ECommerce Case Study4
WHAT COMES NEXT DataVines is now building a next-best-action recommendation engine for the brand's retention flows — using each customer's purchase history, product affinity, email engagement pattern, and current retention segment to generate a personalised recommended action (product recommendation, loyalty offer, subscription pause, or win-back incentive) for each individual customer, triggered automatically by behavioural events rather than sent on a fixed calendar. The goal is to make the retention programme respond to customer behaviour as it happens, not on a schedule built around when it is convenient to send. CASE STUDY 04 · E-COMMERCE · MULTI-BRAND RETAIL GROUP · EXECUTIVE KPI & DATA PIPELINE AUTOMATION Three Brands, Seven Platforms, One Person Spending Her Mornings in Spreadsheets. Every Day. A multi-brand e-commerce group operating three DTC storefronts across fashion, home décor, and beauty had a full-time analyst whose primary job had become producing daily reports manually. DataVines automated the entire data infrastructure — and gave the analyst her days back for the work that actually required a human.
The Challenge
Every weekday morning, the lead analyst would begin at 8 AM. She would log into each brand's Shopify admin and pull the overnight orders report — three logins, three exports. She would log into Meta Business Manager for each brand — three more logins, three ad account exports. She would pull
the Google Ads reports for each brand. She would download the Klaviyo daily summary for each email programme. She would open the 3PL's web portal and pull the fulfilment status report. By 1 PM, she had seventeen files open in Excel. She would spend the afternoon consolidating them into three brand-level daily dashboards and one group-level summary, then email all four to the executive team. The executive team, receiving the summary at 3 PM, was reviewing data that reflected activity from the prior day. The CEO's first look at how the business was performing on a given day arrived more than 30 hours after that day had started. The problem compounded on Mondays. W eekend data required pulling Friday, Saturday, and Sunday exports for all three brands across all seven platforms. The analyst's Monday morning was consumed entirely by data production — she typically had nothing ready for the executive team until mid-afternoon, meaning strategic decisions about weekend performance and the coming week's media pacing were being made without current data at the moment they were most needed. The daily grind that was consuming the analytics function: • Three brands × seven platforms = 21 separate manual exports every single business day — plus an additional weekend cycle every Monday • Five hours of daily data production consuming the senior analyst's most productive morning hours, leaving no capacity for actual analysis, testing, or strategic work • No cross-brand visibility in real time — the group's investors and CEO were reviewing three separate brand reports, never a unified group view • No early warning system for underperforming days — if one brand's ad spend went off-track or a Klaviyo flow broke, it appeared in the next morning's report, not within the hour • Klaviyo email data never connected to Shopify orders in the consolidated report — the daily summary showed email performance and order performance as two separate sections with no attribution linking them • The entire reporting function dependent on one person — when the analyst was on leave, the CEO received no daily data at all
The DataVine Solution
The Solution
- W e came in with a single design principle: every number the executive team sees should be produced by a pipeline, not a person. The analyst's value was in interpretation, recommendation, and strategic thinking — not in downloading files. Our job was to make the infrastructure do the downloading. Building agreement before building anything • Ran a cross-brand KPI alignment workshop with the CEO, the three brand directors, and the investors' operating partner to establish a single agreed set of group-level and brand-level KPIs — 12 group-level metrics and between 8 and 14 brand-specific metrics, with agreed definitions, time periods, and calculation methodologies documented formally • Established a single data model hierarchy: raw platform data → standardised brand-level tables → consolidated group-level aggregations — ensuring every KPI could be traced from the executive summary back to its source platform record in five minutes Automating the data connections • Built API integrations pulling overnight order data, product performance, and customer data from all three Shopify storefronts; campaign spend, impressions, and conversion data from each brand's Meta Ads and Google Ads accounts; email send, open, click, and conversion data from each brand's Klaviyo instance; and fulfilment status and exception data from each 3PL provider into a centralised Google BigQuery warehouse
- Built Python ETL scripts with full error handling for every integration — if any API call fails, the
