*By Kostiantyn Shurupov, an expert in digital marketing and affiliate business development*

### **Abstract**

In many companies, affiliate marketing is still perceived as a set of “hacks” and one-off deals with partners. As a result, decisions are made based on feelings rather than data, and the program either does not scale or turns into a source of uncontrolled expenses. Meanwhile, in highly competitive and regulated niches, it is precisely the affiliate channel that can become the main source of predictable growth — provided that it is built as a system: with clear metrics, transparent analytics, and constant experiments.

The article examines how to transform affiliate marketing from a “black box” into a controllable growth mechanism. It covers basic metrics (EPC, CR, LTV, payback), principles of funnel construction, approaches to A/B testing of landings and creatives, as well as practices for working with partners as a distributed experimental laboratory. Current data on the affiliate marketing market and the online gaming industry are provided, illustrating the scale and potential of this channel.

**Keywords:** data; affiliate marketing; experiment; A/B testing; analytics; funnel; online gaming; poker; EPC; LTV

### **Introduction**

The global affiliate marketing market is growing, amounting to tens of billions of dollars and providing a significant share of online sales. In parallel, the online gaming and poker markets are developing, where competition for the user is especially intense, and regulatory restrictions require careful work with communication and data.

In such an environment, affiliate marketing works only in one format — as a system. Its core is data: without transparent analytics, a correct attribution window, and an understanding of user economics, any program turns into an argument about whether the partner is doing a good job or not.

![](https://cdn-images-1.medium.com/max/1600/1*WTHMkA8VeBsfm6M8IOkDpA.png)

### **1. Metrics Without Which the System Does Not Work**&#xA;&#xA;

The basic set of metrics for the affiliate channel is fairly compact:

**CR (Conversion Rate)** — conversion from clicks into the target action (registration, deposit, subscription).

**EPC (Earnings Per Click)** — earnings per click for the partner, the key indicator of an offer’s attractiveness.

**ARPU / ARPPU** — average revenue per user / per paying user.

**LTV** — total revenue from a user over their lifetime.

**Payback Period** — the payback time of marketing investments.

These metrics make it possible to link two worlds — the business and the partners: the partner sees EPC and conversion, the business sees CAC and payback. If the data is transparent, a space for constructive dialogue emerges: you can discuss landing page tests, offer changes, traffic segmentation.

### 2. **The Funnel as an Object of Analysis, Not a “Black Box”**

A typical mistake is to look only at the top of the funnel (clicks and registrations) and the final result (deposits, payments), while ignoring what happens between them. In practice when working with the affiliate channel in highly competitive niches, two layers of analysis are especially important.

### 2.1. Micro-conversions

The funnel is broken down into micro-steps:

* landing page view;
* click on the CTA;
* start of registration;
* completion of registration;
* first entry into the product;
* first target action (deposit, subscription, purchase);
* repeated target action.

Each step is measured by partner, geo, and traffic sources. This makes it possible, for example, to see that some partners bring an audience that clicks well but “drops off” at registration, or vice versa — rarely reaches the landing page but converts well afterwards.

### 2.2. Separating Product and Marketing Problems

If conversion drops at the registration step, the probability of a product problem is high: a complicated form, bugs, lack of localization.
If users register but do not reach the first payment — the question is about onboarding and the value proposition.

Such separation makes the conversation with a partner constructive: instead of the abstract “traffic is weak,” specific hypotheses and experiments are discussed.

### 3. **Experiments: How to Test Offers, Landings, and Creatives**

The affiliate channel offers a unique opportunity to run distributed experiments: dozens of partners simultaneously test different combinations of “source — creative — landing — offer.”

Key principles:

* **Limited number of variables.** Only one element changes in each experiment: the landing headline, the wording of the offer, or the visual.
* **Sufficient volume.** The experiment continues until a statistically significant number of clicks and conversions is reached, not until the first “nice-looking” result.
* **Unified attribution windows.** The same conversion crediting rules apply for all partners; otherwise comparing results becomes meaningless.

On the X-axis are the landing variants, on the Y-axis — the conversion from click to registration. This visualization makes it easy to communicate to product and partners which hypothesis “won” and which elements should be scaled.

### 4. Online Games and Poker: The Affiliate Channel as a Growth Laboratory

Online games and poker are examples of categories where affiliate marketing is especially important. The global online gambling market is valued at tens of billions of dollars, with steady growth expected through the end of the decade.

The online poker market is also growing: the global volume, estimated at several billion dollars in 2024, is projected to double by 2030.

From the standpoint of data and experiments, such markets are convenient: user LTV is high, there are many events inside the product, and user behavior lends itself well to quantitative analysis. This makes it possible to:

* build segmented partner reward models (different rates for different player cohorts);
* test changes in product economics (rake, bonuses, missions) on limited volumes of affiliate traffic;
* quickly see the effect of changes in the funnel and communication.

### Conclusion

Affiliate marketing ceases to be a collection of disparate deals and becomes an engineered growth system where decisions are made based on data. Clearly defined metrics, a detailed funnel, continuous experiments with creatives and offers, as well as working with partners as full participants in the product process, make it possible to achieve predictable results even in complex and regulated niches.

