Decisioning studio docs update - adding PRo docs

_docs/_user_guide/brazeai/decisioning_studio/decisioning_studio_pro.md

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+---
+page_order: 4
+nav_title: Decisioning Studio Pro
+article_title: Setting up Decisioning Studio Pro agents
+description: "Learn how to set up Decisioning Studio Pro agents to make 1:1 AI decisions that maximize your business metrics."
+---
+
+# Setting up Decisioning Studio Pro agents
+
+> Think of a Decisioning Studio agent like the brain of your Martech stack. In order to make optimal and personalized decisions at a 1:1 level to maximize a target metric, it needs customer context, a bank of possible actions to select, and the ability to orchestrate marketing communications.
+
+AI Decisioning Services (Braze's forward-deployed engineering team) will configure the agent itself and most integration points, and guide your team on required integration and setup steps on your side.
+
+The first step is to design your agents with assistance from our expert services. This will include mapping out all of the available data, determining an overall orchestration pattern, and setting up the agents for maximal success for your business, including configuring target metrics and appropriate constraints.
+
+To learn more about what agents can do, [book a call](https://www.braze.com/get-started/) with Braze.
+
+## Best practices
+
+A few best practices for setting up Decisioning Studio agents:
+
+1. The more information agents have about your customers, the better they will perform
+2. The fewer constraints on your agents, the better. Constraints should be designed to respect business rules while freeing agent-led experimentation as much as possible.
+
+## Setup steps
+
+Once the agent has been designed, the following additional steps need to be taken to set up the self-learning cycle:
+
+1. Configure customer context for the agent, including:
+   - Define the audience
+   - Feed customer data to the agent
+2. Set up Orchestration
+3. Create the Feedback loop
+
+{% alert important %}
+This guide explains the most common integration patterns. Information Security will still need to vet all connection points.
+{% endalert %}
+
+## Next steps
+
+1. [Design your agent]({{site.baseurl}}/user_guide/brazeai/decisioning_studio/decisioning_studio_pro/design_your_agent/)
+2. [Set Customer Context]({{site.baseurl}}/user_guide/brazeai/decisioning_studio/decisioning_studio_pro/set_customer_context/)
+3. [Configure Orchestration]({{site.baseurl}}/user_guide/brazeai/decisioning_studio/decisioning_studio_pro/configure_orchestration/)
+4. [Create the Feedback Loop]({{site.baseurl}}/user_guide/brazeai/decisioning_studio/decisioning_studio_pro/create_feedback_loop/)
+

_docs/_user_guide/brazeai/decisioning_studio/decisioning_studio_pro/configure_orchestration.md

