How can a first-time Amazon seller use Campaign Architecture Engine to structure Sponsored Products, control waste, and lift organic orders in 21 days?
(A long and in-depth article with over 10,000 words, it is recommended to save it and read it again)
1
Core idea: Randomness is the "low cost" with the greatest opportunity, and advertising is the greatest "truth argument"
The underlying logic of the methodology discussed in this article is very simple. Maximize the use of Amazon's randomness, use the extreme "small budget" + "low cost" to obtain a large number of "long-tail keywords", and then use a set of sufficiently restrained "ad-discipline" (Ad-Discipline) to graft these golden scattered traffic onto the listing weight, and finally in turn drive the natural ranking of core keywords. In the practical process of this methodology, the core is how to calculate the "most accurate" and "most cost-effective" bid, and use the powerful algorithm of the Amazon platform to naturally "match exposure" for the massive long-tail keywords in the SP panoramic ad group.
💡 ps: For data demonstration on Amazon's stochastic process, please refer to the previous article "Incremental Snatching War in the Era of AI High Traffic on Amazon's Global Sites".
This article focuses on how the "random strategy" is put into practice: when the combination of "super keywords" + "super ASINs" begins to see a "number increase", it means that product links have completed the Normandy login - and at the same time seized search traffic + associated traffic.
The greatest value of this strategy is to give links a strong sense of security: products can be copied, pictures and texts can be copied, but "advertising is difficult to copy." Especially when the scale of keywords and ASINs is large enough, new words and new ASINs will appear in every 7-day attribution window; it is difficult to manually track this matter in a long-term and stable manner, let alone achieve "continuous iteration". The randomness of traffic will turn into a moat in this kind of large-scale scrolling - invisible, intangible, and impossible to copy!
From a more realistic perspective, a lot of growth is not about "a certain skill will kill you in one hit", but about repeating the right actions during the right window period. The right time, right place and right people, timing accounts for 90%. The dividend of the AI era is to make this kind of "correct action at scale" executable and sustainable - the time of strategy, the place of strategy.
2
SP Panoramic View naked opening method: 21-day advertising ROAS = 4.83, how to achieve more than 50% of natural orders?
Look at the pictures and talk: Advertising orders and natural orders
Without further ado, let’s start with the case. Let’s lay out the results first, and then dismantle the execution actions and underlying logic behind them—the key point is: these actions are designed based on the traffic distribution rules within Amazon’s site, and do not rely on “slap on the head”. One product has this result, 10 products, and 100 products all have this result. Finally, the results are aligned!
📊 Data statistics period: December 1st - December 21st, 2025 (21 days in total)
📈 Overall data: Total number of orders: 1117
📈 520 advertising orders
📈 597 natural orders (accounting for 53%).
📈 Let’s talk about the advertising order: ROAS = 4.83
📈 Let’s look at the picture of natural orders: the total number of orders is 1117, and the number of natural orders is 597 (accounting for 53%)
A timeline explains clearly: how to escape from "digging words → burying words → uncovering → scrolling and zooming in" in 21 days
The core of these 21 days is not "magic operations", but a set of reusable full-link actions : First make the word pool bigger, and then filter the words accurately; first lay down the words firmly, and then use the panoramic architecture to run; finally, rely on a stable delivery rhythm to let the system filter out high-quality traffic and amplify it on a rolling basis.
🚀 Step 1｜Create an initial vocabulary (a few minutes)
The first step is to do only one thing: spread the "explorable word pool" first.
Use the [Keyword Mining]-R1 reasoning mode of Satlis to mine keywords containing COSMO expressions, and you can get an initial set of lexicon in a few minutes.
🚀 Step 2｜Select precise keywords (the longest time consuming, but the most valuable)
The second step is the most "time-consuming" step in the entire link, and it is also the key to whether you can get a natural order later.
Use the dual test function of Satlis to create real-time tasks in batches on the Amazon front desk. The cumulative calculation amount is about one million times, and it usually takes a whole night to run. The machine runs and the results are obtained directly the next day.
The value here lies in: turning "correlation" from empirical judgment into verifiable results . Especially for long-tail words - which words really have natural vacancies, and which words just seem relevant but don't actually bring about natural growth, this step will make a difference.
🚀 Step 3｜AI copywriting (deciding "whether there is a natural order")
The third step is to truly "precipitate" the precise keywords screened out in the second step into the listing.
Use the AI Listing function of Satlis to bury words in copywriting. The goal is very clear:
Words must be accurate (relevance is high enough)
The words should be buried enough (the coverage density is sufficient)
If this step is not done firmly, a typical phenomenon will often occur later: advertising can generate orders, but naturally the orders cannot be generated.
The reason is not complicated: advertising brings traffic, but the link itself cannot handle "natural crawling", so it is naturally difficult to continue to increase.
