• Gaming
  • 19 columns
  • 85 rows
  • 8 formats

In-Game Purchase Transaction Logs

This dataset provides granular logs of in-game purchase transactions, capturing player identifiers, item details, pricing, discounts, payment methods, and contextual data such as platform and region. It enables deep analysis of player spending behavior, monetization effectiveness, and the impact of promotions or platform differences. The dataset is ideal for optimizing in-game economies, personalizing offers, and supporting revenue analytics in the gaming industry.

  • opened 6 times
  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Analyzing player spending habits and lifetime value

Sample rows

preview · 8 of 85 rows · all 19 columns
transaction_idstringitem_categorystringquantityintegeris_first_purchasebooleangame_idstringplayer_idstringtransaction_datetimedatetimeitem_idstringitem_namestringprice_per_unitfloattotal_amountfloatdiscount_amountfloatfinal_amountfloatcurrencystringpayment_methodstringplatformstringregionstringpromotion_codestringsession_idstring
TXN001skin1trueGME01PLR0012024-04-14T17:22:00ZITM1001Legendary Sword14.9914.99014.99USDcredit_cardPCUSblankSES-1001
TXN002currency1trueGME02PLR0022024-04-15T09:08:32ZITM2002500 Gold Coins4.994.9913.99EURpaypalAndroidDESPRING24SES-2002
TXN003boost2trueGME01PLR0032024-04-15T13:55:27ZITM1002XP Boost (3 Days)2.995.9805.98USDapp_storeiOSUSblankSES-3003
TXN004consumable3falseGME01PLR0012024-04-16T11:42:13ZITM1003Health Potion Pack1.494.4704.47USDin-game_currencyPCUSblankSES-1001
TXN005bundle1trueGME03PLR0042024-04-16T20:31:55ZITM3001Starter Bundle7.997.9916.99GBPgift_cardConsoleGBNEWSTARTSES-4004
TXN006skin1falseGME02PLR0022024-04-17T07:19:45ZITM2003Rare Mount9.999.9927.99EURpaypalAndroidDESPRING24SES-2002
TXN007consumable5trueGME03PLR0052024-04-17T16:11:47ZITM3002Energy Refill0.994.9504.95GBPcredit_cardConsoleGBblankSES-5005
TXN008bundle1trueGME04PLR0062024-04-18T10:43:33ZITM4001Season Pass19.9919.99514.99USDapp_storeiOSCAPASS2024SES-6006

What the 85 rows show

from the 85-row sample

Consumable (item category) stands out: mean quantity is 3.3, against 1.2 for the rest.

  • 47%is_first_purchase = true
  • 1median quantity
  • 5currencies
  • 5platforms
  • 6payment methods
  • 9regions
Mean quantity by item_category85 rows
0241.1skin22 rows1.4boost20 rows3.3consum…17 rows1.3curren…14 rows1.0bundle12 rows
quantity85 rows, in bands of 1
03060571663200011610quantity →

Median 1, from 1 to 10.

game_id85 rows · 10 values
  1. G-BETA-316
  2. G-OMEGA-214
  3. G-ALPHA-112
  4. GME0111
  5. GME027
  6. GME037
  7. GME055
  8. GME065
  9. GME044
  10. GME074
19 columns by typefrom the column list below
  • string 12
  • integer 1
  • float 4
  • datetime 1
  • boolean 1

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 12 columns
transaction_idstringUnique identifier for each in-game purchase transaction.uniqueTXN001
player_idstringUnique identifier for the player making the purchase.PLR001
game_idstringIdentifier for the game in which the purchase was made.10 gamesGME01
item_idstringIdentifier for the purchased in-game item or bundle.ITM1001
item_namestringName or description of the purchased item.Legendary Sword
item_categorystringCategory of the purchased item (e.g., currency, skin, boost, consumable).5 categoriesskin
currencystringCurrency code used for the transaction (ISO 4217, e.g., USD, EUR, JPY).5 currenciesUSD
payment_methodstringPayment method used (e.g., credit_card, paypal, app_store, gift_card, in-game_currency).6 valuescredit_card
platformstringPlatform where the transaction occurred (e.g., iOS, Android, PC, Console, Web).6 valuesPC
regionstringGeographical region or country code of the player at the time of purchase (ISO 3166-1 alpha-2).9 regions · optionalUS
promotion_codestringPromotion or coupon code applied to the transaction, if any.optionalSPRING24
session_idstringIdentifier for the game session during which the purchase was made.optionalSES-1001
Numbers 5 columns
quantityintegerNumber of units of the item purchased in this transaction.1 or more1
price_per_unitfloatPrice per single unit of the item in the transaction currency.0 or more14.99
total_amountfloatTotal amount paid for this transaction (quantity x price_per_unit), before discounts.0 or more14.99
discount_amountfloatTotal discount applied to this transaction, if any.0 or more · optional0
final_amountfloatFinal amount paid after discounts and promotions.0 or more14.99
Dates and times 1 column
transaction_datetimedatetimeDate and time when the purchase transaction occurred (UTC).2024-04-14T17:22:00Z
True or false 1 column
is_first_purchasebooleanIndicates if this is the player's first-ever in-game purchase.true

Use it for

  • is first purch…47%40 of 85 rowsmean quantity by item…1.1skin1.4boost3.3cons…1.3curr…

    A gaming dashboard

    The is_first_purchase rate, quantity by item_category and a breakdown of game_id. Excel, Power BI or Tableau.

  • Why do the 17 consumable rows have a mean quantity of 3.3?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable transactions with player_id, game_id and transaction_datetime to fill a screen in front of a buyer.

Not quite right?

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This dataset85 rows19 columns
Yours1,000 rows19 columnsregion: UK only

blueprint · in-game-purchase-transaction-logs

Behind this dataset

Same schema. As many rows as you need.

These 85 rows came out of a blueprint — 19 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
  • Record transaction amount and item
  • Log player ID and timestamp
  • Flag refunds or chargebacks
  • Aggregate purchases by item category
  • Identify top 10% spenders
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
in-game-purchase-transaction-logs

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