Data Views
New Products
Data Octopus data view: performance during a product’s first 30 days in the product file. Field specifications, insights and troubleshooting.
View overview – New Products
This report focuses on newly added products and tracks their performance across data sources during their first 30 days in the product file. It combines the product file, Google Analytics 4, Google Ads Shopping campaigns and Meta Ads to show how products perform immediately after launch. Products added within the last 30 days are identified using product-age tracking in Data Octopus.
Key metrics
- Google Analytics visibility metrics (item views, add-to-cart events and checkouts), indicating customer interest and purchase intent for new products.
- Advertising performance, with separate Google Ads and Meta Ads figures to monitor early product effectiveness.
- Conversion metrics, including add-to-cart conversion rate and purchase quantity, to identify new products that resonate most with customers.
- Revenue and cost metrics across platforms, with ROAS and COS to assess early marketing efficiency.
- Availability tracking to monitor whether new products remain in the product file.
Data date range
The report covers the last 30 days, ending yesterday.
Example insights from the data view
- 1Analyse new products using your preferred metrics. The table is sorted by Google Analytics revenue by default; change the sort to find the highest impressions, clicks or other metrics in the first days after introduction.
- 2Monitor how quickly new products gain traction using Google Analytics metrics. Item views (view_item), add-to-cart events (add_to_cart) and checkouts (begin_checkout) show how quickly customers become interested.
- 3Check whether new products receive impressions in all connected advertising systems. Impressions in Google Ads but not Meta Ads may indicate that Meta Ads campaign settings need optimisation for new products.
- 4Compare CTR across advertising platforms to identify which new products attract the most interest. Higher CTR may suggest stronger market demand and potential to increase the advertising budget.
- 5Analyse revenue-to-cost ratios (ROAS/COS) to quickly identify new products with the best advertising potential and those deserving more budget in the first days after launch.
- 6Track ADD TO CART CR to identify new products that best meet customer needs and have the greatest sales potential.
- 7Use purchased item quantity to identify new products that are quickly gaining popularity and may require extra attention in inventory management.
- 8Monitor availability to ensure popular new arrivals do not lose sales potential because of stock shortages.
- 9Monitor promotion_status to assess new products at regular and promotional prices during their initial period.
- 10Analyse product_category_1/2/3 to identify categories that perform best when introducing new products and where future launches should be focused.
Product file
| Field name | Description |
|---|---|
| availability | Product-file availability: "in stock" if the new product is still present in the product file, otherwise "out of stock". |
| id | Product variant identifier. |
| title | Product name. |
| product_category_1 | Level 1 category. |
| product_category_2 | Level 2 category. |
| product_category_3 | Level 3 category. |
| brand | Manufacturer name. |
| price | Product price. |
| sale_price | Sale price. |
Data Octopus
| Field name | Description |
|---|---|
| promotion_status | If the product has a Sale Price, it is labelled "on promotion". |
| product_age_days | Product age in days, measured from the date the product first appeared in the product file. |
Google Analytics 4
| Field name | Description |
|---|---|
| ga4_item_view_event | Number of view_item events in Google Analytics. |
| ga4_item_add_to_cart_event | Number of add_to_cart events in Google Analytics. |
| ga4_item_check_out_event | Number of begin_checkout events in Google Analytics. |
| ga4_add_to_cart_conversion_rate | Ratio of add_to_cart events to view_item events in Google Analytics. |
| ga4_item_purchase_quantity | Total quantity of products in purchase events in Google Analytics. |
| ga4_item_netto_revenue | Total net revenue (excluding tax and shipping) from purchase events in Google Analytics. |
Google Ads Shopping Campaigns
| Field name | Description |
|---|---|
| gads_impressions | Number of product impressions from Google Ads Shopping campaigns. |
| gads_clicks | Number of product clicks from Google Ads Shopping campaigns. |
| gads_ctr | Percentage ratio of clicks to impressions from Google Ads Shopping campaigns. |
| gads_netto_costs | Total net costs of Google Ads Shopping campaigns. |
| gads_conversions | Number of product conversions from Google Ads Shopping campaigns. Only primary conversions used as campaign goals are counted. |
| gads_netto_conversions_value | Total net conversion value from Google Ads Shopping campaigns. Only primary conversions used as campaign goals are counted. |
| gads_roas | Ratio of net conversion value to net Google Ads Shopping campaign costs (Net Conversion Value / Net Costs). |
| gads_cos | Ratio of net Google Ads Shopping campaign costs to net conversion value (Net Costs / Net Conversion Value). |
| gads_campaign_count | Number of Google Ads campaigns in which the product received impressions. |
| gads_campaign_names | Names of Google Ads campaigns in which the product received impressions. If none, returns "Not seen in any campaign". |
Meta Ads Shopping Campaigns
| Field name | Description |
|---|---|
| meta_impressions | Number of product impressions from Meta Ads Shopping campaigns. |
| meta_clicks | Number of product clicks from Meta Ads Shopping campaigns. |
| meta_ctr | Percentage ratio of clicks to impressions from Meta Ads Shopping campaigns. |
| meta_netto_costs | Total net costs of Meta Ads Shopping campaigns. |
Totals
| Field name | Description |
|---|---|
| total_impressions | Total product impressions from Shopping campaigns across every advertising system connected to Data Octopus. For example, 1 impression each from Google Ads, Meta Ads and Bing Ads adds up to 3 impressions. |
| total_clicks | Total product clicks from Shopping campaigns across every advertising system connected to Data Octopus. For example, 1 click each from Google Ads, Meta Ads and Bing Ads adds up to 3 clicks. |
| total_ctr | Percentage ratio of total clicks to total impressions from Shopping campaigns across all advertising systems connected to Data Octopus. |
| total_costs | Total net Shopping campaign costs across every advertising system connected to Data Octopus. For example, PLN 1 each from Google Ads, Meta Ads and Bing Ads adds up to PLN 3. |
| total_roas | Ratio of Google Analytics net revenue to total net Shopping campaign costs across all advertising systems connected to Data Octopus (Net Revenue / Total Net Costs). |
| total_cos | Ratio of total net Shopping campaign costs across all advertising systems connected to Data Octopus to Google Analytics net revenue (Total Net Costs / Net Revenue). |
Questions and answers
What should I do if the data view shows no data for my store?
In the Data Octopus app, use the chat icon in the bottom-right corner to report the issue to the Data Octopus team.
Why is data missing from a source such as Google Ads?
Possible causes: the data source is not connected; product identifiers differ across systems (for example, warehouse SKU in Google Ads and store ID in GA4); or data uses different identification levels (product versus variant). The report requires consistent product identifiers and identification levels across all sources.
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