In an environment where traffic costs are rising and regulators are tightening requirements, exactly this approach — systematic, data-driven — gives a business the chance to scale the affiliate channel without losing manageability and profitability.

1. Ahrefs / Authority Hacker. Affiliate Marketing Statistics 2024.[Ahrefs](https://ahrefs.com/blog/affiliate-marketing-statistics/?utm_source=chatgpt.com)
2. Wix. Affiliate marketing statistics and facts for 2025.[wix.com](https://www.wix.com/blog/affiliate-marketing-statistics?utm_source=chatgpt.com)
3. [ResearchAndMarkets.com](https://ResearchAndMarkets.com). Online Gambling Industry Report 2025.[Business Wire](https://www.businesswire.com/news/home/20250924101188/en/Online-Gambling-Industry-Report-2025---Global-Market-Trends-Regulation-Forecast-2024-2033---ResearchAndMarkets.com?utm_source=chatgpt.com)
4. Yahoo Finance. Online Poker Global Strategic Business Report 2025–2030.[Yahoo Финансы](https://finance.yahoo.com/news/online-poker-global-strategic-business-095300195.html?utm_source=chatgpt.com)
5. Industry reviews on A/B testing and performance marketing

<p><em>By Kostiantyn Shurupov, an expert in digital marketing and affiliate business development</em></p>
<h3 id="abstract"><strong>Abstract</strong></h3>
<p>In many companies, affiliate marketing is still perceived as a set of “hacks” and one-off deals with partners. As a result, decisions are made based on feelings rather than data, and the program either does not scale or turns into a source of uncontrolled expenses. Meanwhile, in highly competitive and regulated niches, it is precisely the affiliate channel that can become the main source of predictable growth — provided that it is built as a system: with clear metrics, transparent analytics, and constant experiments.</p>
<p>The article examines how to transform affiliate marketing from a “black box” into a controllable growth mechanism. It covers basic metrics (EPC, CR, LTV, payback), principles of funnel construction, approaches to A/B testing of landings and creatives, as well as practices for working with partners as a distributed experimental laboratory. Current data on the affiliate marketing market and the online gaming industry are provided, illustrating the scale and potential of this channel.</p>
<p><strong>Keywords:</strong> data; affiliate marketing; experiment; A/B testing; analytics; funnel; online gaming; poker; EPC; LTV</p>
<h3 id="introduction"><strong>Introduction</strong></h3>
<p>The global affiliate marketing market is growing, amounting to tens of billions of dollars and providing a significant share of online sales. In parallel, the online gaming and poker markets are developing, where competition for the user is especially intense, and regulatory restrictions require careful work with communication and data.</p>
<p>In such an environment, affiliate marketing works only in one format — as a system. Its core is data: without transparent analytics, a correct attribution window, and an understanding of user economics, any program turns into an argument about whether the partner is doing a good job or not.</p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*WTHMkA8VeBsfm6M8IOkDpA.png" alt=""></p>
<h3 id="1-metrics-without-which-the-system-does-not-work"><strong>1. Metrics Without Which the System Does Not Work</strong>

