Wishlist report
Products added to customer wishlists — counts, ranking and export guidance.
At a glance
- Purpose: surface product-level wishlist additions over a selected date range so you can identify popular items and measure interest.
- Primary visuals: a ranked bar chart showing top wishlisted products and a table under the chart listing the same rows. Filters allow narrowing by date.
Chart overview
- Visual: horizontal or vertical bars representing wishlist counts per product (common metric: quantity added to wishlists).
- Behaviour: bars are ordered by quantity so top wishlisted products are shown first; long product names can be inspected in the table or on hover.
- Metric options: quantity (number of times added), unique_users (distinct customers who added the product).
Filters and controls
- Date range selector: pick the period to analyse (from / to).
- Brand and Category filters: restrict the results to a subset of the catalog.
- Segment filters: channel or campaign to analyse source traffic.
Table and export
Under the chart there is a table listing products in rank order (same rows as the chart). Typical columns exported in CSV:
sku,product_name,quantity,item_price,image_url,product_url
SKU-000123,Example Product,120,25.00,https://.../img.jpg,https://.../product/sku-000123- The CSV includes one row per product containing SKU (if available), product name, quantity (times added to wishlist), item_price (current unit price), image_url and product_url.
How to interpret the report
- High quantity, low item_price: many users are interested but at a low price point — consider bundling or merchandising strategies.
- High quantity and high item_price: high-interest, high-value items — ensure availability and consider promotions to convert interest to purchase.
Walkthrough: quick start
Choose the date range you want to analyse.
Use Brand / Category filters to focus on the assortment you care about.
Sort or inspect the table to find the products with the highest wishlist counts.
Click Export → CSV to download the product rows for offline analysis.
How is this guide?
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