Analyzing $145,000 of CPA payouts

[affiliate99]

I am currently seeking to establish a foothold in internet marketing, primarily through the promotion of CPA offers.

My current approach is using a popular method and to automating.

The actions I have taken:

1. Generating ~1000 number of keywords / topics and corresponding landing pages within my designated niche
2. Creating videos for each landing page (Remotion)
3. Using a YouTube bot to schedule the distribution of each video across a set of 20 aged YouTube channels
4. Using an SMM panel to deliver approximately 50 to 100 views to boost each video

Results:

1. All 20 associated accounts have been subjected to suspension.
2. A total revenue of $8 has been generated.
3. The entire revenue amount has originated from a single video.

Potential Next Steps:

1. Manually create high quality videos?
2. Run tests to figure out the best method of promoting videos?
3. Investigate other methods instead of trying to promote CPA offers using free traffic, as I'll fighting against Google's algorithm

As part of this project, I analyzed the offer feed of a prominent CPA network to ascertain its viability and identify the most promising areas for directing my efforts. In a span of approximately two months, I collected data from random one-hour samples, resulting in a total of about $145,000 in completed offers. Here's a breakdown of user earnings based on revenue thresholds:

• 10 users earned more than $600
• 20 users earned more than $400
• 83 users earned more than $200
• 222 users earned more than $100
• 500 users earned more than $50
• 935 users earned more than $25
• 1728 users earned more than $10
• 5400 users earned more than $0

This distribution follows a Pareto pattern, with a small fraction of users contributing the majority of revenue to the platform. A similar trend is observed among top-performing offers, where a minority of offers generate the bulk of the platform's revenue:

• 6 offers with over $10,000 total payout
• 22 offers with over $1000 total payout
• 42 offers with over $500 total payout
• 163 offers with over $100 total payout
• 250 offers with over $50 total payout
• 350 offers with over $25 total payout
• 1000 offers with over $1 total payout

Given this data, I am deliberating whether to continue pursuing my current project trajectory, considering that a mere 0.01% of platform users are potentially earning an estimated $1,000 to $2,000 per month. It's important to acknowledge that this data may be skewed, as certain users have the option to exclude their leads from the feed, possibly excluding many of the top-earning and experienced users from this dataset.

I have also analyzed every message in the network chatbox during this time, none of the top users interacted with the chat during the data collection window. All the messages were low quality and did not yield any useful information.

 

[Graybeard]

WELCOME ABOARD ...

━━━●──────────────
This is an introduction thread. Like to say hello and maybe tell the community about yourself and what you are doing.
This is a bit detailed and complex however.

You have attempted a lot and to see $8 in return for all of this work --this has to be a bit frustrating to say the least.

About this data: what have you surmised apart from individual users earnings or the revenue of each offer globally? What good will any of that do really?

You need transactional line attributes to make based and empirical findings.

• 10 users earned more than $600
• 20 users earned more than $400
• 83 users earned more than $200
• 222 users earned more than $100
• 500 users earned more than $50
• 935 users earned more than $25
• 1728 users earned more than $10
• 5400 users earned more than $0

Forget about the -0's They are mostly inactive (or useless) ...

But I fail to see what this tells you that is of value to you
--unless you are a part of the cohort and want to compare your performance (ranking)
--then it might be useful.

6 offers with over $10,000 total payout

• 22 offers with over $1000 total payout
• 42 offers with over $500 total payout
• 163 offers with over $100 total payout
• 250 offers with over $50 total payout
• 350 offers with over $25 total payout
• 1000 offers with over $1 total payout

But why? What are the data-points you have to consider and what makes you think they are reliable?
If the source is some user provided webpage you may just be chasing ghosts and wasting your time.

If you do not have the line data --the real transactions (clicks, events, conversions) --the real sources (media placement names) to maybe be able to scale cohorts --I think you are just pulling your chain and fooling yourself.

If you have any real transactional line data of affiliate offers to share I would be very interested in analyzing it.

---

An Example:

This data is what is furnished to the SEC (Securities and Exchange Commission) under criminal penalty for any provable fraud. It is supposed to comply with GAAP (generally accepted accounting principles) in some cases it is audited to CPA standards. --most likely reliable and factual.

the attributes used in the analysis are:

1. Symbol,
2. fiscalDateEnding
3. profitLoss
4. paymentsForRepurchaseOfCommonStock
5. column headings or computed results are:
   pct_changeprofitLoss,
   $StockByBack,
   pctCg_StockByBack (pctCg =percentage of change from the preceding quarter's report [10-Q])
6. paymentsForRepurchaseOfCommonStock / profitLoss AS buyBackRATIO

Conclusion: the stock WRB is making a profit and is pumping the market value while paying less than a 2% dividend per year.

Forget the 'chicken tracks (work product)' and look at the pale orange line and the R factor 77 out of 100 confidence of the increase of about 25% in the stock's market value in the next 10- 18 months, as the profit trends will allow to the continued pumping of the market price.
No "unusual" insider dumping pattern ...

SEC.gov | EDGAR Full Text Search

Form 4 Transaction Codes

View all SEC transaction codes found on Form 4 filings.

stocktrot.com

 

[T J Tutor]

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[VirtualGlobalPhone]

@affiliate99 , welcome to affiliatefix