In the dynamic space of digital marketing, Amazon Marketing Cloud (AMC) emerges as a beacon of innovation and precision. At its core, AMC is a robust, cloud-based data clean room, ingeniously designed to safeguard privacy while unlocking a treasure trove of insights. This platform is a game-changer for advertisers and agencies, offering a secure and compliant way to merge and analyze vast swathes of pseudonymized data.

Historically, AMC has been the secret weapon for marketers seeking to decipher the intricate tapestry of consumer interactions across Amazon’s sprawling ecosystem. From the bustling digital aisles of Sponsored Ads to the immersive world of Amazon DSP, AMC has enabled the crafting of aggregated reports that illuminate the typical shopping journey with stunning clarity. These reports aren’t just numbers and graphs; they’re the narratives of customer engagement, the hidden stories behind every click and scroll.

But AMC’s prowess doesn’t stop there. Its ability to generate custom reports is akin to giving marketers a high-powered microscope to zoom in on specific aspects of their campaigns. Whether fine-tuning keywords for Sponsored Ads or calibrating audience settings for Amazon DSP, AMC offers a level of customization that transforms raw data into strategic gold. In the hands of a skilled marketer, AMC becomes more than a tool; it’s a compass guiding towards uncharted territories of opportunity and growth in the ever-changing Amazon landscape.

New Feature: AMC Rule-Based Audiences

The Amazon Marketing Cloud (AMC) has recently introduced a groundbreaking feature that significantly enhances audience targeting capabilities: the AMC Rule-Based Audiences. This new functionality revolutionizes how advertisers create and refine their audience segments, leveraging the power of SQL-based logic.

At its essence, AMC’s rule-based audience feature allows for the creation of highly tailored audience segments by applying intricate SQL-based criteria to the diverse range of signals available within AMC. This includes data from various Amazon Ads events, such as traffic from Amazon DSP and Sponsored Ads, as well as relevant conversion events and first-party tables. The utilization of SQL (Structured Query Language) for this purpose empowers advertisers to craft audience segments with unprecedented precision, aligning closely with specific campaign objectives and marketing strategies.

In addition to this, AMC has expanded its capabilities with the introduction of lookalike functionality. This feature enables brands to extend their reach by identifying and targeting new potential customers who exhibit similar behaviours or characteristics to their existing customer base. The lookalike functionality is particularly valuable for brands seeking to expand their audience while maintaining relevancy and a high potential for engagement.

Together, these advancements in AMC’s rule-based audiences and lookalike functionality offer advertisers a more refined, data-driven approach to audience segmentation, opening up new possibilities for targeted advertising campaigns on Amazon’s platforms.

Audience Creation and Customization

The introduction of AMC’s rule-based audience feature marks a significant leap forward in audience creation and customization. This advanced capability hinges on the strategic use of rule-based logic, allowing advertisers to craft highly specific audience segments that align precisely with their marketing objectives.

The process begins with the selection of various signals within AMC, such as interactions with Sponsored Ads or Amazon DSP traffic events. Advertisers then apply SQL-based logic to these signals, creating rules that define their target audience. For example, one might create a segment targeting users who have engaged with specific ads but haven’t yet made a purchase, or perhaps those who have shown loyalty by repeatedly buying certain products. The beauty of this system lies in its granularity; advertisers can fine-tune their audiences with remarkable specificity, tailoring their campaigns to resonate with precisely the right people at the right time.

A particularly innovative aspect of AMC’s audience creation is the availability of Amazon-authored SQL templates, also known as Instructional Queries (IQs). These templates serve as starting points for advertisers, offering pre-defined query structures that can be customized as needed. Whether it’s tweaking the criteria for a repeat purchase audience or altering the parameters for a cart abandoner segment, these IQs significantly streamline the audience creation process. They not only save time but also provide guidance to advertisers who may be less familiar with SQL, ensuring that even those with limited technical expertise can leverage AMC’s powerful capabilities to their fullest extent.

This blend of SQL-based rule logic and customizable templates represents a potent tool in the advertiser’s arsenal, offering an unprecedented level of control over audience segmentation and enabling more targeted, effective, and efficient advertising campaigns.

Walkthrough of the AMC user interface.

The Amazon Marketing Cloud (AMC) interface has been thoughtfully designed to offer a seamless and intuitive user experience, especially with the introduction of its new audience creation features. Here’s a walkthrough of the AMC interface, focusing on the audience panel and the audience creation process:

1. Navigating the AMC Interface:
   • Upon logging into AMC, users are greeted with a dashboard that provides a holistic view of their data and analytics. The interface is clean and user-friendly, with easy navigation to various functionalities.
2. The Audience Panel:
   • A key addition to the AMC UI is the audience panel. This panel serves as the command center for all audience-related activities. Here, users can view and manage their created audiences, monitor the status of each audience, and access the tools for audience creation.
3. Creating Audiences:
   • In the audience panel, there is a clear option to ‘Create Audience.’ Clicking this presents two primary methods: using an Instructional Query (IQ) or creating a custom query from scratch.
4. Using Instructional Queries:
   • For those seeking guidance or a quicker setup, Instructional Queries are a valuable resource. These are pre-built SQL templates provided by Amazon, covering common audience scenarios. Users can select an IQ, which then guides them through the process of tailoring it to their specific needs. This option is particularly beneficial for those new to SQL or who wish to save time in audience creation.
5. Building Custom Queries:
   • For more experienced users or those with specific requirements, creating a custom query offers complete flexibility. This option allows users to write their own SQL queries from scratch, leveraging the full range of data and signals available in AMC. This is where the true power of AMC’s customization capabilities comes to the fore, enabling the creation of highly specific and targeted audience segments.
6. Other UI Features:
   • Beyond audience creation, the AMC interface also includes features for running and reviewing reports, accessing documentation and release notes, and exploring additional paid features for enhanced reporting and audience insights.

