Integrate Delighted with Shopify
Delighted is a service that employs single question surveys to provide businesses with real-time customer feedback. Each survey question can have a rating scale for customers to select from as well as a section where customers have the option to leave a free-form comment. This provides both a numerical score - that can be collected to create a Net Promoter Score (NPS) - and useful customer feedback that Delighted can filter and search to retrieve the most useful responses for a given purpose.
Shopify is an eCommerce platform that provides tools for both online and physical sales. On Shopify, users can set up an online store with pre-made themes. They can also accept payments from a variety of sources and use the analytics to look at their business’s sales trends. This can help them understand where they need to better focus their sales and marketing efforts.
Popular Use Cases
Bring all your Delighted data to Amazon Redshift
Load your Delighted data to Google BigQuery
ETL all your Delighted data to Snowflake
Move your Delighted data to MySQL
Bring all your Shopify data to Amazon Redshift
Load your Shopify data to Google BigQuery
ETL all your Shopify data to Snowflake
Move your Shopify data to MySQL
Integrate Delighted With Shopify Today
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Delighted's End Points
Create a survey recipient, including their customer ID, email address and phone number. Then, you can customize your survey delays based on your customers’ needs and preferences, specifying how you want the surveys sent - via SMS or email - and how frequently you want them sent.
Retrieve data from customer responses, including the score they selected, any comments they left in response to the survey and the person ID for the customer (which allows you to continue to track their responses). Additionally, use this data to create and update your Net Promoter Score, which can help provide customer analytics both within Delighted and in other data sources via integration.
View important metrics for your account like your NPS and the percentage of your respondents that identified as promoters, passives, or detractors. This provides a broader view of your survey performance that can help you determine your overall business performance.
When someone unsubscribes, you can maintain their previous survey response data and view their old emails. When integrated with other user data, this information can provide key business insights. It can also be used to run an array of business analyses, including predictive analytics.
Shopify's End Points
Track checkouts that were added to a customer’s cart but not completed as sales. This field includes data about the customer, the product and the reason for cancellation. It can help determine which products are most commonly abandoned at checkout and why, allowing you to run better predictive analyses about your future products and customers.
Retrieve basic customer information - such as ID, email, mailing address, and name - as well as data about customer behavior, such as the last order a customer made, their total amount spent or how many orders they have made with your company. You can then use this data to focus your marketing efforts towards specific customers or demographics.
Retrieve important data about an order request, such as customer contact information, the product ordered or the status of the order itself. Then, use this field to track important sales data like what products are being ordered the most or sales trends based on region or product price.
Create any number of product groupings and view data ranging from the product name and product ID to how much the product weighs, when it was created and how much it costs. Then, use that data to track trends and understand what types of products have been successful and why.
Track any exchange of money that occurs on Shopify, including completed sales, refunds and voided orders. This data can also track the actual revenue generated from your orders via their order ID’s, which will provide you with a sales-focused view of how well your business is performing.
Capture data from any transaction where the money has been refunded to the customer or any transaction where an item has been returned after being ordered. You can then view details about how much was refunded, what products were returned and whether or not those products have been restocked. This information can ultimately help you understand which products are successful, which are not and why.