In addition, the title carries a lot of weight. Here, the latest inference model of Satlis is used to generate the title structure and number of characters at once, and will not be modified repeatedly in the future. The generation time will be longer, about 5-7 minutes, but the results are worth it.
🚀 Step 4｜AI advertising panoramic view (let the "long tail + association" start rolling)
The fourth step is to use Satlis's "SP Panoramic View Architecture" to directly use the customer search terms predicted by the system to open ads. The point is not to bet on a certain word, but to allow different types of traffic portals to run at the same time:
n
Super high probability keywords (cold start words)
: Will be marked after double testing
"
Ultra-high Probability
" tag, used as a cold start word pool, this group of ads
ROAS = 5.13
. Looking back, I ran a million front-end calculations the night before and it was very cost-effective.
n
Super keywords
: in the second
7
The daily attribution cycle ran out
ROAS = 5.59
, belongs to the group that can make money stably.
n
High competition keywords
: run out
ROAS = 4.58
. This group of bidding engines gives a more restrained bid (about half of the recommended bid in the Amazon merchant backend), and achieves high production at a lower cost, which is also verified from the side: when the long-tail word pool starts to give links
After "right promotion", the cost of issuing orders for core words will be more easily reduced.
n
Super
ASIN
ROAS = 5.24
. The importance of associated traffic is obvious here. It is more random, but once it rolls up, the increment it brings is also more considerable. associated with the randomness of traffic, generally speaking, before
3-14
Days vary, and there may not be exposure, but once exposure is obtained, the production ratio and order volume will be very strong!
"Advertising Discipline" of Panoramic Architecture: In one sentence - open naked, etc., only close
Under the SP Panoramic View structure, advertising operations can be condensed into one sentence: Nude, open, etc., only close .
The core is not "how smart", but make fewer mistakes and make less noise , let the system filter out the optimal traffic in a stable framework.
Three disciplines (minimize variables)
✅ 1) Bidding consistency principle
An SP panoramic ad group only gives one "bid". You can directly give the group the "highest bid" based on the bid calculated by Satlis.
The purpose of this is very clear: Reduce variables so that the system can more quickly determine whether this set of traffic is worth increasing. .
✅ 2) Attribution consistency principle
SP panoramic ad groups should be opened at the same time to maximize the consistency of the attribution window.
The attribution time is consistent, making it easier for the system to compare and learn the performance of different groups at the same stage, and the convergence speed is faster.
✅ 3) Action consistency principle
If the performance of keywords or ASINs in the group is obviously not good, it will be closed; otherwise, no other operations will be performed.
The key point of this discipline is to use to complete noise reduction with minimal movement , to avoid "the more control the more chaos becomes."
Why be so "restrained"? Because the cost of frequent adjustments is often underestimated
To put it bluntly, Amazon advertising is more like a "quantitative system" that is constantly testing and screening.
In this system, discipline is often more important than "how detailed the analysis is":
Frequently adjust bids, change budgets, split groups, deny words...it seems to be a refined management;
But from a system perspective, many changes are more like introducing new variables and noise;
The result is: the system takes longer to see clearly which change has brought about the results, and the volume will be increased more cautiously.
So the emphasis here is not on "not optimizing at all", but on: minimizing the actions in the key stage to maximize the learning efficiency of the ad group .
"Close only principle" is clearly stated separately: only look at the number of clicks, and don't worry about short-term fluctuations
The implementation of the shutdown-only principle is very simple:
Keywords/ASINs in the ad group will not be adjusted during this period;
Only look at one metric: the number of clicks.
Be conservative (take the US site as an example): if no order is placed after 15 clicks, it will be closed.
Take the most common scenario: in the "super keyword" group, certain words can get exposure and generate clicks, but if the total number of clicks exceeds 15, there are still no orders - such keywords or ASINs will be closed directly.
We usually observe a phenomenon: as bad samples are eliminated, the system will automatically select better quality ones from the remaining keywords or ASINs to allocate exposure, and the performance within the group will more easily converge in a good direction.
The entire operation experience is "simple, brisk and sustainable":
Focus on "screening out the obviously bad ones" instead of spending time on repeated price adjustments and repeated monitoring of data . For example, Huang Zheng relayed one of Buffett's metaphors in an interview: If Yao Ming walks in in a restaurant, everyone can tell at a glance that he is very tall; on the other hand, if you can't tell that the person is "very tall", there is a high probability that the person is tall and not noticeable! Put in the SP panoramic ad group, really better keywords and ASINs are usually easier and faster to "reveal" under the same set of rules; if you still can't see the difference after running for a while or the data of a certain copula or ASIN is not good, then there is a high probability that it is bad, so close it decisively! ——This is also the logical basis of "less fuss, just close".