Overall, the AMC interface is designed to cater to a wide range of user needs, from those requiring a guided approach to audience creation to experts who seek the freedom to craft highly customized queries. The audience panel, in particular, stands out as a pivotal tool, centralizing all aspects of audience management and creation in one convenient location.

Instructional Query Templates

Instructional Query Templates (IQs) in Amazon Marketing Cloud (AMC) are a game-changer for advertisers looking to streamline their audience creation process. These templates serve as pre-built, customizable SQL queries, providing a robust starting point for various audience-targeting scenarios. Here’s a closer look at how they work and the customization options they offer:

1. Functionality of Instructional Query Templates:
   • IQs are essentially pre-authored SQL templates provided by Amazon within AMC. They cover a range of common audience targeting scenarios based on typical marketing needs.
   • These templates are designed to be both instructional and practical, offering users guidance on how to structure SQL queries while simultaneously serving as a ready-to-use solution.
2. Customization Options:
   • While IQs provide a foundation, they are highly customizable. Advertisers can tweak the templates to better align with their specific campaign goals.
   • Customization can range from simple modifications, like changing date ranges or targeting specific product categories, to more complex alterations involving the addition of new data points or altering the logic of the query.
3. Example of Modifying a Template for Specific Audience Targeting:
   • Consider a template designed for targeting users who have viewed a product but haven’t made a purchase. The basic structure of this query would identify users based on their interaction with a product detail page.
   • An advertiser can modify this template to target a specific product or a range of products. For instance, if the campaign is for a new product line, the query can be altered to focus on users who have viewed any product within that line but haven’t purchased it.
   • Further customization might involve adjusting the look-back period. Instead of targeting users who viewed a product in the last 30 days, the advertiser might narrow this to a more recent timeframe, such as the last 7 days, to capture a more immediate interest.

In summary, Instructional Query Templates in AMC are a powerful tool for advertisers, offering a balance between ease of use and the flexibility for customization. Whether making minor adjustments or overhauling the entire query, these templates provide a solid foundation for creating targeted, effective audience segments.

Building Specific Audience Types

Creating a specific audience type, such as “cart abandoners,” in Amazon Marketing Cloud (AMC) involves a nuanced understanding of shopper behaviour and the flexibility to tailor audience parameters to meet specific campaign goals. Here’s a step-by-step guide to building a “cart abandoners” audience, along with insights into the flexibility of audience parameters and the importance of understanding shopper behaviour:

Selecting the Base Template or Creating a Custom Query:

• Start by choosing an Instructional Query (IQ) template that closely aligns with the cart abandoner audience, or opt to create a custom SQL query from scratch.
• If using an IQ, look for a template that identifies users who have added items to their cart but have not completed the purchase.

Customizing the Query:

Modify the selected template or write your custom SQL query. Focus on key parameters such as the time frame (e.g., users who abandoned their cart in the last 7 days), specific products or categories, and other relevant criteria.

For example, you might want to target users who added a specific product (identified by its ASIN) to their cart but didn’t proceed to checkout within a certain period.

Defining the Look-Back Period:

Decide on the look-back period for your audience. A shorter period, like 7 days, might capture more immediate abandonment behaviour, whereas a longer period may include users who are still in the decision-making process.

Incorporating Shopper Behavior Insights:

Leverage your understanding of shopper behaviour. Consider factors like the average time between cart addition and purchase, frequency of visits, and other buying signals. This information can help refine your audience for greater relevance and potential conversion.

Adjusting Parameters for Specific Campaign Goals:

Tailor the audience parameters to align with your campaign objectives. If your goal is to re-engage recent visitors, you might focus on short-term cart abandonment, such as targeting users who abandoned their cart in the last 7 days. For broader re-engagement strategies, you could extend this window to capture a larger group who abandoned their cart within the past 30 days or more.

Incorporate Shopper Behavior Insights:

Understanding shopper behaviour is crucial. Analyze past data to determine typical time frames between cart addition and purchase. This insight allows you to set a more effective time window for targeting. For instance, if data shows that most customers complete their purchases within a week of adding items to the cart, a 7-day window for cart abandonment might be more appropriate.

Finalize and Review the Query:

Once you’ve adjusted the parameters, review the query for accuracy. Ensure it aligns with the desired audience profile and campaign goals.

Test and Iterate:

After creating the audience, it’s advisable to test its effectiveness. Monitor the performance and make adjustments as needed. This iterative process is key to refining your audience for optimal results.

Apply the Audience in Campaigns:

Once satisfied with the audience definition, apply it to relevant campaigns. This targeted approach helps in effectively reaching out to potential customers who have shown interest but haven’t completed a purchase.

Continuous Learning and Adaptation:

Continuously gather data on audience behaviour and campaign performance. Use these insights to further refine your audience parameters, adapting to changing consumer behaviours and market trends.