</h3>
<p>The basic set of metrics for the affiliate channel is fairly compact:</p>
<p><strong>CR (Conversion Rate)</strong> — conversion from clicks into the target action (registration, deposit, subscription).</p>
<p><strong>EPC (Earnings Per Click)</strong> — earnings per click for the partner, the key indicator of an offer’s attractiveness.</p>
<p><strong>ARPU / ARPPU</strong> — average revenue per user / per paying user.</p>
<p><strong>LTV</strong> — total revenue from a user over their lifetime.</p>
<p><strong>Payback Period</strong> — the payback time of marketing investments.</p>
<p>These metrics make it possible to link two worlds — the business and the partners: the partner sees EPC and conversion, the business sees CAC and payback. If the data is transparent, a space for constructive dialogue emerges: you can discuss landing page tests, offer changes, traffic segmentation.</p>
<h3 id="2-the-funnel-as-an-object-of-analysis-not-a-blackbox">2. <strong>The Funnel as an Object of Analysis, Not a “Black Box”</strong></h3>
<p>A typical mistake is to look only at the top of the funnel (clicks and registrations) and the final result (deposits, payments), while ignoring what happens between them. In practice when working with the affiliate channel in highly competitive niches, two layers of analysis are especially important.</p>
<h3 id="21-micro-conversions">2.1. Micro-conversions</h3>
<p>The funnel is broken down into micro-steps:</p>
<ul>
<li>landing page view;</li>
<li>click on the CTA;</li>
<li>start of registration;</li>
<li>completion of registration;</li>
<li>first entry into the product;</li>
<li>first target action (deposit, subscription, purchase);</li>
<li>repeated target action.</li>
</ul>
<p>Each step is measured by partner, geo, and traffic sources. This makes it possible, for example, to see that some partners bring an audience that clicks well but “drops off” at registration, or vice versa — rarely reaches the landing page but converts well afterwards.</p>
<h3 id="22-separating-product-and-marketing-problems">2.2. Separating Product and Marketing Problems</h3>
<p>If conversion drops at the registration step, the probability of a product problem is high: a complicated form, bugs, lack of localization.
If users register but do not reach the first payment — the question is about onboarding and the value proposition.</p>
<p>Such separation makes the conversation with a partner constructive: instead of the abstract “traffic is weak,” specific hypotheses and experiments are discussed.</p>
<h3 id="3-experiments-how-to-test-offers-landings-and-creatives">3. <strong>Experiments: How to Test Offers, Landings, and Creatives</strong></h3>
<p>The affiliate channel offers a unique opportunity to run distributed experiments: dozens of partners simultaneously test different combinations of “source — creative — landing — offer.”</p>
<p>Key principles:</p>
<ul>
<li><strong>Limited number of variables.</strong> Only one element changes in each experiment: the landing headline, the wording of the offer, or the visual.</li>
<li><strong>Sufficient volume.</strong> The experiment continues until a statistically significant number of clicks and conversions is reached, not until the first “nice-looking” result.</li>
<li><strong>Unified attribution windows.</strong> The same conversion crediting rules apply for all partners; otherwise comparing results becomes meaningless.</li>
</ul>
<p>On the X-axis are the landing variants, on the Y-axis — the conversion from click to registration. This visualization makes it easy to communicate to product and partners which hypothesis “won” and which elements should be scaled.</p>
<h3 id="4-online-games-and-poker-the-affiliate-channel-as-a-growth-laboratory">4. Online Games and Poker: The Affiliate Channel as a Growth Laboratory</h3>
<p>Online games and poker are examples of categories where affiliate marketing is especially important. The global online gambling market is valued at tens of billions of dollars, with steady growth expected through the end of the decade.</p>
<p>The online poker market is also growing: the global volume, estimated at several billion dollars in 2024, is projected to double by 2030.</p>
<p>From the standpoint of data and experiments, such markets are convenient: user LTV is high, there are many events inside the product, and user behavior lends itself well to quantitative analysis. This makes it possible to:</p>
<ul>
<li>build segmented partner reward models (different rates for different player cohorts);</li>
<li>test changes in product economics (rake, bonuses, missions) on limited volumes of affiliate traffic;</li>
<li>quickly see the effect of changes in the funnel and communication.</li>
</ul>
<h3 id="conclusion">Conclusion</h3>
<p>Affiliate marketing ceases to be a collection of disparate deals and becomes an engineered growth system where decisions are made based on data. Clearly defined metrics, a detailed funnel, continuous experiments with creatives and offers, as well as working with partners as full participants in the product process, make it possible to achieve predictable results even in complex and regulated niches.</p>
<p>In an environment where traffic costs are rising and regulators are tightening requirements, exactly this approach — systematic, data-driven — gives a business the chance to scale the affiliate channel without losing manageability and profitability.</p>
<ol>
<li>Ahrefs / Authority Hacker. Affiliate Marketing Statistics 2024.<a href="https://ahrefs.com/blog/affiliate-marketing-statistics/?utm_source=chatgpt.com">Ahrefs</a></li>
<li>Wix. Affiliate marketing statistics and facts for 2025.<a href="https://www.wix.com/blog/affiliate-marketing-statistics?utm_source=chatgpt.com">wix.com</a></li>
<li><a href="https://ResearchAndMarkets.com">ResearchAndMarkets.com</a>. Online Gambling Industry Report 2025.<a href="https://www.businesswire.com/news/home/20250924101188/en/Online-Gambling-Industry-Report-2025---Global-Market-Trends-Regulation-Forecast-2024-2033---ResearchAndMarkets.com?utm_source=chatgpt.com">Business Wire</a></li>
<li>Yahoo Finance. Online Poker Global Strategic Business Report 2025–2030.<a href="https://finance.yahoo.com/news/online-poker-global-strategic-business-095300195.html?utm_source=chatgpt.com">Yahoo Финансы</a></li>
<li>Industry reviews on A/B testing and performance marketing</li>
</ol>

How Data and Experiments Turn Affiliate Marketing into a Controllable Growth System

#### Implementation Plan:

* Use AWS Systems Manager to take action on AWS resources, EC2 instances in this case.
* Create maintenance windows to schedule and automate the start and stop of EC2 instances. This will automatically perform tasks in a defined time window.
* Register the EC2 instance(s) as targets. The schedule/window applies to these targets.
* Registering tasks, these tasks will be performed on the targets.
* IAM role which SSM assumes and is allowed to make changes to the EC2 instances.

