Item-data import can bring your entire product catalog into Analytics, letting you measure user behavior, site traffic, revenue and key events with item-specific data such as size, color, style, or whatever item dimensions make sense for your business.
Importing item data into Analytics simplifies and reduces the amount of ecommerce data you need to send along with events: a single Item ID or SKU sent to Analytics at collection time can be joined with your imported item data to populate ecommerce dimensions and metrics in your reports.
How item-data import works
To use this feature, you must be collecting recommended events for online sales.
Item-data import works by using the Item ID dimension as a key. You can send one or more item IDs with your events.
Item-data import maps item_ids in historically and ongoing collected events to the item-field values you import, and processes and displays the uploaded values (in audiences, reports, and explorations) instead of the originally collected values. For example, if an item_id is used in a report (individually or in aggregate), Analytics replaces the value that was collected originally with the value that was uploaded and displays the uploaded value in the report. This lets you fix or update collected values in order to restructure your data or make it useful again. In addition, if there are newer uploaded values at query time that haven't been processed yet, Analytics uses those newer values instead of the processed values.
Deleting an item data source from the data-upload service does not delete the values stored in the item dimensions for the affected items as those values may have changed. If necessary, and as with all other collection methodologies, you may need to follow up with a data deletion to remove data uploaded via this method.
Imported data needs to be processed before it appears in reports. Once processing is complete, it may take up to 24 hours before the imported data is applied to incoming event data.
Analyze and take action
The default item dimensions (e.g., Item brand, Item category, Item name) appear in the Ecommerce purchases report.
You can use item data in Explorations to explore funnels and segment overlap.
You can also segment your users in Explorations by their shopping behavior based on imported item data
Create a CSV file
Create a CSV file of item dimensions. For example:
| item_id | item_name | item_cat1 | item_cat2 | item_cat3 | brand | variant |
|---|---|---|---|---|---|---|
| MT100001 | baseball_t | men | casual | tees | fauxTfaux | small |
| MT100001 | baseball_t | men | casual | tees | fauxTfaux | medium |
| MT100001 | baseball_t | men | casual | tees | fauxTfaux | large |
Upload the data
The general upload procedure is outlined in About Data Import.
When you create the data source, select Item data.
When you map Analytics fields to your imported fields, you'll see something like the following (based on the example above):
When you map Analytics fields to your imported fields, you'll see something like the following:
In the first column you'll see:
- The Analytics field (in this case, Id) on which you're joining your data. Also called the schema key.
- Item dimensions that match the fields in your CSV. (e.g., Name, Brand, etc.)
In the second column, you select the matching fields in your CSV:
After you upload your data, it can take up to 24 hours for Analytics to make that data available in reports, audiences, and explorations. Users have to engage with your items after you upload the data in order for those item dimensions to be associated with user activity.
You can overwrite dimension values by uploading new ones.
Deleting an item-data source from the data-upload service does not delete the values stored in the dimensions for the related items. If necessary, and as with all other collection methodologies, you may need to follow up with a user deletion or data deletion to remove data uploaded via Data Import.
Data-source details
Legend
- Scope: the scope determines which events will be associated with the import-dimension values. There are four levels of scope: user, session, event, and item. Item-data import is used to widen item-scoped metadata within ecommerce events.
- Schema key: lists the key dimensions or metrics. The key is used to join the data you upload with the existing data in your events for this data-source type.
- Imported data: lists the dimensions and metrics available for the data you upload to Analytics.
The dimensions and metrics listed for the schema are for reference only and may not be complete; the actual dimensions and metrics available will appear in the user interface when you create the data source.
| Scope | Event |
|---|---|
| Schema key | Item ID (Product ID/SKU) (required) |
| Imported data |
Dimensions:
|
Template
Here is an example CSV template for item data. If you need to create your upload files by hand, use this example as a guide.
| item_id | item_name | item_cat1 | item_cat2 | item_cat3 | item_cat4 | item_cat5 | brand | variant |
|---|---|---|---|---|---|---|---|---|
| p100001 | itemname1 | cat1a | cat2b | cat3c | cat4d | cat5e | brand_foo | variant_small |
| p100002 | itemname2 | cat1s | cat2b | cat3c | cat4d | cat5e | brand_bar | variant_medium |
| p100003 | itemname3 | cat1v | cat2w | cat3x | cat4y | cat5z | brand_bar | variant_large |