Customer Purchase Frequency Patterns

This dataset provides a comprehensive view of customer purchase frequency patterns, including total purchases, recency, spending, and lapsed status. It is designed to support marketing optimization, retention analysis, and win-back campaign targeting by offering actionable insights into customer engagement and churn risk.

  • opened 6 times
  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Identifying lapsed customers for win-back marketing campaigns

Sample rows

preview · 8 of 120 rows · all 19 columns
customer_idstringlapse_reasonstringtotal_spentfloatwinback_campaign_eligiblebooleanregionstringemailstringfirst_namestringlast_namestringsignup_datedatelast_purchase_datedatetotal_purchasesintegeraverage_purchase_valuefloataverage_days_between_purchasesfloatis_lapsedbooleanpreferred_channelstringcitystringstatestringpostal_codestringcountrystring
C00001n/a2478.33falseEuropemarina.dupont19@orange.frMarinaDupont2022-11-082023-11-123375.111.1falseemailLilleHDF59000France
C00002n/a58.97falseNorth Americajason_richards23@gmail.comJasonRichards2021-02-162024-04-19229.49blankfalsesmsDenverCO80205USA
C00003churned3249.5trueAsian.murakami@outlook.jpNaokoMurakami2017-08-272023-04-0229112.0525.7trueemailNagoyaAichi460-0008Japan
C00004n/a0falseEuropel.martens@telenet.beLotteMartens2023-03-12blank00blankfalsenoneGentOVL9000Belgium
C00005n/a23.45falseAsiaduy.nguyen2000@gmail.comDuyNguyen2020-07-142024-02-18123.45blankfalsesmsHanoiblank100000Vietnam
C00006inactivity963.2falseAfricasamira_abdi@orange.ciSamiraAbdi2019-05-102023-02-221468.833.2truepostalAbidjanblank22501Ivory Coast
C00007n/a6450.75falseEuropetomislav.kovac23@inet.hrTomislavKovač2016-10-312024-05-018476.815.5falseemailSplitSplitsko21000Croatia
C00008n/a71.25falseSouth Americaluciana.salazar@outlook.com.arLucianaSalazar2022-08-202023-12-15323.7561.5falseemailBuenos AiresCABAC1007Argentina

What the 120 rows show

from the 120-row sample

Churned (lapse reason) stands out: 12 of its 13 rows have winback_campaign_eligible = true, against 11 of 107 for the rest.

  • 19%winback_campaign_eligible = true
  • 85.7median total_spent
  • 5preferred channels
  • 3median total_purchases
  • 32.3median average_purchase_value
  • 43.2median average_days_between_purchases
Winback campaign eligible rate by lapse_reasonwinback_campaign_eligible = true
0%50%100%0%inactivi…0 of 1692%churned12 of 1365%unknown11 of 170%n/a0 of 74

Churned (lapse reason)'s mean total_spent is 3,908, against 97.9 for inactivity, 586.5 for unknown and 1,748 for n/a.

total_spent120 rows, in bands of 2,000
050100921443221011010k20ktotal_spent →

Median 85.7, from 0.0 to 18,523.

region120 rows · 7 values
  1. Europe56
  2. Asia15
  3. Africa13
  4. North America12
  5. South America12
  6. Oceania7
  7. Central America5
19 columns by typefrom the column list below
  • string 11
  • integer 1
  • float 3
  • date 2
  • boolean 2

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 11 columns
customer_idstringUnique identifier for each customeruniqueC00001
emailstringCustomer's email addressuniquen.murakami@outlook.jp
first_namestringCustomer's first nameMarina
last_namestringCustomer's last nameDupont
lapse_reasonstringReason for lapse if customer is lapsed (e.g., inactivity, churned, etc.)inactivity · churned · unknown · n/a · optionaln/a
preferred_channelstringCustomer's preferred marketing communication channelemail · sms · phone · postal · none · optionalemail
regionstringGeographic region associated with the customer7 regions · optionalEurope
citystringCity of the customeroptionalLille
statestringState or province of the customeroptionalHDF
postal_codestringPostal or ZIP code of the customeroptional59000
countrystringCountry of the customeroptionalFrance
Numbers 4 columns
total_purchasesintegerTotal number of purchases made by the customer0 or more33
total_spentfloatTotal amount spent by the customer across all purchases0 or more2478.33
average_purchase_valuefloatAverage value of a single purchase by the customer0 or more · optional75.1
average_days_between_purchasesfloatAverage number of days between purchases for this customer0 or more · optional11.1
Dates and times 2 columns
signup_datedateDate the customer first registered or made a purchase2022-11-08
last_purchase_datedateDate of the customer's most recent purchaseoptional2023-11-12
True or false 2 columns
is_lapsedbooleanIndicates if the customer is considered lapsed based on business criteriafalse
winback_campaign_eligiblebooleanIndicates if the customer is eligible for a win-back marketing campaignfalse

Use it for

  • winback campai…19%23 of 120 rowsmean total spent by l…97.9inac…3.9kchur…586.5unkn…1.7kn/a

    A marketing dashboard

    The winback_campaign_eligible rate, total_spent by lapse_reason and a breakdown of region. Excel, Power BI or Tableau.

  • Why do 23 of 120 rows have winback_campaign_eligible = true?

    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 customers with email, first_name and last_name to fill a screen in front of a buyer.

Not quite right?

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This dataset120 rows19 columns
Yours1,000 rows19 columnsregion: UK only

blueprint · customer-purchase-frequency-patterns

Behind this dataset

Same schema. As many rows as you need.

These 120 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
  • Each customer has a unique ID
  • Purchase frequency calculated per month
  • Flag customers inactive for 90+ days
  • Calculate average order value for active customers
  • Segment customers by purchase frequency
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
customer-purchase-frequency-patterns

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