Data Subject Request API Version 1 and 2
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Alexa
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
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Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
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Create an Input
Start capturing data
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What are predictive attributes?
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Introduction
Introduction
Rudderstack
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Snowflake (Define Your Own Schema)
Aliasing
Next Best Actions (NBAs) are a type of Predictive Attribute that determine which action or offer (from a set of choices that you define) is mostly likely to result in your desired business outcome. By generating AI-powered recommendations at the level of individual users, NBA takes the guesswork out of your campaigns, and helps yon achieve common business goals like increasing retention or driving upsells.
Let’s explore how a gym would use NBA to determine the membership tier offer that is most likely to turn trial customers into paying members.
Focus on trial users or existing members on lower tiers who regularly attend the gym (e.g., moderate to high engagement with classes, personal training sessions, or equipment). Avoid targeting users who are already on the Elite membership plan.
Use NBA (Next Best Action) to assess different gym membership tiers—Basic, Premium, and Elite—as potential upgrades based on the user’s activity level and interests.
Let NBA analyze factors like the user’s workout habits, class attendance, and upcoming promotions (e.g., a new fitness class launch or personal training package) to recommend the best tier, maximizing the likelihood of an upsell.
Let’s explore how a retail or financial institution would use NBA to determine the best co-branded financial product offer for frequent shoppers.
Focus on customers who frequently shop in-store or online and demonstrate financial engagement, such as frequent use of store loyalty rewards or payment methods. Avoid targeting customers who already have one of the available co-branded products.
Use NBA (Next Best Action) to assess different financial products—Savings Account, Credit Card, and Line of Credit—as potential offers based on the user’s purchasing patterns, credit needs, and spending habits.
Let NBA analyze factors like transaction history, average spending, and upcoming promotional events (e.g., a seasonal sale with cashback bonuses) to recommend the most suitable financial product. This approach increases the likelihood of new product sign-ups.
Let’s explore how a media company would use NBA to determine the best type of content to promote for increased user engagement and retention.
Focus on users who have shown moderate to high engagement but may need additional encouragement to stay active on the platform. Avoid targeting users who have already reached high engagement with frequent content consumption across multiple genres.
Use NBA (Next Best Action) to assess different content types—Comedy, Horror, and Action—as potential promotions based on the user’s viewing history, preferences, and genre exploration.
Let NBA analyze factors like the user’s past viewing patterns, session duration, and upcoming releases (e.g., a new season of a popular show in their preferred genre) to recommend the most engaging content. This approach boosts user activity and long-term retention.
Let’s explore how a quick-service restaurant (QSR) would use NBA to determine the best product category to recommend for increasing user purchases.
Focus on customers who have made recent purchases but have not yet added multiple categories to their orders. Avoid targeting customers who consistently order a full meal that already includes appetizers, mains, and desserts.
Use NBA (Next Best Action) to assess different product categories—Appetizer, Main, and Dessert—as potential recommendations based on the user’s ordering patterns and preferences.
Let NBA analyze factors like the user’s past order history, favorite items, and upcoming promotions (e.g., a limited-time combo deal or seasonal menu item) to recommend the most appealing product category. This strategy encourages larger orders and higher purchase value.
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