Influencer Product Launch Delay Logs

This dataset provides detailed logs of product delivery delays triggered by influencer-driven campaigns for SME e-commerce brands, capturing key information about brands, influencers, campaigns, affected orders, and customers. It enables supply chain and marketing teams to analyze the causes and patterns of fulfillment disruptions during high-demand social media launches, supporting improved operational planning and customer experience management.

  • last updated 25 Oct 2025
  • by GoMask
The brief that made it

Analyzing delivery delays and their root causes during influencer campaigns

Sample rows

preview · 8 of 58 rows · all 24 columns
delay_idstringdelay_reasonstringdelay_duration_daysintegerresolvedbooleanbrand_idstringbrand_namestringinfluencer_idstringinfluencer_namestringcampaign_idstringcampaign_namestringproduct_idstringproduct_namestringorder_idstringcustomer_idstringorder_datedatetimeexpected_delivery_datedateactual_delivery_datedatedelay_notesstringresolution_datedatesupply_chain_contactstringmarketing_manager_contactstringcustomer_citystringcustomer_statestringcustomer_countrystring
DLG_8hKx3zA1stockout4trueBRND_001GlowifyINF_101@luna_beautyCMP_2024ASpring Glow LaunchPRD_01ARadiant SerumORD_0001CUST_10012024-03-13T10:45:002024-03-182024-03-22Unexpected spike in demand post-campaign.2024-03-23Emily WaltersSamantha LeeLos AngelesCaliforniaUSA
DLG_B2p9rVmQlogistics3trueBRND_002EcoChicINF_102TheGreenGuruCMP_2024BEco Essentials DropPRD_02BReusable Tote BagORD_0002CUST_10022024-01-19T14:10:002024-01-252024-01-28Weather-related transit delay.2024-01-29Carlos MendozaOliver GrantAustinTexasUSA
DLG_7wLkYpE3surge_in_demand7falseBRND_003TechNovaINF_103@techbytomCMP_2024CGadget FrenzyPRD_03CSmartwatch XORD_0003CUST_10032024-02-05T09:32:002024-02-102024-02-17Inventory replenishment required.blankVinay GuptaHarriet StoneTorontoOntarioCanada
DLG_pQ9v6XjNlogistics3trueBRND_001GlowifyINF_104@skin_sophieCMP_2024ASpring Glow LaunchPRD_01BHydra MistORD_0004CUST_10042024-03-15T17:24:002024-03-202024-03-23Carrier missed initial pickup.2024-03-24Emily WaltersSamantha LeeMiamiFloridaUSA
DLG_X5zv8sRpsystem_error2trueBRND_004FitFuelINF_105@fitbyjakeCMP_2024DPower Shake DebutPRD_04AProtein Power ShakeORD_0005CUST_10052024-04-04T12:09:002024-04-092024-04-11Order processing glitch.2024-04-12Jasmine LinDavid ChuVancouverBritish ColumbiaCanada
DLG_9mVwDkT6stockout4trueBRND_005PetPalINF_106@petloverellaCMP_2024EPet Wellness WavePRD_05AOmega TreatsORD_0006CUST_10062024-05-12T15:42:002024-05-172024-05-21Supplier delayed restock.2024-05-22Frank TurnerLisa CohenChicagoIllinoisUSA
DLG_3tNjW8xZlogistics3trueBRND_002EcoChicINF_102TheGreenGuruCMP_2024BEco Essentials DropPRD_02CBiodegradable WrapsORD_0007CUST_10072024-01-21T11:37:002024-01-262024-01-29blank2024-01-30Carlos MendozaOliver GrantDenverColoradoUSA
DLG_Ek2yWvU7surge_in_demand5falseBRND_003TechNovaINF_103@techbytomCMP_2024CGadget FrenzyPRD_03DWireless EarbudsORD_0008CUST_10082024-02-08T18:23:002024-02-132024-02-18Backorder placed for item.blankVinay GuptaHarriet StoneMontrealQuebecCanada

What the 58 rows show

from the 58-row sample

Surge_in_demand (delay reason) stands out: mean delay_duration_days is 4.1, against 2.8 for the rest.

  • 91%resolved = true
  • 3median delay_duration_days
  • 2customer countries
Mean delay_duration_days by delay_reason58 rows
0483.4stocko…13 rows3.2logist…18 rows4.1surge_…12 rows1.9system…8 rows1.7other7 rows
delay_duration_days58 rows, in bands of 1
0918517171153147delay_duration_days →

Median 3, from 1 to 7.

24 columns by typefrom the column list below
  • string 18
  • integer 1
  • date 3
  • datetime 1
  • boolean 1

Columns

24 columns in four groups
blueprint · 24 columns
columntypedescriptionexample
Text 18 columns
delay_idstringUnique identifier for each delay log entryuniqueDLG_8hKx3zA1
brand_idstringUnique identifier for the SME e-commerce brandBRND_001
brand_namestringName of the SME e-commerce brandGlowify
influencer_idstringUnique identifier for the influencer involved in the campaignINF_101
influencer_namestringFull name or handle of the influencer@luna_beauty
campaign_idstringUnique identifier for the influencer campaignCMP_2024A
campaign_namestringName or title of the influencer campaignSpring Glow Launch
product_idstringUnique identifier for the product affected by the delayPRD_01A
product_namestringName of the product affected by the delayRadiant Serum
order_idstringUnique identifier for the affected orderORD_0001
customer_idstringUnique identifier for the customer whose order was delayedCUST_1001
delay_reasonstringPrimary reason for the delay (e.g., stockout, logistics, surge in demand)5 valuesstockout
delay_notesstringAdditional notes or comments about the delayoptionalOrder processing glitch.
supply_chain_contactstringName or identifier of the supply chain team member responsible for managing the delayoptionalEmily Walters
marketing_manager_contactstringName or identifier of the marketing manager overseeing the campaignoptionalSamantha Lee
customer_citystringCity of the customer whose order was delayedoptionalLos Angeles
customer_statestringState or region of the customeroptionalCalifornia
customer_countrystringCountry of the customer2 countries · optionalUSA
Numbers 1 column
delay_duration_daysintegerNumber of days the delivery was delayed1 or more4
Dates and times 4 columns
order_datedatetimeDate and time when the order was placed2024-03-13T10:45:00
expected_delivery_datedateOriginal expected delivery date for the order2024-03-18
actual_delivery_datedateActual date when the order was delivered2024-03-22
resolution_datedateDate when the delay was resolved (if applicable)optional2024-03-23
True or false 1 column
resolvedbooleanIndicates whether the delay issue has been resolvedtrue

Use it for

  • resolved91%53 of 58 rowsmean delay duration d…3.4stoc…3.2logi…4.1surg…1.9syst…

    An e-commerce dashboard

    The resolved rate and delay_duration_days by delay_reason. Excel, Power BI or Tableau.

  • Why do the 12 surge_in_demand rows have a mean delay_duration_days of 4.1?

    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 delays with brand_id, brand_name and influencer_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 24 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 dataset58 rows24 columns
Yours1,000 rows24 columnscustomer_city: UK only

blueprint · influencer-product-launch-delay-logs

Behind this dataset

Same schema. As many rows as you need.

These 58 rows came out of a blueprint — 24 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 row records a unique product launch event linked to an influencer.
  • Delay periods are measured in hours and categorized by severity levels.
  • Only orders affected by social media-driven campaigns are included.
  • Log includes influencing factors: campaign timing, order volume spikes, logistics bottleneck type.
  • Resolution actions and outcomes are recorded for each delay.
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
influencer-product-launch-delay-logs

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