#### IAM role:

* Trust relationship

![](https://cdn-images-1.medium.com/max/1600/1*Xc6mftzJBIUEeEmRFyKD3g.png)

* Permissions
  \- Use built-in permissions policy [AmazonSSMAutomationRole](https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonSSMAutomationRole.html):

```
{    "Version": "2012-10-17",    "Statement": [        {            "Effect": "Allow",            "Action": [                "lambda:InvokeFunction"            ],            "Resource": [                "arn:aws:lambda:*:*:function:Automation*"            ]        },        {            "Effect": "Allow",            "Action": [                "ec2:CreateImage",                "ec2:CopyImage",                "ec2:DeregisterImage",                "ec2:DescribeImages",                "ec2:DeleteSnapshot",                "ec2:StartInstances",  --> Ec2 start instance allow action                "ec2:RunInstances",                "ec2:StopInstances",   --> Ec2 stop instance allow action                "ec2:TerminateInstances",                "ec2:DescribeInstanceStatus",                "ec2:CreateTags",                "ec2:DeleteTags",                "ec2:DescribeTags",                "cloudformation:CreateStack",                "cloudformation:DescribeStackEvents",                "cloudformation:DescribeStacks",                "cloudformation:UpdateStack",                "cloudformation:DeleteStack"            ],            "Resource": [                "*"            ]        },        {            "Effect": "Allow",            "Action": [                "ssm:*"            ],            "Resource": [                "*"            ]        },        {            "Effect": "Allow",            "Action": [                "sns:Publish"            ],            "Resource": [                "arn:aws:sns:*:*:Automation*"            ]        }    ]}
```

#### Systems Manager Maintenance Windows

* I used Maintenance Windows sub-feature of Change Management under Systems Manager to start and stop the EC2 instances.
* **Schedule:**

![](https://cdn-images-1.medium.com/max/1600/1*beFnpWIfS8MRcO0vTBrCBw.png)

* **Targets:**
  \- Here we have a few options. I chose to select instance manually since I only had once instance to work with. It auto populates all the running instances.

![](https://cdn-images-1.medium.com/max/1600/1*-0brAU_HfVSKS9osWGtcYw.png)

![](https://cdn-images-1.medium.com/max/1600/1*fKMOoYJZmSnA9gRe3CTWaQ.png)

\- A **resource group** can also be selected in case resources are grouped together based on a criteria.
 - The selection criteria in these set of screenshots are:
**Resource Types → AWS::EC2::Instance**
**Tags → Key:env , value:dev**

![](https://cdn-images-1.medium.com/max/1600/1*UxaehQBtSWXYj2hcta6aBQ.png)

The resources fulfilling the criteria can then be selected after selecting the Preview group resources option:

![](https://cdn-images-1.medium.com/max/1600/1*LFT6jUJ5YIHCLA3uNelRKg.png)

#### Tasks:

* Navigate to tasks:

![](https://cdn-images-1.medium.com/max/1600/1*H5b2eeHorQnagHqPMyA5XQ.png)

* Select the Automation Document:
  \- I selected EC2 Start Instance document

![](https://cdn-images-1.medium.com/max/1600/1*XunRjyIL1gO9_u_ldxifhg.png)

-Since our target type is `INSTANCE`, `{{RESOURCE_ID}}` parameter yields the instance ID only. When the maintenance window task runs, it passes the correct values instead of the pseudo parameter placeholders. More information [here](https://docs.aws.amazon.com/systems-manager/latest/userguide/maintenance-window-tasks-pseudo-parameters.html#pseudo-parameters).

![](https://cdn-images-1.medium.com/max/1600/1*BHnSnjGlkEvSZF6eDatoww.png)

#### Additional Pointers:

* Execution history of tasks can be checked under the history tab:

![](https://cdn-images-1.medium.com/max/1600/1*t1gtfUqZsVwKf9HSiPP_Lg.png)

* If for some reason, the schedule needs to be changed, click on edit in the window description tab:

![](https://cdn-images-1.medium.com/max/1600/1*Ox0phC8yrHnr_oaQJcYSQg.png)

The current schedule is set to run everyday at 9:15 AM :

**cron(0 15 9 ? \* \* \*)**

This can be changed, for example, to run at 11:30 PM everyday:

**cron(0 30 23 ? \* \* \*)**

#### Testing:

* Stop Instance maintenance window:
  \- At a set schedule, the task execution starts and the instance stops as shown in the screenshots below:

![](https://cdn-images-1.medium.com/max/1600/1*h066wHZznkk0_1N-dBj-6A.png)

![](https://cdn-images-1.medium.com/max/1600/1*1v_lytqYAVuPFOyqzKjozg.png)

* Start Instance maintenance window:
  \- At a set schedule, the task execution starts and the instance goes into a running state again as shown in the screenshots below:

![](https://cdn-images-1.medium.com/max/1600/1*NpkspcCPwP4N35K4Ki5C8g.png)

![](https://cdn-images-1.medium.com/max/1600/1*TdlZH7AYg1vDqHWtLoBNag.png)

#### For further reading:

* I will implement this feature in my upcoming blog [here](https://medium.com/p/e58612d35f83/edit).
* I will also be making sure that:
  \- Docker service on the instance keeps running after the instance is stopped and started following the schedule.
  \- Docker container keeps running and serving the web application as usual even after the instance is re-started.

Thanks for reading!

