new abandoned browse composable cdp solution accelerator #1139
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mscwilson
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Feb 6, 2025
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| # Introduction | ||
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| Abandoned Browse is a common e-commerce problem where users show interest in products but don't complete a purchase. It is also referred to as "shopping cart abandonment", "abandoned basket" or "abandoned cart". Despite how common it is, it is still a challenge to implement a successful re-engagement campaign when using traditional marketing tools because they lack all the context needed to create a compelling personalized message. At Snowplow, we have found that a composable CDP approach is the best way to solve this problem and this tutorial has been written to show that it is straightforward to get started. |
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| Abandoned Browse is a common e-commerce problem where users show interest in products but don't complete a purchase. It is also referred to as "shopping cart abandonment", "abandoned basket" or "abandoned cart". Despite how common it is, it is still a challenge to implement a successful re-engagement campaign when using traditional marketing tools because they lack all the context needed to create a compelling personalized message. At Snowplow, we have found that a composable CDP approach is the best way to solve this problem and this tutorial has been written to show that it is straightforward to get started. | |
| Abandoned browse is a common e-commerce problem where users show interest in products but don't complete a purchase. It is also referred to as "browse abandonment", "shopping cart abandonment", "abandoned basket", or "abandoned cart". Despite how common it is, it is still a challenge to implement a successful re-engagement campaign when using traditional marketing tools because they lack all the context needed to create a compelling personalized message. At Snowplow, we have found that a composable CDP approach is the best way to solve this problem. This tutorial has been written to show that it is straightforward to get started. |
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| Abandoned Browse is a common e-commerce problem where users show interest in products but don't complete a purchase. It is also referred to as "shopping cart abandonment", "abandoned basket" or "abandoned cart". Despite how common it is, it is still a challenge to implement a successful re-engagement campaign when using traditional marketing tools because they lack all the context needed to create a compelling personalized message. At Snowplow, we have found that a composable CDP approach is the best way to solve this problem and this tutorial has been written to show that it is straightforward to get started. | ||
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| This tutorial demonstrates how to implement an Abandoned Browse tracking and re-engagement system using Snowplow, Snowflake, and Census. This solution helps e-commerce businesses identify and re-engage users who have shown interest in a product (e.g. viewed something for 10+ seconds) but haven't proceeded further. |
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| This tutorial demonstrates how to implement an Abandoned Browse tracking and re-engagement system using Snowplow, Snowflake, and Census. This solution helps e-commerce businesses identify and re-engage users who have shown interest in a product (e.g. viewed something for 10+ seconds) but haven't proceeded further. | |
| This tutorial demonstrates how to implement an abandoned browse tracking and re-engagement system for web using Snowplow, Snowflake, and Census. This solution helps e-commerce businesses identify and re-engage users who have shown interest in a product (e.g. viewed something for 10+ seconds) but haven't proceeded further. |
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| ### Track User Engagement Time | ||
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| Use the `enableActivityTracking` function to calculate the time the user is actively engaged on the page: |
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add an intro sentence here about why this is needed, it took me a second to get it 🙃
| - **minimumVisitLength**: The minimum time (in seconds) a user must stay on the page before tracking starts. | ||
| - **heartbeatDelay**: The interval (in seconds) at which activity pings are sent. | ||
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| If you change these parameters, make sure to update the values in the [Data Modeling](./data-modeling.md#identifying-most-viewed-but-not-added-to-cart-products) step. This event must be called before the `trackPageView` function. |
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| If you change these parameters, make sure to update the values in the [Data Modeling](./data-modeling.md#identifying-most-viewed-but-not-added-to-cart-products) step. This event must be called before the `trackPageView` function. | |
| If you change these parameters, make sure to update the values in the [Data Modeling](./data-modeling.md#identifying-most-viewed-but-not-added-to-cart-products) step. | |
| This function must be called before the `trackPageView` function. This example shows the default values: 5 seconds for each. |
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| First, initialize the Snowplow JavaScript tracker. Below is an example of how to set up the tracker: |
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| 1. Configure timing: | ||
| - Set delay after trigger: 1 hour | ||
| - Set quiet hours: e.g., 9 PM - 9 AM local time |
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| - Set quiet hours: e.g., 9 PM - 9 AM local time | |
| - Set quiet hours e.g. 9 PM - 9 AM local time |
| 1. Configure timing: | ||
| - Set delay after trigger: 1 hour | ||
| - Set quiet hours: e.g., 9 PM - 9 AM local time | ||
| - Set frequency capping: Max 1 email per user per 24 hours |
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| - Set frequency capping: Max 1 email per user per 24 hours | |
| - Set frequency capping e.g. maximum 1 email per user per 24 hours |
| ## Campaign Settings | ||
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| 1. Configure timing: | ||
| - Set delay after trigger: 1 hour |
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| - Set delay after trigger: 1 hour | |
| - Set delay after trigger e.g. 1 hour |
| - Remove users from the campaign audience once they've engaged | ||
| ::: | ||
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| ## Campaign Settings |
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where are these set? maybe screenshot useful?
| - In Snowflake or Census, check the `winback_successful` column for the user has been set to true | ||
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