ECommerce Case Study1
DATA-VINES Client Success Series E-COMMERCE & RETAIL Case Studies Four stores. Four data crises. Four transformations. How DataVines helped e-commerce brands, DTC retailers, and marketplace sellers replace gut instinct and disconnected spreadsheets with real-time intelligence — and start making decisions as fast as their customers shop. data-vines.com CASE STUDY 01 · E-COMMERCE · DTC FASHION · MARKETING ROI & CUSTOMER ACQUISITION INTELLIGENCE They Were Spending $480K a Year on Ads. Four Platforms Said Four Different Numbers for the Same Sale. A fast-scaling direct-to-consumer fashion brand was running paid media across Meta, Google, TikTok, and Pinterest — with each platform claiming more conversions than the brand actually made. DataVines built the unified attribution engine that finally showed them which channels were genuinely driving customers, and which were borrowing credit.
The Challenge
Every Monday, the growth team met to review the prior week's performance. The Meta Ads Manager showed ROAS of 4.2x. Google Ads showed ROAS of 3.8x. TikTok showed a cost-per-acquisition of $28. Pinterest showed 340 conversions. W hen the CMO added them up and compared to Shopify's actual order count, the total attributed purchases exceeded real orders by 73 percent. Every platform was using last-click or view-through attribution within its own ecosystem. A customer who saw a TikTok video on Tuesday, clicked a Google Shopping ad on Thursday, opened a Klaviyo email on Saturday, and purchased on Sunday was claimed as a conversion by all four touchpoints simultaneously. The growth team knew this was happening in theory. They had no mechanism to resolve it in practice. The consequence was structural misallocation. Because TikTok's self-reported CPA looked best, budget had been incrementally shifted toward TikTok over the prior two quarters. Because Pinterest's conversion count looked strong, it had been kept active despite the team's private doubts. The channels that had been deprioritised — Google Shopping in particular — had seen budget cuts based on their comparatively modest self-reported numbers. Nobody had the data to know whether those cuts were correct. The attribution blind spots driving wrong decisions: • Four ad platforms, four independent attribution windows, zero deduplication — total reported conversions routinely exceeding actual Shopify orders by 60–80% • Budget allocation decisions made on platform-self-reported ROAS figures that systematically overcounted each channel's true contribution • Influencer and affiliate traffic entirely untracked in the paid media stack — affiliate-driven purchases appearing as "direct" or misattributed to whatever paid touchpoint had appeared in the same session window • Email marketing contribution to first-purchase and repeat-purchase invisible in the paid media dashboard — Klaviyo data existing in a completely separate reporting environment • No cohort-level customer acquisition analysis — the team could not see whether the customers being acquired through each channel had materially different repeat-purchase behaviour or average order values • Post-campaign analysis consuming 12+ hours of the growth team's time weekly — building six separate platform exports, reconciling them by hand, producing a summary deck that everyone knew was methodologically unsound
The DataVine Solution
The Solution
- W e started where the money starts: the Shopify order. Every attribution model we built was anchored to an actual transaction — not a platform-reported conversion event. The principle was simple: if a sale did not appear in Shopify, it did not count, regardless of what any ad platform claimed.
