diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7396c14..d077106 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,9 @@
   - For each staging model, if the source table is not found in any of your schemas, the package will create a table with one row with null values for Redshift destinations. There will be no change in behavior in other non-Redshift warehouses.
   - This is necessary as Redshift will ignore explicit data casts when a table is completely empty and materialize every column as a `varchar`. This throws errors in downstream transformations in the `shopify` package. The 1 row will ensure that Redshift will respect the package's datatype casts.
 
+## Documentation
+- Corrected references to connectors and connections in the README. ([#94](https://github.com/fivetran/dbt_shopify_source/pull/94))
+
 # dbt_shopify_source v0.14.0
 [PR #93](https://github.com/fivetran/dbt_shopify_source/pull/93) includes the following changes:
 
diff --git a/README.md b/README.md
index 688e731..fcffbcb 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@
 ## How do I use the dbt package?
 ### Step 1: Prerequisites
 To use this dbt package, you must have the following:
-- At least one Fivetran Shopify connector syncing data into your destination.
+- At least one Fivetran Shopify connection syncing data into your destination.
 - A **BigQuery**, **Snowflake**, **Redshift**, **Databricks**, or **PostgreSQL** destination.
 
 #### Databricks dispatch configuration
@@ -49,7 +49,7 @@ packages:
 ```
 
 ### Step 3: Define database and schema variables
-#### Single connector
+#### Single connection
 By default, this package runs using your destination and the `shopify` schema. If this is not where your Shopify data is (for example, if your Shopify schema is named `shopify_fivetran` and your `issue` table is named `usa_issue`), add the following configuration to your root `dbt_project.yml` file:
 
 ```yml
@@ -58,8 +58,8 @@ vars:
     shopify_schema: your_schema_name 
 ```
 
-#### Union multiple connectors
-If you have multiple Shopify connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the `source_relation` column of each model. To use this functionality, you will need to set either the `shopify_union_schemas` OR `shopify_union_databases` variables (cannot do both) in your root `dbt_project.yml` file:
+#### Union multiple connections
+If you have multiple Shopify connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the `source_relation` column of each model. To use this functionality, you will need to set either the `shopify_union_schemas` OR `shopify_union_databases` variables (cannot do both) in your root `dbt_project.yml` file:
 
 ```yml
 # dbt_project.yml
@@ -142,8 +142,10 @@ models:
     +schema: my_new_schema_name # leave blank for just the target_schema
 ```
 
-#### Change the source table references (not available if unioning multiple Shopify connectors)
-If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable.  This config is available only when running the package on a single connector:
+
+#### Change the source table references (not available if unioning multiple Shopify connections)
+If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable: This config is available only when running the package on a single connection:
+
 > IMPORTANT: See this project's [`src_shopify.yml`](https://github.com/fivetran/dbt_shopify_source/blob/main/models/src_shopify.yml) for the default names.
     
 ```yml