Mobile Data Usage Pattern Dataset

This dataset provides detailed, time-segmented records of mobile data, call, and SMS usage for telecom customers, including network type, device, and location context. It enables in-depth analysis of user consumption patterns, peak usage periods, and regional trends, supporting telecom plan optimization, network planning, and customer segmentation.

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

Identifying high-consumption user segments for targeted plan recommendations

Sample rows

preview · 8 of 90 rows · all 14 columns
usage_idstringtime_blockstringcall_minutesfloatis_roamingbooleanlocation_citystringuser_idstringplan_idstringusage_datedatedata_used_mbfloatsms_countintegernetwork_typestringdevice_typestringlocation_statestringlocation_countrystring
USG000108:00-11:5915.8falseSan FranciscoUSR102PLAN_B2024-05-27583.224GsmartphoneCAUSA
USG000216:00-19:590falseLos AngelesUSR104PLAN_A2024-05-271202.515GsmartphoneCAUSA
USG000312:00-15:5923.6falseAustinUSR108PLAN_B2024-05-28322.903GtabletTXUSA
USG000404:00-07:590falseMadisonUSR101PLAN_C2024-05-2848.302GdongleWIUSA
USG000520:00-23:595.3falseSeattleUSR105PLAN_D2024-05-291766.745GsmartphoneWAUSA
USG000600:00-03:590falsePortlandUSR103PLAN_B2024-05-2775.102GotherORUSA
USG000708:00-11:598.1trueVancouverUSR107PLAN_A2024-05-29650.834GsmartphoneBCCanada
USG000816:00-19:590falseNew YorkUSR110PLAN_B2024-05-301095.415GsmartphoneNYUSA

What the 90 rows show

from the 90-row sample

12:00-15:59 (time block) stands out: mean call_minutes is 12.0, against 2.7 for the rest.

  • 12%is_roaming = true
  • 0.0median call_minutes
  • 4network types
  • 4device types
  • 17location countries
  • 21plans
Mean call_minutes by time_block90 rows
0102012.012:00…20 rows0.7616:00…19 rows2.620:00…17 rows4.508:00…15 rows6.204:00…10 rows0.000:00…9 rows
call_minutes90 rows, in bands of 2.5
0306054268726221012.525call_minutes →

Median 0.0, from 0.0 to 23.6.

location_city90 rows · top 10 of 34 values
  1. Seattle7
  2. Vancouver6
  3. Austin5
  4. Madison5
  5. New York5
  6. Denver5
  7. San Francisco4
  8. Los Angeles4
  9. Portland4
  10. Dallas4
14 columns by typefrom the column list below
  • string 9
  • integer 1
  • float 2
  • date 1
  • boolean 1

Columns

14 columns in four groups
blueprint · 14 columns
columntypedescriptionexample
Text 9 columns
usage_idstringUnique identifier for each mobile data usage recorduniqueUSG0001
user_idstringUnique identifier for the mobile userUSR102
plan_idstringIdentifier for the telecom plan subscribed by the user at the time of usagePLAN_B
time_blockstringTime block during which data usage was recorded (e.g., '00:00-03:59', '04:00-07:59')6 blocks08:00-11:59
network_typestringType of mobile network used (e.g., '4G', '5G', '3G')2G · 3G · 4G · 5G4G
device_typestringType of device used (e.g., 'smartphone', 'tablet', 'dongle')smartphone · tablet · dongle · other · optionalsmartphone
location_citystringCity where the data usage occurredoptionalSan Francisco
location_statestringState or region where the data usage occurredoptionalCA
location_countrystringCountry where the data usage occurredoptionalUSA
Numbers 3 columns
data_used_mbfloatAmount of mobile data used in megabytes during the time block0 or more583.2
call_minutesfloatTotal voice call minutes used during the time block0 or more · optional15.8
sms_countintegerNumber of SMS messages sent during the time block0 or more · optional2
Dates and times 1 column
usage_datedateDate of the recorded data usage2024-05-27
True or false 1 column
is_roamingbooleanIndicates if the user was roaming during the data usageoptionalfalse

Use it for

  • is roaming12%11 of 90 rowsmean call minutes by …12.012:0…0.7616:0…2.620:0…4.508:0…

    A telecommunications dashboard

    The is_roaming rate, call_minutes by time_block and a breakdown of location_city. Excel, Power BI or Tableau.

  • Why do the 20 12:00-15:59 rows have a mean call_minutes of 12.0?

    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 usages with user_id, plan_id and usage_date 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

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This dataset90 rows14 columns
Yours10,000 rows14 columnslocation_city: UK only

blueprint · mobile-data-usage-pattern-dataset

Behind this dataset

Same schema. As many rows as you need.

These 90 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
  • Usage measured in MB per hour block
  • Top 10% users flagged as heavy users
  • Aggregate usage by weekday/weekend
  • Identify usage spikes exceeding plan limits
  • Breakdown by device type
Rows
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1 credit per row. New accounts start with 25 free credits.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
mobile-data-usage-pattern-dataset

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