Customer Wishlist Item Trends

This dataset provides granular records of customer wishlist activity, capturing each addition and removal of products along with customer demographics and product details. It enables in-depth analysis of product popularity, customer preferences, and demand forecasting, making it ideal for optimizing inventory, marketing strategies, and personalized recommendations.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Identifying trending and high-demand products based on wishlist activity

Sample rows

preview · 8 of 70 rows · all 14 columns
wishlist_event_idstringevent_typestringproduct_pricefloatcustomer_location_countrystringcustomer_idstringproduct_idstringevent_datetimedatetimeproduct_categorystringproduct_namestringproduct_brandstringcustomer_ageintegercustomer_genderstringcustomer_location_citystringcustomer_location_statestring
wevt_0001add119.99USAcust_1023prod_30142023-11-09T14:21:13ZElectronicsWireless Earbuds ProSoundPulse28femaleNew YorkNY
wevt_0002remove59.5USAcust_1102prod_20212023-10-17T09:45:30ZHome & KitchenBlender UltraMixHomeEase34maleChicagoIL
wevt_0003add22.99USAcust_1045prod_40522023-11-13T17:38:52ZBooksThe Quantum EnigmaPenguin22otherSan FranciscoCA
wevt_0004add32USAcust_1007prod_50202023-09-28T12:08:10ZBeautyHydrating Face SerumGlowLine41femaleAustinTX
wevt_0005remove44.49USAcust_1090prod_20322023-10-25T20:15:44ZSportsYoga Mat DeluxeFitVibe35maleMiamiFL
wevt_0006add68.75USAcust_1117prod_30812023-11-06T13:07:51ZClothingSlim Fit JeansUrbanThreads26femaleLos AngelesCA
wevt_0007add24.99USAcust_1158prod_12052023-10-12T19:34:02ZToysBuilding Blocks SetPlayFun31femaleSeattleWA
wevt_0008remove199USAcust_1187prod_10092023-09-30T17:00:00ZElectronicsSmart Watch X2GearTech29maleBostonMA

What the 70 rows show

from the 70-row sample
  • 40.0median product_price
  • 4customer genders
  • 11product categories
  • 34median customer_age
event_type70 rows by value
0255043add61%27remove39%
product_price70 rows, in bands of 50
0255043146420010200400product_price →

Median 40.0, from 9.0 to 400.0.

customer_location_country70 rows · top 10 of 17 values
  1. USA35
  2. Belgium4
  3. France4
  4. Spain4
  5. Ireland3
  6. Switzerland3
  7. Italy3
  8. Norway2
  9. Germany2
  10. Portugal2
14 columns by typefrom the column list below
  • string 11
  • integer 1
  • float 1
  • datetime 1

Columns

14 columns in three groups
blueprint · 14 columns
columntypedescriptionexample
Text 11 columns
wishlist_event_idstringUnique identifier for each wishlist event (addition or removal).uniquewevt_0001
customer_idstringUnique identifier for the customer performing the wishlist action.cust_1023
product_idstringUnique identifier for the product added to or removed from the wishlist.prod_3014
event_typestringType of wishlist event: either addition or removal.add · removeadd
product_categorystringCategory or department to which the product belongs.11 categories · optionalElectronics
product_namestringName or title of the product.optionalWireless Earbuds Pro
product_brandstringBrand or manufacturer of the product.optionalSoundPulse
customer_genderstringGender of the customer.male · female · other · prefer_not_to_say · optionalfemale
customer_location_citystringCity where the customer is located.optionalNew York
customer_location_statestringState or region where the customer is located.optionalNY
customer_location_countrystringCountry where the customer is located.optionalUSA
Numbers 2 columns
product_pricefloatCurrent price of the product at the time of the wishlist event.0 or more · optional119.99
customer_ageintegerAge of the customer at the time of the wishlist event.0 or more · optional28
Dates and times 1 column
event_datetimedatetimeTimestamp when the wishlist event occurred.2023-11-09T14:21:13Z

Use it for

  • median product…40.070 rowsmean product price by…67.7add65.3remove

    An e-commerce dashboard

    Product_price by event_type and a breakdown of customer_location_country. Excel, Power BI or Tableau.

  • What pattern can you find in 70 rows?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 70 rows.

  • A software demo

    Believable wishlist events with customer_id, product_id and event_type to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 14 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset70 rows14 columns
Yours1,000 rows14 columnscustomer_location_city: UK only

blueprint · customer-wishlist-item-trends

Behind this dataset

Same schema. As many rows as you need.

These 70 rows came out of a blueprint — 14 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Track item added/removed events
  • Log user, item, and timestamp
  • Sum daily additions and removals
  • Flag items with rapid popularity changes
  • Aggregate trends by item category
Rows
Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
customer-wishlist-item-trends

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