3
SP Panoramic View architecture naked opening case hodgepodge: from the United States to the European station "kill"
SP Panoramic View full version and slim version (with case data)
In the SP panoramic ad group, there are 6 ad groups with forecast data , for other ultra-high probability keywords, you must follow Satlis's word mining → double test screening → generate accurate keywords to get the advertising cold start keywords marked as "Ultra-high Probability". After importing the Amazon report, check the SP Panoramic View data of the ASIN. If the 6 "predictive ad groups" have data, then the advertising structure of the ASIN is called " Panorama full version ":
Simple understanding: the full configuration version has more complete traffic entrances for running, and there is more space for subsequent expansion.
If there are 6 predictive ad groups, some of which have no data, it is called " Panoramic slim version ":
Simple understanding: In the slim version, all entrances have not been opened. It doesn't mean that it can't be done, it just means that the "traffic types that can be received" by this link at the current stage are not complete enough.
SP Panoramic View full and slim versions are predicted by neural network algorithms based on the actual data in the advertising store, ensuring a 95% confidence interval: that is to say, Satlis will predict customer search terms based on order data (successful samples) and no order data (failure samples), simulated 100 times of delivery, and 95 times met the system's preset multi-goals (for example, simultaneous fulfillment of orders > 300 orders, conversion rate > 15%, ACoS
Screening stage
Quantity
/
Proportion
Description
original record
51,309
Article
30
All search terms in daily advertising reports
/ASIN
Record
The only word after removing duplicates
22,212
piece
Aggregation
Independent customer search terms
/ASIN
Effective words
4,059
piece
The order rate is only
- 27%
means
- 73% The word for is
"
Invalid noise
"
AI
Select high-quality words
3,771
piece
经
Satlis
Effective words recognized by panoramic classification algorithm
super word
/ASIN
3,273
piece
ACoS
Only
- 04%
, contributed
72%
's order
Judging from the evaluation data, the funnel effect of this framework is extremely significant. It proves two key facts:
The prevalence of noise : In unfiltered advertising traffic, more than 80% of click costs go to "noise words" that do not generate orders. This is the fundamental reason why ACoS remains high.
High concentration of value : Through the precise screening of the AI algorithm, the "super" words (accounting for more than 80% of the words) that were finally settled contributed more than 70% of the orders with an extremely low ACoS of only 8.04%, demonstrating amazing delivery efficiency. So these long tail value contribution words , is Amazon randomness + high variance The distribution logic background!
Long tail effect verification: random orders are the cornerstone of sales
Another core theory of the panoramic framework is to use "random orders" of massive long-tail words to increase the ranking of Listings. This 30-day real data also verifies the correctness of this theory:
Look at the watch and talk:
Order range
word
/ASIN
Quantity proportion
Proportion of orders
1
Single (extremely random)
- 25%
- 95%
2
Single (high random)
- 29%
- 24%
3-5
Single (long tail)
- 25%
- 63%
6-10
Single (medium long tail)
- 13%
- 70%
11
Single or above (core)
- 07%
- 53%
📈 Key insight: Random orders (1-2 orders) accounted for 86.54% of the total word volume, contributing 45.19% of the total order volume:
This data irrefutably proves that those massive long-tail words that are ignored by traditional word selection tools and cannot enter the ABA ranking are precisely the stable cornerstone of product sales. Any advertising strategy that ignores this part of the traffic is tantamount to giving up nearly half of the potential market.
Accuracy of AI classification: Comparison of panoramic classification effects
The execution effect of SP Panoramic View framework is highly dependent on its AI ability to accurately classify keywords. By comparing the actual performance of different categories such as "Super", "High Potential", "High Competition", etc., the accuracy of its algorithm can be evaluated:
Look at the watch and talk:
Panorama classification
word count
Number of orders
Sales
ACoS
super
3,273
5,114
$67,210
- 04%
High potential
346
1,580
$22,138
- 45%
High competition
138
388
$5,480
- 39%
Secondary potential
14
32
$472
- 90%
global average
- 44%
Judging from the evaluation results, the classification effect of AI is very significant:
"Super" category : Contributes the vast majority of orders with the highest efficiency (ACoS is only 8.04%), and is the core source of profits.
"High Potential" classification : ACoS is the same as the global average, but the order contribution is huge, and it is an incubation pool for future "super" words.
"High Competition" category : ACoS is well above average and belongs to the "strategic defense zone" that requires strict budget control.
This clear classification makes it possible to implement differentiated budget allocation and bidding strategies, so that advertising expenditures can be accurately invested in the most efficient traffic.
Analysis of algorithm principles: learn "success" from "failure"
From a technical perspective, the accuracy of the Satlis prediction algorithm comes from a set of rigorous data science logic rather than some incomprehensible "black box".
Build an exclusive time series model: The system requires users to import three core reports (promoted products, search terms, advertising spaces) in the last 30 days to create a unique "DNA map" for each store.