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+---
+page_order: 4.3
+nav_title: Configure Orchestration
+article_title: Configure Orchestration
+description: "Learn how to configure orchestration for Decisioning Studio Pro agents to enable personalized communications."
+---
+
+# Configure Orchestration
+
+> Decisioning agents need some means to orchestrate communications once they have ingested customer data and personalized at a 1:1 level.
+
+Decisioning agents need some means to orchestrate communications once they have ingested customer data and personalized at a 1:1 level.
+
+While Decisioning Studio integrates most smoothly with Braze's orchestration capabilities, Decisioning Studio also supports native (code-free) integrations Salesforce Marketing Cloud (SFMC) and Klaviyo. Custom integrations can also be configured with any other customer engagement platform (CEP).
+
+{% alert note %}
+The Decisioning Studio team is currently building even more seamless integrations between Decisioning Studio and Braze orchestration. This documentation will be updated as these steps are simplified.
+{% endalert %}
+
+## If Customer Engagement Platform is Braze (Best case scenario)
+
+Follow these steps to integrate a Braze Decisioning Studio agent with Braze's orchestration capabilities (and Braze's services team will be able to help):
+
+### Step 1: Create an API key
+
+Go to **Settings** > **API Keys**, then create a new key with the following permissions:
+
+{% multi_lang_include decisioning_studio/api_key_permissions.md %}
+
+### Step 2: Set up API-triggered campaigns
+
+Set up an API-triggered campaign for each base template with API trigger properties for all optimized dimensions.
+
+A base template is any template that the Decisioning Agent might use for orchestrating messages. A Decisioning Agent might have 1 base template and multiple, in which case choosing the right base template for each customer will be one of the decisions the agent personalizes.
+
+### Step 3: Configure re-eligibility
+
+Ensure all API Triggered Campaigns allow users to become re-eligible within **15 minutes**.
+
+{% alert note %}
+While the Decisioning Studio agent will never send the same campaign more than once a day, you will want to have the ability to send the same campaigns multiple times in a day for testing purposes.
+{% endalert %}
+
+### Step 4: Add dynamic placeholders
+
+These will serve as dynamic placeholders for decisions that the Decisioning Studio agent is optimizing.
+
+Here are some examples:
+
+#### Example #1: Email Campaign
+
+Suppose the Decisioning Studio agent is optimizing an email campaign. Supposing the agent is optimizing for choice of templates and Call to Action (CTA) message, then an API-triggered campaign should be created for each template, and the CTA section of one template might look like:
+
+{% raw %}
+```
+{{${cta_message}}}
+```
+{% endraw %}
+
+#### Example #2: Push campaign
+
+Suppose a Decisioning Studio agent is optimizing the message of a Push campaign. This might be configured like this:
+
+{% raw %}
+```
+{{${push_message}}}
+```
+{% endraw %}
+
+#### Example #3: SMS Campaign
+
+Suppose that the Decisioning Studio agent is optimizing for fields in an SMS campaign. This might be configured like this:
+
+{% raw %}
+```
+{{${sms_message}}}
+```
+{% endraw %}
+
+## If Customer Engagement Platform is SFMC or Klaviyo
+
+Decisioning Studio also supports native integrations with SFMC and Klaviyo.
+
+For SFMC, for example, Decisioning Studio triggers API events into a journey with data required to populate dynamic elements.
+
+For detailed steps to configure an agent that uses either of these customer engagement platforms, contact the services team.
+
+## If another Customer Engagement Platform
+
+Decisioning Studio can integrate with any customer engagement platform. However, this may require some custom engineering work, if Decisioning Studio cannot trigger communications directly.
+
+In this scenario, the agent delivers a "recommendation file." This file contains rows for each customer, with columns that indicate all of the personalized decisions for that customer.
+
+For example, the following recommendation file might be used by a customer to optimize an email campaign:
+
+| Customer ID | Template | Subject Line | Send Time |
+|-------------|----------|--------------|-----------|
+| user_123    | Template A | Welcome! | 10:00 AM |
+| user_456    | Template B | Get Started | 2:00 PM |
+
+For more information on custom integrations, contact the AI Decisioning Services team.
+

_docs/_user_guide/brazeai/decisioning_studio/decisioning_studio_pro/create_feedback_loop.md