<h4 id="implementation-plan">Implementation Plan:</h4>
<ul>
<li>Use AWS Systems Manager to take action on AWS resources, EC2 instances in this case.</li>
<li>Create maintenance windows to schedule and automate the start and stop of EC2 instances. This will automatically perform tasks in a defined time window.</li>
<li>Register the EC2 instance(s) as targets. The schedule/window applies to these targets.</li>
<li>Registering tasks, these tasks will be performed on the targets.</li>
<li>IAM role which SSM assumes and is allowed to make changes to the EC2 instances.</li>
</ul>
<h4 id="iam-role">IAM role:</h4>
<ul>
<li>Trust relationship</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*Xc6mftzJBIUEeEmRFyKD3g.png" alt=""></p>
<ul>
<li>Permissions
- Use built-in permissions policy <a href="https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonSSMAutomationRole.html">AmazonSSMAutomationRole</a>:</li>
</ul>
<pre><code class="code-highlight"><span class="code-line line-number" line="1">{    "Version": "2012-10-17",    "Statement": [        {            "Effect": "Allow",            "Action": [                "lambda:InvokeFunction"            ],            "Resource": [                "arn:aws:lambda:*:*:function:Automation*"            ]        },        {            "Effect": "Allow",            "Action": [                "ec2:CreateImage",                "ec2:CopyImage",                "ec2:DeregisterImage",                "ec2:DescribeImages",                "ec2:DeleteSnapshot",                "ec2:StartInstances",  --> Ec2 start instance allow action                "ec2:RunInstances",                "ec2:StopInstances",   --> Ec2 stop instance allow action                "ec2:TerminateInstances",                "ec2:DescribeInstanceStatus",                "ec2:CreateTags",                "ec2:DeleteTags",                "ec2:DescribeTags",                "cloudformation:CreateStack",                "cloudformation:DescribeStackEvents",                "cloudformation:DescribeStacks",                "cloudformation:UpdateStack",                "cloudformation:DeleteStack"            ],            "Resource": [                "*"            ]        },        {            "Effect": "Allow",            "Action": [                "ssm:*"            ],            "Resource": [                "*"            ]        },        {            "Effect": "Allow",            "Action": [                "sns:Publish"            ],            "Resource": [                "arn:aws:sns:*:*:Automation*"            ]        }    ]}
</span></code></pre>
<h4 id="systems-manager-maintenance-windows">Systems Manager Maintenance Windows</h4>
<ul>
<li>I used Maintenance Windows sub-feature of Change Management under Systems Manager to start and stop the EC2 instances.</li>
<li><strong>Schedule:</strong></li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*beFnpWIfS8MRcO0vTBrCBw.png" alt=""></p>
<ul>
<li><strong>Targets:</strong>
- Here we have a few options. I chose to select instance manually since I only had once instance to work with. It auto populates all the running instances.</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*-0brAU_HfVSKS9osWGtcYw.png" alt=""></p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*fKMOoYJZmSnA9gRe3CTWaQ.png" alt=""></p>
<p>- A <strong>resource group</strong> can also be selected in case resources are grouped together based on a criteria.
 - The selection criteria in these set of screenshots are:
<strong>Resource Types → AWS::EC2::Instance</strong>
<strong>Tags → Key:env , value:dev</strong></p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*UxaehQBtSWXYj2hcta6aBQ.png" alt=""></p>
<p>The resources fulfilling the criteria can then be selected after selecting the Preview group resources option:</p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*LFT6jUJ5YIHCLA3uNelRKg.png" alt=""></p>
<h4 id="tasks">Tasks:</h4>
<ul>
<li>Navigate to tasks:</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*H5b2eeHorQnagHqPMyA5XQ.png" alt=""></p>
<ul>
<li>Select the Automation Document:
- I selected EC2 Start Instance document</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*XunRjyIL1gO9_u_ldxifhg.png" alt=""></p>
<p>-Since our target type is <code>INSTANCE</code>, <code>{{RESOURCE_ID}}</code> parameter yields the instance ID only. When the maintenance window task runs, it passes the correct values instead of the pseudo parameter placeholders. More information <a href="https://docs.aws.amazon.com/systems-manager/latest/userguide/maintenance-window-tasks-pseudo-parameters.html#pseudo-parameters">here</a>.</p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*BHnSnjGlkEvSZF6eDatoww.png" alt=""></p>
<h4 id="additional-pointers">Additional Pointers:</h4>
<ul>
<li>Execution history of tasks can be checked under the history tab:</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*t1gtfUqZsVwKf9HSiPP_Lg.png" alt=""></p>
<ul>
<li>If for some reason, the schedule needs to be changed, click on edit in the window description tab:</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*Ox0phC8yrHnr_oaQJcYSQg.png" alt=""></p>
<p>The current schedule is set to run everyday at 9:15 AM :</p>
<p><strong>cron(0 15 9 ? * * *)</strong></p>
<p>This can be changed, for example, to run at 11:30 PM everyday:</p>
<p><strong>cron(0 30 23 ? * * *)</strong></p>
<h4 id="testing">Testing:</h4>
<ul>
<li>Stop Instance maintenance window:
- At a set schedule, the task execution starts and the instance stops as shown in the screenshots below:</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*h066wHZznkk0_1N-dBj-6A.png" alt=""></p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*1v_lytqYAVuPFOyqzKjozg.png" alt=""></p>
<ul>
<li>Start Instance maintenance window:
- At a set schedule, the task execution starts and the instance goes into a running state again as shown in the screenshots below:</li>
</ul>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*NpkspcCPwP4N35K4Ki5C8g.png" alt=""></p>
<p><img src="https://cdn-images-1.medium.com/max/1600/1*TdlZH7AYg1vDqHWtLoBNag.png" alt=""></p>
<h4 id="for-furtherreading">For further reading:</h4>
<ul>
<li>I will implement this feature in my upcoming blog <a href="https://medium.com/p/e58612d35f83/edit">here</a>.</li>
<li>I will also be making sure that:
- Docker service on the instance keeps running after the instance is stopped and started following the schedule.
- Docker container keeps running and serving the web application as usual even after the instance is re-started.</li>
</ul>
<p>Thanks for reading!</p>