Define the "success/failure" probability space: The system divides a large number of search terms/ASINs into two core sample spaces based on "whether the number of orders is 0" - "failure probability space" (words with an order of 0) and "success probability space" (words with an order greater than 0).
Neural network deep learning: AI compares these two "probability spaces" day and night, and autonomously learns and refines the core patterns that determine order conversion from tens of thousands of cases. According to its official documentation, it can accurately predict the multi-target performance of a keyword or ASIN in the next 30 days with a 95% confidence interval.
Evaluation conclusion:
The core of the Satlis prediction algorithm lies in valuing users' ignored "failure data" and allowing AI to gain insight into the growth patterns of specific stores by comparing "success" and "failure". From a technical perspective, this deep learning method based on the user's own historical data is the basis for achieving accurate predictions and assisting sellers in making data-driven decisions:
In summary, through an in-depth deconstruction of 30 days of real data, we can see that the "SP Panoramic View framework" is not an empty concept, but a practical system with complete data support, logical closed loop, and significant effects. It provides Amazon sellers with a systematic approach to harnessing the inherent “randomness” of the platform and turning it into a predictable, optimizable growth engine.
5
SP Panoramic View Pitfall Guide: Practical Operation
⚠️ Pitfall 1: Uncontrollable hands - Always trying to adjust prices, but it turns out to be a "noise-making machine"
The "bidding engine" of the SP panoramic ad group is based on the number of exposures in the advertising report data and uses time series modeling to calculate the recommended bidding range, accurate to the "percentile". After testing, the "highest bid" can be used directly for the best results. Each SP panoramic ad group, no matter how many keywords or ASINs there are, will be given "one bid"!
⚠️ Pitfall 2: High clicks are not placed and orders are not closed in time, causing ROAS to drop rapidly
According to the "discipline" of SP Panoramic View, for US sites, if keywords with high clicks but no orders are not closed in time, or are closed too early, the former will lower the advertising ROAS, and the latter will miss the opportunity to place orders. Under normal circumstances, the US site can have a lower fault tolerance (for example, orders of less than 50 US dollars will be closed based on 15-20 clicks), while Europe's fault tolerance is higher (for example, orders of less than 50 US dollars will be closed based on 20-25 clicks).
⚠️ Pitfall 2: Listing "streaking" - the advertisement is on, but the words are not done well
Copywriting is the "base" of natural orders, especially after millions of real-time calculations at Amazon's front desk. Keyword collection performance, natural position competition status, and natural position vacancy status are all scientific calculations. The calculation time of this process, for example, 30 ASINs, 30,000 keywords, the total calculation time is about 20 hours (dual tests are run in parallel at the same time, which requires high computer configuration) to 40 hours (double tests are run in series). If you do this step, it is "science", if you don't do it, it is "feeling and experience". Listing copywriting is a once-and-for-all job, once in a lifetime, done in one step!
⚠️ Pitfall 3: Superstitious "big words" - spend all your budget on top words
Through this article, the "randomness" + "high variance" of Amazon traffic has been proven. The "exposure" of keywords in the time series is close to the Poisson distribution. The key word is not not to invest, but to invest based on the numbers calculated by the “Panorama Bidding Engine” and “Panorama Budget Engine”. If the core big words do not appear in the "high competition keywords", then it is recommended that the core big words directly give priority to the bidding of "high competition keywords"; if there are no "high competition keywords", then the bidding suggestion is to use "super keywords with a 60-20% discount". Because the core of Panorama's 8-level advertising structure is to use "massive keywords" from other ad groups to hit the homepage, the order cost for "core keywords" will be reduced a lot!
⚠️ Pitfall 4: The associated traffic cannot wait patiently - the ad group is closed without ASIN exposure
Note that ASIN fixed investment is a typical "associated traffic", which is more random. For details, see Chapter 3.5 of the previous article "Incremental Snatching War in the Era of Amazon Global Site AI High Traffic" Keywords vs. ASIN: Who has stronger "randomness"? Therefore, even if you don’t get exposure for 14 days, you must wait patiently for random events to happen. Because there is no cost, no exposure → no clicks → no cost. This is a zero-cost transaction, you only make money without losing money!
📋SP Panoramic View Pitfalls Cheat Sheet
The following is a quick reference list for SP Panoramic View pitfalls for easy query:
serial number
Misunderstanding name
Correct approach
1
Frequently call up bids
Maximum bid calculated using bid engine
2
High clicks do not place orders and are not closed in time
US site
15-20
Click to close, European site
20-25
Click to close
3
Listing
Streaking
First do a double test to bury the words, and the copywriting is in place in one step
4
Superstitious big words
Use the panorama bidding engine to calculate, or press the super word
6-8
fold
5
ASIN
Close if not exposed
Wait patiently, zero cost, only profit and no loss
— END —
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