significant difference between Tiktok clicks and Big Cartel store visits made me suspicious. It happened all week:

• Day 1: 440 store visits and 1004 ad clicks

• Day 2: 482 store visits, 987 ad clicks

• 3rd day: 963 hits on ads, 452 store visits

• 443 store visits and 1,050 ad clicks on day 4.

• Day 5: 459 store visits and 1,184 ad clicks

• Day 6: 430 store visits and 1,050 ad clicks

• Day 7: 409 store visits and 1,031 ad clicks

• Day 8: 166 store visits and 418 ad clicks

The disparity wasn't related to residual data or data processing. The disparity between visits and clicks looked regular, but I couldn't explain it.

After the campaign concluded, I discovered all my creative assets (the videos) had a 0% CTR and a $0 expenditure in a separate dashboard. Whether it's a dashboard reporting issue or a budget allocation bug, online marketers shouldn't see this.

Tiktok can present any stats they want on their dashboard, just like any other platform that runs advertisements to promote content to its users. I can't verify that 895,687 individuals saw and clicked on my ad. I invested $200 for what appears to be around 900K impressions, which is an excellent ROI. No one bought a t-shirt, even an unattractive one, out of 900K people?

Would I do it again?

Nope. Whether I didn't make sales because Tiktok inflated the dashboard numbers or because I'm horrible at producing advertising and items that sell, I’ll stick to writing content and making videos. If setting up a business and ads in a few days was all it took to make money online, everyone would do it.

Video advertisements and dropshipping aren't dead. As long as the internet exists, people will click ads and buy stuff. Converting ads and selling stuff takes a lot of work, and I want to focus on other things.

I had always wanted to try dropshipping and I’m happy I did, I just won’t stick to it because that’s not something I’m interested in getting better at.

If I want to sell t-shirts again, I'll avoid Tiktok advertisements and find another route.

Email, landing pages, and digital content

Today's most significant skill:

Copywriting.

Unfortunately, most people don't know how to write successful copy because they weren't taught in school.

I've been obsessed with copywriting for two years. I've read 15 books, completed 3 courses, and studied internet's best digital entrepreneurs.

Here are 8 copywriting frameworks that educate more than a four-year degree.

1. Feature — Advantage — Benefit (F.A.B)

This is the most basic copywriting foundation. Email marketing, landing page copy, and digital video ads can use it.

F.A.B says:

• How it works (feature)

• which is helpful (advantage)

• What's at stake (benefit)

The Hustle uses this framework on their landing page to convince people to sign up:

2. P. A. S. T. O. R.

This framework is for longer-form copywriting. PASTOR uses stories to engage with prospects. It explains why people should buy this offer.

PASTOR means:

• Problem

• Amplify

• Story

• Testimonial

• Offer

• Response

Dan Koe's landing page is a great example. It shows PASTOR frame-by-frame.

3. Before — After — Bridge

Before-after-bridge is a copywriting framework that draws attention and shows value quickly.

This framework highlights:

• where you are

• where you want to be

• how to get there

Works great for: Email threads/landing pages

Zain Kahn utilizes this framework to write viral threads.

4. Q.U.E.S.T

QUEST is about empathetic writing. You know their issues, obstacles, and headaches. This allows coverups.

QUEST:

• Qualifies

• Understands

• Educates

• Stimulates

• Transitions

Tom Hirst's landing page uses the QUEST framework.

5. The 4P’s model

The 4P’s approach pushes your prospect to action. It educates and persuades quickly.

4Ps:

• The problem the visitor is dealing with

• The promise that will help them

• The proof the promise works

• A push towards action

Mark Manson is a bestselling author, digital creator, and pop-philosopher. He's also a great copywriter, and his membership offer uses the 4P’s framework.

6. Problem — Agitate — Solution (P.A.S)

Up-and-coming marketers should understand problem-agitate-solution copywriting. Once you understand one structure, others are easier. It drives passion and presents a clear solution.

PAS outlines:

• The issue the visitor is having

• It then intensifies this issue through emotion.

• finally offers an answer to that issue (the offer)

The customer's story loops. Nicolas Cole and Dickie Bush use PAS to promote Ship 30 for 30.

7. Star — Story — Solution (S.S.S)

PASTOR + PAS = star-solution-story. Like PAS, it employs stories to persuade.

S.S.S. is effective storytelling:

• Star: (Person had a problem)

• Story: (until they had a breakthrough)

• Solution: (That created a transformation)

Ali Abdaal is a YouTuber with a great S.S.S copy.

8. Attention — Interest — Desire — Action

AIDA is another classic. This copywriting framework is great for fast-paced environments (think all digital content on Linkedin, Twitter, Medium, etc.).

It works with:

• Page landings

• writing on thread

• Email

It's a good structure since it's concise, attention-grabbing, and action-oriented.

Shane Martin, Twitter's creator, uses this approach to create viral content.