Automating the Start and Stop state of EC2 instance

Building a SaaS MVP with Python in Under a Year

The Solo AI Founder

### **Introduction**

Prompt engineering — the craft of designing inputs for AI language models — has a huge impact on the quality and consistency of the model’s output. Traditionally, prompts are written in plain natural language. However, a new approach called **JSON prompting** has been gaining traction among developers and AI enthusiasts . Instead of writing a request as free-form text, the idea is to structure the prompt as a JSON object with explicit fields. Proponents claim this technique yields more precise and reliable responses, especially for technical or structured tasks, while skeptics caution that it’s not a magic bullet for every situation . In this article, we’ll explore what regular (plain text) prompts are versus JSON-formatted prompts, how they differ, and how to use JSON prompts effectively — complete with examples, benefits, and limitations.

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***

### What is a Regular Prompt?

A **regular prompt** (or plain-text prompt) is simply a query or instruction given to an AI model in natural language. For example, a user might ask: *“Summarize the customer feedback about shipping delays.”* This kind of prompt relies on the model’s ability to interpret human language and figure out the task from context. Regular prompts are easy for humans to write, but they can sometimes be *ambiguous or inconsistent*. The model might misunderstand the request or produce outputs in an unpredictable format if the prompt isn’t specific enough. In other words, traditional prompting often feels like conversing with a “brilliant but inconsistent colleague” — you may get insightful answers, but their format and focus can vary . Developers using plain prompts sometimes struggle when they need the AI’s output to be strictly structured or directly parsed by software, since natural language responses can be verbose or inconsistent.

### What is a JSON Prompt?

A **JSON prompt** is a prompt written as a JSON (JavaScript Object Notation) object, using keys and values to explicitly define the request. Instead of a free-form sentence, you “fill out” a structured template — much like entering parameters into a form . In a JSON prompt, each aspect of the task is a field. For instance, the same request above in JSON format could look like:

```
{  "task": "summarize",  "topic": "customer feedback",  "focus": "shipping delays"}
```

This format is **machine-readable and unambiguous**. Each key tells the model what kind of information or constraint you’re providing — e.g. “task” specifies the action, “topic” provides the subject matter, and “focus” might narrow down the aspect to emphasize . The model still ultimately sees a textual input (the JSON is text), but because the input is highly structured, the model can recognize the pattern and intent more easily. As one source puts it, *“Not English. Not vibes. Just clear instructions.”* — JSON prompts remove the guesswork by providing a rigid template of the user’s intent.

### Example: Plain vs. JSON Prompt

To make the difference concrete, consider a simple task. A regular prompt might say:

*“Can you write a tweet about dopamine detox?”*

In contrast, a JSON-formatted prompt for the same request could be:

```
{  "task": "write a tweet",  "topic": "dopamine detox",  "style": "viral",  "length": "under 280 characters"}
```

In the plain prompt, the model has to parse the sentence and may or may not infer the desired style or length. In the JSON prompt, we explicitly specified the style (“viral” tone) and length constraint. The JSON version is **clear, modular, and explicitly structured**, leaving less room for interpretation . You can already see how the JSON prompt acts like a mini-specification: the model is being instructed exactly what to do (write a tweet) and under what conditions (on the topic of dopamine detox, in a viral style, within 280 characters).

