Case Study: CPG & FMCG

CPG FMCG Case Study4

CASE STUDY 04 · CPG / FMCG · DATA ENGINEERING & PIPELINES The Reporting Was a Full-Time Job. It Shouldn't Have Been. A fast-growing household products brand in Texas was spending six hours every morning pulling data from five different systems — manually. DataVines automated the whole thing in eight weeks, and the team hasn't touched a CSV export since.

INDUSTRY

FMCG — Household & Cleaning Products

GEOGRAPHY

United States (Texas-based, nationwide DTC)

ENGAGEMENT

16 Weeks

Key Outcomes

100%

of daily reports now fully

IndustryCPG & FMCG

RegionUnited States

Tech Stack

Python · ApacheAirflow · BigQuery ·dbt · Looker StudioResults at a Glance100%of daily reports now fullyautomated6 hrs →20 mindaily data preparation timedata incidents in first 6months postlaunchsourceintegrated into onewarehouse in 8 weeksAbout the ClientTexas. They'd grown from 4 to 22a 3PL portal for warehousing andfulfillmentExtensiv for inventory managementShipBob for logistics trackingand Meta and Google forthey were a reporting nightmare.What Was Actually Going OnThe brand had a twoperson ops team. Every morningone of them would spend the first six hours ofdownloading the orders CSV. Logging into the 3PLportaldownloading the delivery status file.Opening each file in Excelcopypasting into a master sheetfixing the formatting issues that came withevery exportreconciling the numbers against each otherand finally producing a daily summary tosend to the founders.it was usually noon. The data in it was fromyesterday. And if the ops person was sickor in backtoback meetingsit didn't go out at all.into launchday ordersfulfillment statustheir bestbutfrom a customer complaint on Instagram.The daily grind that was breaking things:built from manual snapshotsFounders operating on a 12 to 24hour data lag at all timesnot internal alertsthe business was blindWhat DataVines Didmonitoredand selfrecovering. Everythe output.Automating the data connectionsreturnscancellations)Extensiv(inventory positions and movements)ShipBob (shipment and delivery status)Meta AdsandGoogle Adsthe pipeline retriesautomatically and alerts the ops lead via Slackit doesn't silently drop data— everything loaded into BigQuery before 6 AMMaking the data trustworthyconsistentbusinessready tablesnull checksreferentialintegrity between orders and fulfillment recordsBuilt a full 18month historical backfillhad a continuousreliable dataset going back to when they launchedsilent errorseverBuilding the reporting layerinventoryfulfillment status)a marketing performance dashboard (ad spendROAScustomeracquisition cost by channel)and a founder summary (topline health metrics)sellthrough velocity and daysonhandwith a 10day forward projection so they can act beforeit becomes a problemTechnology UsedPython ApacheAirflowGoogleBigQuerydbt LookerStudioShopify ·ShipBobAPIsWhat ChangedOn the first morning the system went livethe ops team's sixhour ritual became a 20minute review ofthe automated summary. Within two weeksthe alertsnot producing it.one of them wastravelingSix months after goliverefreshesno silent failuressources. No additional headcount required.processimprovement projectsWhat Comes NextWith the data foundation in placesellthrough velocityseasonal patternsand upcoming campaign schedules to generate SKUlevelnot just faster to catch.Seen enough?Let's build yours.labelexecutivebrands.Start with a free 5day Proof of Concept.www.datavines.comMumbaiIndia · Serving clients across North America

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CPG FMCG Case Study4

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