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+---
+page_order: 4.4
+nav_title: Create the Feedback Loop
+article_title: Create the Feedback Loop
+description: "Learn how to create the feedback loop for Decisioning Studio Pro agents to enable self-learning."
+---
+
+# Create the Feedback Loop
+
+> Finally, while all customer data is important for the agent, the *most important data assets* are those that tell the agent what happened after customer engagement decisions were sent.
+
+Finally, while all customer data is important for the agent (and for information on how to configure, refer back to [Set Customer Context]({{site.baseurl}}/user_guide/brazeai/decisioning_studio_pro/set_customer_context/)), the *most important data assets* are those that tell the agent what happened after customer engagement decisions were sent.
+
+These assets create the feedback loop that allows the agent to learn.
+
+There are three critical assets for creating the feedback loop: conversions (including any relevant financial data, such as revenue); engagement data; and orchestration data. There are special requirements for each of these data assets.
+
+{% alert note %}
+Finally, note that while information on all three categories—conversions, engagement, and activations—is required for an agent to be successful, if the agent is natively integrated with the customer engagement platform, such as Braze, there may not be additional configuration steps necessary, since these may be sent with the customer data.
+{% endalert %}
+
+## Conversions Data
+
+The conversion asset describes what happened to the customer after orchestration. For example, supposing an agent is optimizing on Net Present Value (NPV) for customers receiving optimized campaigns, the conversion asset might include a daily update of changes to NPV.
+
+The conversion asset must meet the following requirements:
+
+| Requirements | Why? |
+|-------------|------|
+| Each record contains a unique customer identifier that is consistent with all data assets | Decisioning Studio needs to be able track the individual customer journey from recommendation, through activation, to conversion. |
+| Each record has an associated timestamp. | Understanding the time between communication and sequence of customer actions is extremely important for model training and metric calculation. |
+| If using a non-binary (e.g., converted vs. unconverted) target metric, the target metric value associated with each conversion is provided with each conversion event. | Decisioning Studio uses the target metric value to generate training experiences to appropriately reward/penalize the model based on the outcomes of the recommended actions. |
+| If conversions can be uniquely attributed to communications (e.g., coupon redemption), fields needed to match conversions to activations are provided. | If a conversion event can be tied to a particular communication, this allows for clean and precise attribution. Direct attribution provides the clearest signal to the model, but if is not possible, as if often the case, proximity-based attribution will be used. |
+{: .reset-td-br-1 .reset-td-br-2 role="presentation"}
+
+## Engagement Data
+
+The engagement asset describes what happened to the customer, including clicks, opens, and other impressions. Engagement data may be included in the conversion data or it may be separate. It plays a similar role as conversions data—telling the agent what happened after customer engagement.
+
+The engagement asset must meet similar requirements to the conversion asset:
+
+| Requirements | Why? |
+|-------------|------|
+| Each record contains a unique customer identifier that is consistent with all data assets | Decisioning Studio needs to be able track engagement events for each individual customer. |
+| Each record has an associated timestamp. | Understanding the time between communication and sequence of customer actions is extremely important for model training and metric calculation. |
+| If clicks, opens or other engagement data can be uniquely attributed to communications (e.g., coupon redemption), fields needed to match conversions to activations are provided. | As with conversion data, if engagement can be tied to a particular communication, this allows for clean and precise attribution. Direct attribution provides the clearest signal to the model. |
+{: .reset-td-br-1 .reset-td-br-2 role="presentation"}
+
+## Activations Asset
+
+The activations asset tells the agent which communications were sent. This is often necessary depending on how orchestration is configured. If the agent orchestrates via a direct integration with Braze, SFMC, or Klaviyo, then the agent may be able to pull activation data directly.
+
+The activation asset must meet the following requirements:
+
+| Requirements | Why? |
+|-------------|------|
+| Each record contains a unique customer identifier that is consistent with all data assets | Decisioning Studio needs to be able track the individual customer journey from recommendation, through activation, to conversion. |
+| Each record has an associated timestamp. | Understanding the time between communication and sequence of customer actions is extremely important for model training and metric calculation. |
+| Fields needed to match communication content to activation events are provided (e.g., event_id). | Correctly matching communication characteristics to sends is necessary for model attribution and training. |
+{: .reset-td-br-1 .reset-td-br-2 role="presentation"}
+

_docs/_user_guide/brazeai/decisioning_studio/decisioning_studio_pro/design_your_agent.md

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+---
+page_order: 4.1
+nav_title: Design your agent
+article_title: Design your agent
+description: "Learn how to design your Decisioning Studio Pro agent with the AI Decisioning Services team."
+---
+
+# Design your agent
+
+> During the Sales process and initial agent configuration, the first step is working with our AI Decisioning Services team to design your agent.
+
+During the Sales process and initial agent configuration, the first step is working with our AI Decisioning Services team to design your agent.
+
+This includes making the following decisions:
+
+1. **Success metric:** What will the agent maximize when personalizing customer engagement? (e.g., revenue, LTM)
+2. **Audience:** For whom will the Decisioning Studio agent make customer engagement decisions?
+3. **Experiment groups:** How should Decisioning Studios' randomized controlled trials be structured?
+4. **Dimensions:** What decisions should the agent personalize? These could include time of day, subject line, frequency, offers, and more (the possibilities are vast!)
+5. **Options:** What options does the agent have to work with? For example, what email templates or offers can it send, and to whom?
+6. **Constraints:** What decisions should the agent *never* make?
+
+Each of these decisions has implications for how much incremental uplift the agent may be able to generate, and how quickly. Our AI Decisioning Services team will work with you to design an agent that generates maximum value while respecting all of your business rules.
+
+Once these decisions are made, and we have an architecture diagram for our integration, we can proceed to configuration.
+