***

### Why Use JSON Prompts? (Benefits)

Using JSON to structure a prompt offers several **advantages** for certain applications:

* **Clarity and Reduced Ambiguity:** JSON prompts significantly reduce ambiguity in instructions. By breaking down the request into discrete fields, you eliminate the vagueness that natural language can have . The model doesn’t have to “guess” your intent or the format of the answer — you’re telling it exactly what you want in a structured way. This often leads to more *precise and on-point outputs*.
* **Consistency and Format Compliance:** When you need the AI’s output in a specific format or to include certain elements, JSON prompting helps enforce that. The structured input serves like a *contract* for the output structure . In fact, many have reported that responses become far more consistent with the desired format. One informal report noted that using JSON formatting made the responses **80–90% more accurate and format-compliant** (e.g. following instructions like “give me 5 bullet points” exactly) .
* **Leverages Model Training Biases:** Large language models have been trained on not just natural language, but also on *lots of structured data*: code, JSON, YAML, XML, configuration files, etc. This means they have seen patterns of keys, values, and structured syntax during training . When you present a prompt in JSON, the model likely recognizes it as a familiar pattern (similar to data or code) and treats it as a high-signal input. In other words, *“Models like GPT learn from code, documentation, APIs, and organized data. JSON looks similar to what they were trained with, so they treat it as higher-signal”* . The model doesn’t have to work as hard to parse your intent from a narrative sentence; instead, the structure directly maps to what it should do. This often *reduces the chance of misunderstanding* and can even reduce off-topic or “hallucinated” content, since the model is being funneled into a clear pattern .
* **Easier Post-Processing and Integration:** For developers, having the AI output follow a structure is gold. While the prompt format and output format are not the same thing, they’re related — if you prompt in JSON form, you’re implicitly guiding the model to output information in a structured manner too. Many find that JSON prompts make it easier to parse the results (sometimes you can even instruct the model to answer in JSON). In production systems, this is invaluable: *“JSON prompting ensures that LLMs behave like dependable APIs”* , meaning you can reliably extract fields from the response. This reduces the need for complex string parsing or regex on AI output. In fact, recognizing this trend, AI providers have started offering **native support for structured outputs**.
* **Reusability and Modular Prompt Design:** JSON prompts encourage you to separate *content* from *instructions*. This modular design means you can reuse prompt templates. For example, you might have a generic prompt like *“Write a scientific summary using the provided context”* and then feed different JSON blocks as the context (containing the study details, audience, tone, etc.) . By doing so, you create a flexible “prompting system” where the JSON defines the variables and the actual prompt can remain constant. This is extremely useful in enterprise settings — you can plug in new data (different product info, different user profiles, etc.) into a JSON template without rewriting the entire prompt each time .

***

### How to Write a JSON Prompt

Using JSON in a prompt might feel unusual at first, but it essentially involves translating a request into a structured **key-value specification**. Here are some guidelines and best practices for writing effective JSON prompts :

1. **Identify the Key Parameters:** Break down your task into elements like *action, subject, style, format, constraints, etc.* Each of these becomes a key in the JSON. Choose clear, descriptive key names. For example, use “task” for the action you want (e.g., “summarize”, “translate”), “topic” or “input” for the content to act on, “tone” for the style of writing, “length” for any length or format requirements, and so on. If the task involves an output format or specific fields, you can include a key like “output\_format” or even a nested schema. The idea is to **be explicit** about everything. A regular prompt that says *“Write a summary of this article for college students in 100 words”* can be turned into JSON as:

```
{  "task": "summarize article",  "audience": "college students",  "length": "100 words",  "tone": "curious"}
```

2\. **Use Nested Objects for Structure (if needed):** JSON allows hierarchy. If your prompt has subcomponents or if the content can be grouped logically, use nested JSON objects. This can be useful for complex prompts. For example, suppose you want an AI to generate a Twitter thread with a specific structure: a hook, a body, and a call-to-action. You might nest those requirements under a "structure" key, like:

```
{  "task": "write a thread",  "platform": "twitter",  "structure": {    "hook": "strong, short, curiosity-driven",    "body": "3 core insights with examples",    "cta": "ask a question to spark replies"  },  "topic": "founder productivity systems"}
```

This nested JSON clearly organizes the prompt’s requirements . The model can see that under “structure”, it has specific instructions for different parts of the output. Nesting can help when there are categories of instructions or when you want to include example data separately from the main instruction.

3\. **Keep it Valid and Lean:** Make sure your JSON syntax is correct (proper quotes, braces, etc.), because a malformed JSON might confuse the model (and won’t be parseable for you either!). Also, while adding structure, avoid unnecessary complexity. Use only as many fields as needed to convey your requirements. The goal is clarity, so don’t go overboard with deeply nested structures that might overwhelm the model. For instance, five to ten well-chosen fields might describe most tasks. If you have a very complex instruction set, consider whether it can be broken into multiple steps or prompts. **Use comments or descriptions sparingly**, if at all – remember the model sees everything, and extra fluff in the prompt can introduce ambiguity. Stick to the "key": "value" pairs of actual importance.

```
{  "task": "recommend books",  "topic": "thinking clearly",  "audience": "entrepreneurs",  "output_format": "list of 5 with one-sentence summaries"}
```

This structured prompt explicitly asks for a list of 5 book recommendations on the topic of “thinking clearly” for an audience of entrepreneurs, and even specifies that each should have a one-sentence summary . By running both versions (the plain request vs the JSON), users have found that *“the second one is sharper, easier to understand, and more on point.”* The model, when given the JSON, is more likely to output exactly five bullet points, each with a concise description, because the prompt itself laid out that expectation clearly.