- pipeline retries automatically with exponential backoff, logs the specific failure, and sends a Slack
- alert to the data operations channel before 6 AM; no failure ever propagates silently into the
- morning report
- Orchestrated all seven data source pipelines through Apache Airflow with scheduled runs
- beginning at 3:00 AM — every platform's prior day's data loaded into BigQuery before 5 AM, with
- all dbt transformation models run and dashboard data refreshed before 6 AM
- Built a Shopify-to-Klaviyo attribution layer connecting each email conversion event to the specific
- order it influenced — enabling the daily report to show email-attributed orders alongside total
- orders for the first time
- Building the executive reporting layer
- Built a Looker Studio dashboard suite with five views: a group CEO summary (all three brands, all
- three markets on one screen, readable in under 60 seconds), individual brand performance
- dashboards for each of the three brand directors, a paid media performance comparison across
- all six brand-platform combinations, a 3PL fulfilment health tracker, and a weekly investor
- summary
- Built automated daily email reports for the executive team generating from live BigQuery data and
- landing in inboxes at 6:30 AM — before anyone starts their day, covering all three brands' prior
- day performance with variance flags against the prior week and month-to-date plan
- Built a real-time alert layer for critical operational events: any brand's daily order count dropping
- more than 20% below its 14-day average by 10 AM triggers an automatic Slack alert to the brand
- director and CMO; any 3PL fulfilment exception rate exceeding 2% triggers an alert to the
- operations lead; any Klaviyo flow showing zero sends for more than 6 hours triggers an alert to
- the email team
- Built an automated weekly investor performance pack generating on Sunday evenings, covering
- the prior week's group and brand-level KPIs against plan — ready for the Monday morning
- investor call without anyone building it
Operational Impact
On the first morning the system went live, the analyst opened her laptop at 8 AM and checked the dashboard. Every brand, every platform, every KPI — current as of 4 AM. She spent the morning analysing a trend she had noticed in the home décor brand's weekend email conversion rate. It was the first time in 14 months she had used a Monday morning for analysis rather than data production. The CEO's experience changed most visibly. Before go-live, she had been receiving her daily summary at 3 PM on data from 36 hours earlier. After go-live, her phone received a push notification from the dashboard app at 6:30 AM with the overnight performance summary for all three brands. By the time she arrived at the office, she had already identified that the beauty brand had an unusually strong overnight in Australia — and had messaged the brand director to ask whether the Klaviyo flow that had launched the day before was the driver. The real-time alert layer caught something no morning report ever would have: at 10:47 AM on a Thursday, the fashion brand's Klaviyo welcome sequence stopped sending — a platform configuration change had broken the trigger. The alert landed in the email team's Slack channel at 10:52 AM. The flow was restored by 11:30 AM. In the prior system, that failure would have appeared in the next
morning's report as a 24-hour gap in welcome sequence performance — with no new customers receiving the brand's onboarding emails for an entire day.
Daily data production time reduced from 5 hours per analyst per morning to 15 minutes of
dashboard review — across all three brands and all seven platforms
100% of executive KPIs now automated and delivered by 6:30 AM — covering all three brands,
all three markets, and all seven platforms without human intervention
Executive data lag reduced from 30+ hours to under 6 hours — the CEO's morning briefing
reflects overnight activity, not the prior day's
Real-time alert layer catches operational failures within minutes — Klaviyo flow breaks, Meta
spend anomalies, and 3PL exception spikes surfaced immediately, not in the next morning's
report
W eekly investor pack fully automated — generated Sunday evening from live data, zero manual
PowerPoint preparation
Zero data incidents in seven months post-launch — every pipeline failure caught and
auto-recovered before business hours
Seen enough?
Let's build yours.
DataVines is a boutique data analytics company that builds tailored dashboards and automated data
pipelines for e-commerce brands, DTC retailers, marketplace sellers, and multi-brand retail groups
across the US, UK, and globally.
W e don't do retainers without proving value first.
Start with a free 5-day Proof of Concept.
W e build something real with your data. You decide if it's worth continuing.
www.data-vines.com
Mumbai, India · Serving e-commerce clients across North America, Europe & Asia-Pacific
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