Sync data from
Amazon Redshift to Help Scout

Model your Amazon Redshift data using any of these methods

• dbt Model Selector

  

  Sync directly with your dbt models saved in a git.

• Looker

  

  Query using looks. Hightouch turns your look into SQL and will pull from your source.

• SQL Editor

  

  Create and Edit SQL from your browser. Hightouch supports SQL native to Amazon Redshift.

• Table Selector

  

  Select available tables and sheets from Amazon Redshift and sync using existing views without having to write SQL.

• Customer Studio

  

  For less technical users, pass traits and audiences from Amazon Redshift using our visual segmentation builder.

Does this integration support in-warehouse planning?

Yes, if you integerate Amazon Redshift and Help Scout using Hightouch, in-warehouse planning is supported.

Great, but what is in-warehouse planning?

Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Help Scout). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.

The issue here is this can be slow for large models.

Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this works here.

Why is it valuable to sync Amazon Redshift data to Help Scout?

Lifecycle marketing platforms like Help Scout are built on omnichannel experiences and multiple touch points. They allow marketers to experiment and iterate at a moment's notice. However, to build personalized experiences for your customers, you first need access to the rich behavioral data in your data warehouse.

This might include data models built around unique objects in your business like workspaces, subscriptions, playlists, or even custom audiences your data team has defined based on core metrics like items in cart, pages viewed, average order value, time spent in-app, etc.

To truly convert new users and retain existing customers, you need to build customized experiences that feel unique to each user. Usually, this includes sending custom emails, SMS messages, push notifications, or even web notifications.

For example, if you want to encourage users who abandoned their shopping cart in the last seven days to complete their purchase, you might want to send an email letting them know your stock is running low. Maybe you want to target new users with a special offer through an in-app/on-browser notification, or perhaps you simply want to send an SMS notifying your customers that their order has shipped.

Either way, the only complete 360-degree view of your customer lives in your warehouse. Your lifecycle marketing tool is only as good as the data you give it. If you want to offer the best possible experience to your customers, you need to take advantage of the data in your warehouse.

Why should you use reverse ETL to connect Amazon Redshift and Help Scout data?

Whenever your marketing team wants to launch a new campaign, the typical process is to request a specific data set from your data team (e.g., can you give a list of every user who's visited our pricing page over the last seven days?) One-off requests like these are frequent, and each one pulls away from high-value work your data team could be doing.

To facilitate this, your data engineers must download ad-hoc CSV files so your marketing team can upload that data into Help Scout. Alternatively, your data team might build and maintain custom pipelines to consistently ingest that data into Help Scout. The problem is CSV files aren't fresh, and data pipelines are prone to failure because integrating with third-party APIs is hard.

Ultimately, your marketers want to self-serve, and that's only possible with Reverse ETL. With Hightouch, you can leverage your existing data models (e.g., lifetime value, last-login date, workspaces, annual recurring revenue, etc.) in your warehouse and sync that data directly to fields in Help Scout. You can schedule your syncs to run sequentially or define a set cadence (e.g., running your syncs while your marketing campaign is in progress.)

Hightouch has a visual audience builder so your marketers can self-serve and build custom audiences using the parameters your data team has set. If any of your rows fail, Hightouch will automatically retry them later in the next sync. You can easily view all of your API payload requests/responses in a live debugger, and your sync logs can be written back directly to Snowflake. Ultimately your lifecycle marketing tool is only as good as the data you give it.