Amplify Abandoned Cart Activations with Customer Behavioral Profiles

Struggling to understand why your shoppers are abandoning their carts before making it to checkout? Don’t worry, you’re in good hands. 

At our recent webinar, Snowflake, MessageGears, & Snowplow discussed the importance of using rich and reliable behavioral data to empower you with visibility into your end-to-end shopper behavior and effectively tackle the cart abandonment problem.

Key Takeaway & Highlights:

With the right data stack and customer behavioral profiles, you too can tackle cart abandonment by collecting rich, reliable, customizable first-party data on your end-to-end digital shopper behavior resulting in higher sales conversions and increasing customer lifetime value (CLV).

• Luke Ambrosetti, Partner Solutions Engineer at Snowflake, shared his thoughts on how customer engagement is powered through your data warehouse (the Snowflake Data Cloud), the importance of behavioral data, and the benefits of using a composable, modular model. 
• Mike Maloney, Field CDO at Snowplow, walked through how Snowplow’s predictive customer profiles can effectively help you tackle cart abandonment by re-engaging with your customers through personalization.
• Walter Rowland, SVP of Partnerships at MessageGears, discussed the importance of the Warehouse-native “connected app” approach and how it powers your customer engagement MarTech tools, cuts costs, and drives more relevant customer experiences.  

Missed the webinar? Watch the webinar on-demand here to revisit the discussion packed with insights. 

The Customer Data Problem

Marketers understand rich segmentation and personalization are essential when capturing and activating their audiences. However, many companies today turn to traditional packaged CDPs to capture and activate those cohorts, which leads to siloed copies of customer data, long onboarding times, and rigid data models.  

With only 58% of companies with a deployed CDP saying it delivers significant value according to the CDP Institute, it’s clear that packaged CDPs aren’t the solution. 

As our Field CDO, Mike Maloney perfectly summarized, “The problem isn’t a lack of tools - it’s a lack of true explainable, granular data to power the engine we need for good marketing professionals to make a difference.” Simply put, it’s a data problem, not a tooling problem where the issue lies in how the data is being collected and fed into your tools. 

To combat the data problem, Snowplow’s customer behavioral profiles provide you with a unified and complete descriptive view of your customer journey from 1st-party behavioral data created across browsers and unimpacted by privacy technology. Best of all, you have complete 100% data ownership.

How Customer Behavioral Profiles Recover Abandoned Carts

With your Snowplow customer behavioral data landing in your data warehouse of choice, ie yourSnowflake Data Cloud) , ), alongside your back-end data (such as customer data from your CRM, product database such as Postgres etc), you are able to build your customer behavioral profiles.

For an abandon basket campaign, how would you go about doing this with Snowplow? 

First, you need to be tracking robust user identifiers. Snowplow provides a number out of the box IDs, such as session IDs, cookie IDs, IP addresses etc. However what is most important for this use case is a first party user identifier. What format this will be in and how it is surfaced to your tracking layer in your website/app will vary from business to business, but you can use the Snowplow `setUserId()` method in the Javascript tracker for web (there are similar for iOS, Android etc). Whether this is an integer user ID, or a hashed version of the user’s email address (using SHA256 or higher to ensure security), this step is vital for linking your users’ browsing behavior and their known identities, allowing you to contact them in the future. 

From here, you want to ensure you are tracking important business events on your ecommerce site. Snowplow provides a set of ecommerce tracking methods and associated JSON schemas that you can use out of the box (or you can create your own custom data structures for your own specific business needs). 

However you choose to manage your tracking, you will want to ensure that:

1. You track the action of adding a product to the cart
2. You want to ensure you have the relevant product information attached to your events 
3. Ensure the user’s ID is collected with the specific events

Snowplow will automatically track other things that are needed for the abandoned cart use case, such as the page the user was on, the time the event happened, the device and user information etc. 

With the tracking in place in this way, you will know who added which products to their cart and when. 

Once this is in your data warehouse, you have some decisions to make. 

Deciding your abandoned cart workflow may seem straightforward, but in our experience, this is rarely the actual case. There are actually a number of things to consider and decide:

• What actually counts as abandoning?
  • Not interacting with the basket for X minutes? 
  • Leaving the site/app after Y minutes
  • Having an un-purchased item at the end of the day? 
  • Adding an item to favorites
• What time window should there be between the basket “abandonment” and the triggered communications (i.e. the email)? 
• Should every user be contacted in the same fashion? Should this be different based on the product or cart value? Maybe for specific brands or categories
• Should you send the abandon basket messages based on the total cart value or specific items? 

As you can see, determining your business specific approach is probably the key aspect to all of this. 

However, let’s make the following assumptions:

• We will define an abandonment as a user adding a product to the cart and not successfully checking out in that session
• We will aim to send an email to that user no later than 24 hours after the abandonment 
• We will only send abandonment emails for users with items <= $20 in their basket

Thankfully, since Snowplow has all of these properties within a single table, generating this segment of users is straightforward:

This will create a list of users who have abandoned their cart today, and the details of the products that are in their cart. 

This can be connected to the ESP of your choice using a tool such as MessageGears that can read directly from a data warehouse and connect to an email marketing tool of your choice. 

Ready to supercharge your business? 

Book a chat to see how Snowplow can work for you and learn how behavioral data can help recover your abandoned carts faster with customer behavioral profiles.