**Tip:** You can actually include the input text (if any) within the JSON as well. For instance, if the task is to improve some given writing, you can do: {“task”: “improve writing”, “input”: “\<\<\<text>>>”, “criteria”: “make it concise and clear”}. This way, the model sees both the instruction and the text to act on in one structured blob. Many prompt engineers use delimiters or special markers even within JSON to clearly denote where the content is, but the key idea is to keep everything organized.

***

### Limitations and When Not to Use JSON Prompts

While JSON-style prompting can be powerful, it’s **not a silver bullet** for all situations. There are important caveats and scenarios where a regular prompt may be more appropriate:

* **Creative or Free-Form Tasks:** If you ask the model for highly creative output (a poem, a story, an expressive essay) but wrap the prompt in a rigid JSON, you might stifle the model’s creativity. JSON syntax can put the model into a more literal, code-like frame of mind. In fact, the founder of PromptLayer noted an example: *“Asking for ‘Write a love note and return it in JSON with key note’ produces worse results than simply ‘Write a love note.’”* The model essentially “switched into technical mode” upon seeing the JSON structure, which hurt the creative quality of the output. This phenomenon is sometimes called a **context switching penalty** — the model’s style and thinking shift when you force a different format.
* **Unnecessary Complexity and Token Overhead:** JSON prompts, by nature, include extra characters (braces, quotes, keys) that a plain prompt wouldn’t have. This overhead can be non-trivial. Each character and token in the prompt counts toward the model’s context limit and can slightly increase processing time and cost. An OpenAI researcher pointed out that JSON can introduce a lot of “noise” for the model: *whitespace and punctuation consume tokens, special characters have to be escaped, and the model has to keep track of all those brackets* . In some cases, you might achieve the same clarity by just writing a well-formatted plain prompt or using Markdown lists, without the extra JSON syntax. Indeed, others have found that **Markdown or other lightweight structure often performs just as well or better** for certain tasks, since it’s less verbose and something models handle naturally too . The key is to avoid structuring the prompt in a more complicated way than the task actually requires.
* **Pushing the Model into the Wrong Distribution:** Large language models have different “modes” of behavior. When you wrap everything in JSON, you implicitly signal the model to treat this like data or code. That’s great for getting a structured response, but not great if you want a conversational or narrative style output. Over-structuring can constrain **nuanced reasoning or stylistic richness**. As one analysis put it, forcing JSON output can make the model pattern-match to technical documentation styles rather than natural human communication . So if the goal is a friendly chat or an open-ended brainstorm, a JSON prompt might make the responses too terse or formulaic.
* **Not Always Statistically Better:** While anecdotal reports and some studies praise JSON prompting, it’s not universally superior in all metrics. A research study in 2025 compared JSON prompting with other prompt styles (like YAML and a specialized CSV-based format) across tasks like parsing receipts and medical records. Interestingly, a highly compact **Hybrid CSV/Prefix** format slightly outperformed JSON in accuracy for those tasks (82% vs 74% accuracy on average), and was more efficient in terms of tokens and time . The differences weren’t always statistically significant, but the point is that *JSON wasn’t the absolute winner in every case*.

***

### Conclusion & Summary

Regular prompts and JSON prompts are two different approaches to communicating with AI models, each with its own strengths. A regular prompt is written in natural language — it’s intuitive and flexible, but can be prone to ambiguity. A JSON prompt, on the other hand, turns your request into a structured specification — it’s explicit and precise, helping the AI to understand exactly what you want in a more deterministic way.

The rise of JSON prompting reflects a broader trend: as AI systems are increasingly used in **production and automation**, we require their outputs to be predictable and easy to integrate. JSON prompts bring a taste of **software engineering discipline** to AI querying, making the model’s behavior more like an API that returns structured data . This can dramatically cut down on errors, misinterpretations, and the time spent cleaning up AI output. In technical workflows (for instance, multi-step data processing or feeding AI outputs into databases), this reliability often “beats brilliance” — a slightly less eloquent but structured answer is usually more useful than a creative ramble that a program can’t parse .

However, JSON prompting is not about magically improving the model’s intelligence or creativity — it’s about improving **communication and consistency**. Critics rightly point out that it doesn’t make the core model *better*; it just makes the instructions clearer, which in turn can make results more consistently aligned with what you asked for . The real power comes when you as a developer or researcher harness the technique appropriately: know when to use it and when not to. As with any tool in AI, experimentation is key. Try formatting some of your prompts as JSON and compare the outputs to plain prompts. As one guide suggested, build small evaluations to see if JSON prompts help or hurt for your particular use case . Over time, you’ll develop an intuition for which parts of a task benefit from structure and which thrive with a bit of freedom.

In closing, JSON prompting is an exciting development in prompt engineering that bridges the gap between natural language flexibility and structured data precision. It offers a way to speak to AI models in a language they can follow with little ambiguity. Use it to **command** the AI when you need strict adherence and repeatability , but don’t be afraid to stick with regular prompts for those imaginative, open-ended conversations where a little unpredictability can be a good thing. By understanding the difference between regular and JSON prompts and wielding each in the right context, you can get the most out of AI models — whether you’re generating creative content or building robust AI-powered systems.

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