- Building the unified customer and attribution data foundation • Built API integrations pulling campaign spend, impression, click, and platform-reported conversion data from Meta Ads, Google Ads, TikTok for Business, and Pinterest Ads, alongside order-level transaction data from Shopify, email engagement data from Klaviyo, and affiliate/influencer click data from the affiliate tracking platform into a centralised Google BigQuery warehouse • Built a UTM parameter cleaning and standardisation model — resolving the 280+ unique UTM combinations present in the historical Shopify order data into a consistent channel-campaign-creative taxonomy, correcting broken UTM strings and inferring channel from referral domain where UTM data was absent • Built a data-driven multi-touch attribution model using Shapley value methodology — distributing credit for each Shopify order proportionally across all tracked touchpoints in the customer's journey, weighted by their position and the recency of the interaction, producing a single deduplicated attributed-order count per channel • Built a customer master connecting every Shopify buyer's order history, email engagement record, and acquisition channel attribution — enabling cohort analysis by acquisition source for the first time • Built a full 18-month historical backfill, reconstructing attributed customer journeys from available UTM, referral, and email data across the prior six quarters Building the marketing intelligence dashboard • Built a Looker Studio dashboard suite with four views: a CMO weekly performance summary with deduplicated ROAS and CPA by channel, a campaign-level creative performance breakdown, a cohort analysis view showing 30/60/90-day repeat purchase rates by acquisition channel, and a weekly budget pacing tracker showing actual spend versus plan across all four platforms • Built a channel contribution comparison showing each platform's self-reported conversions alongside the Shapley-attributed conversions — making the over-attribution gap visible for every channel simultaneously, so budget discussions were grounded in the actual delta rather than each team's preferred metric • Built an influencer and affiliate performance tracker connecting affiliate link clicks and promo code redemptions to Shopify orders — surfacing the true first-purchase impact of influencer activations that had previously been invisible in the paid media stack • Configured an automated Monday morning performance briefing landing in the CMO's inbox at 7 AM, covering the prior week's deduplicated ROAS, CPA by channel, top-performing creatives by attributed order count, and the budget pacing position across all platforms
Operational Impact
The first Monday dashboard review after go-live was the first one in the brand's history where the growth team looked at a single ROAS figure they all agreed on. No platform reconciliation. No competing numbers. One deduplicated performance view, anchored to Shopify orders. The Shapley attribution model produced a result that directly contradicted the team's prior two quarters of budget decisions. TikTok's attributed CPA — the metric that had driven its budget increases — was 2.3x higher than its self-reported figure once deduplication was applied. Google Shopping's attributed performance was 40% stronger than its self-reported numbers had suggested, because it was
consistently appearing early in the customer journey for purchases that subsequently completed through channels that claimed last-click credit. $112K was reallocated away from TikTok and Pinterest in the following budget cycle, toward Google Shopping and Klaviyo email flows. Blended ROAS, measured on the deduplicated model, moved from 3.1x to 4.8x over the subsequent 12 weeks — not because the team spent more, but because they stopped over-investing in channels that had been borrowing credit from others.
12+ hours of weekly manual platform reconciliation eliminated — replaced by an automated
Monday morning briefing built from a single, methodology-consistent attribution model
$112K in annual ad spend reallocated from over-attributed channels to demonstrably
high-performing ones, based on deduplicated Shapley attribution
Blended ROAS improved from 3.1x to 4.8x over 12 weeks post-reallocation — without increasing
total ad spend
Four ad platforms unified under one attribution framework — zero conflicting conversion counts in
any weekly review
Influencer and affiliate contribution to first-purchase visible for the first time — enabling
data-driven partnership investment decisions
First-ever cohort analysis by acquisition channel — showing which sources were delivering repeat
buyers versus one-time purchasers
WHAT COMES NEXT
DataVines is now building a creative performance prediction model for the brand — using historical creative attributes (format, colour palette, copy length, offer type, product category) and channel-specific engagement signals to generate a predicted attributed-ROAS forecast for new creative concepts before they go live. The goal is to shift creative briefing from intuition-first to a data-informed process where the team enters production knowing which creative directions have historically outperformed in each channel. CASE STUDY 02 · E-COMMERCE · MARKETPLACE RETAIL · INVENTORY INTELLIGENCE & FULFILMENT ANALYTICS They Were Stocking Out on Their Best SKUs and Sitting on $620K of Dead Inventory — at the Same Time. A high-volume Amazon and W almart marketplace seller across five product categories had inventory data locked inside three separate systems that had never been connected. DataVines built the cross-platform inventory intelligence layer that finally made stockout risk and excess inventory visible in the same place, in real time.
More Success Stories
Explore how we helped other brands in the E-Commerce sector.
ECommerce Case Study2
consistently appearing early in the customer journey for purchases that subsequently completed through channels that claimed last-click credit. $112K was reallocated away from TikTok and Pinterest in the following budget cycle, toward Google Shopping and Klaviyo email flows. Blended ROAS, measure...
ECommerce Case Study3
Python Snowflake dbt Power BI Amazon SP-API · Walmart Seller API 3PL Integration Layer · Procurement API WHAT CHANGED The first Monday after go-live, the operations manager opened the dashboard at 8 AM instead of spending her Friday afternoon in Excel. Every SKU across all thr...