TL;DR

8 copywriting frameworks that teach marketing better than a four-year degree

• Feature-advantage-benefit

• Before-after-bridge

• Star-story-solution

• P.A.S.T.O.R

• Q.U.E.S.T

• A.I.D.A

• P.A.S

• 4P’s

Christopher Janz of Point Nine Capital created the SaaS napkin in 2016. This post shows how founders have raised cash in the last 6 years. View raw data.

Round size

Unsurprisingly, round sizes have expanded and will taper down in 2022. In 2016, pre-seed rounds were $200k to $500k; currently, they're $1-$2m. Despite the macroeconomic scenario, Series A have expanded from $3m to $12m in 2016 to $6m and $18m in 2022.

Valuation

There are hints that valuations are rebounding this year. Pre-seed valuations in 2022 are $12m from $3m in 2016, and Series B prices are $270m from $100m in 2016.

Compared to public SaaS multiples, Series B valuations more closely reflect the market, but Seed and Series A prices seem to be inflated regardless of the market.

I'd like to know how each annual cohort performed for investors, based on the year they invested and the valuations. I can't access this information.

ARR

Seed firms' ARR forecasts have risen from $0 to $0.6m to $0 to $1m. 2016 expected $1.2m to $3m, 2021 $0.5m to $4m, and this year $0.5m to $2.5m, suggesting that Series A firms may raise with less ARR today. Series B minutes fell from $4.2m to $3m.

Capitalization Rate

2022 is the year that VCs start discussing capital efficiency in portfolio meetings. Given the economic shift in the markets and the stealthy VC meltdown, it's not surprising. Christopher Janz added capital efficiency to the SaaS Napkin as a new statistic for Series A (3.5x) and Series B. (2.5x). Your investors must live under a rock if they haven't asked about capital efficiency. If you're unsure:

The Capital Efficiency Ratio is the ratio of how much a company has spent growing revenue and how much they’re receiving in return. It is the broadest measure of company effectiveness in generating ARR

What next?

No one knows what's next, including me. All startup and growing enterprises around me are tightening their belts and extending their runways in anticipation of a difficult fundraising ride. If you're wanting to raise money but can wait, wait till the market is more stable and access to money is easier.

I was entering tens of millions of records per hour when I first published Slonik PostgreSQL client for Node.js. The data being entered was usually flat, making it straightforward to use INSERT INTO ... SELECT * FROM unnset() pattern. I advocated the unnest approach for inserting rows in groups (that was part I).

However, today I’ve found a better way: jsonb_to_recordset.

jsonb_to_recordset expands the top-level JSON array of objects to a set of rows having the composite type defined by an AS clause.

jsonb_to_recordset allows us to query and insert records from arbitrary JSON, like unnest. Since we're giving JSON to PostgreSQL instead of unnest, the final format is more expressive and powerful.

SELECT *
FROM json_to_recordset('[{"name":"John","tags":["foo","bar"]},{"name":"Jane","tags":["baz"]}]')
AS t1(name text, tags text[]);
 name |   tags
------+-----------
 John | {foo,bar}
 Jane | {baz}
(2 rows)

Let’s demonstrate how you would use it to insert data.

Inserting data using json_to_recordset

Say you need to insert a list of people with attributes into the database.

const persons = [
  {
    name: 'John',
    tags: ['foo', 'bar']
  },
  {
    name: 'Jane',
    tags: ['baz']
  }
];

You may be tempted to traverse through the array and insert each record separately, e.g.

for (const person of persons) {
  await pool.query(sql`
    INSERT INTO person (name, tags)
    VALUES (
      ${person.name},
      ${sql.array(person.tags, 'text[]')}
    )
  `);
}

It's easier to read and grasp when working with a few records. If you're like me and troubleshoot a 2M+ insert query per day, batching inserts may be beneficial.

What prompted the search for better alternatives.

Inserting using unnest pattern might look like this:

await pool.query(sql`
  INSERT INTO public.person (name, tags)
  SELECT t1.name, t1.tags::text[]
  FROM unnest(
    ${sql.array(['John', 'Jane'], 'text')},
    ${sql.array(['{foo,bar}', '{baz}'], 'text')}
  ) AS t1.(name, tags);
`);

You must convert arrays into PostgreSQL array strings and provide them as text arguments, which is unsightly. Iterating the array to create slices for each column is likewise unattractive.

However, with jsonb_to_recordset, we can:

await pool.query(sql`
  INSERT INTO person (name, tags)
  SELECT *
  FROM jsonb_to_recordset(${sql.jsonb(persons)}) AS t(name text, tags text[])
`);

In contrast to the unnest approach, using jsonb_to_recordset we can easily insert complex nested data structures, and we can pass the original JSON document to the query without needing to manipulate it.

In terms of performance they are also exactly the same. As such, my current recommendation is to prefer jsonb_to_recordset whenever inserting lots of rows or nested data structures.