The Challenge
The client, a multi-national beverage brand, was flying blind. Online ad spend on Meta and TikTok was increasing, but they couldn't correlate it to offline sales in supermarkets like Tesco and Carrefour.
- ✕ Retail sales data arrived 3 weeks late.
- ✕ Marketing teams operated in silos (Social vs. Search).
- ✕ High stockouts during promo periods resulted in lost revenue.
The Solution
DATALIFT implemented a unified "Data Lakehouse" architecture. We ingested real-time data from ad platforms and weekly data from retailers into BigQuery, applying Machine Learning to bridge the gap.
- ✓ Unified View: All channels in one Looker Studio dashboard.
- ✓ Predictive AI: Demand forecasting model (Prophet) to predict stock needs.
- ✓ Auto-Action: Alerts sent to supply chain managers via Slack.
The Data Refinery
How we transformed raw chaos into a self-driving business engine.
Cross-Channel Intelligence
"How do we reach the right audience and reduce wasted ad spend?"
Audience Intelligence Engine
We cluster customers into live segments (Value Shoppers, Brand Loyalists, Health-Conscious) and calculate specific ROAS for each using First-Party Data.
Media Mix Modelling (MMM)
Predicting how offline sales are driven by online spend.
Insight: "Every £1 increase in TikTok spend generates £1.53 in offline supermarket sales."
Converting Interest to Preference
"Why are users dropping off? What do they think of our packaging?"
Web & App Intelligence
Deep GA4 + BigQuery tracking. We create micro-conversions like "Checked Nutrition", "Compared Flavors", or "Scanned QR Code".
Sentiment Analysis
Aggregating reviews from Amazon, Noon, Carrefour. Identifying pain points like "Packaging too hard to open" instantly.
Unified Retail & eCommerce
"Connecting the dots between online ads and supermarket shelves."
Offline Sales Attribution
We correlate regional ad spend, foot traffic, and weather data to build a model showing "Ads → Store Visits → Sales".
Demand Forecasting
Using LSTM/Prophet models to predict SKU demand 90 days out, accounting for seasonality (e.g. Ramadan, Summer).
Retention Intelligence
Retention & Loyalty
"Predicting who will leave and who will buy again."
Predictive Churn Models
We model when a customer is likely to switch brands and trigger an automatic intervention.
Automated Personalization
Triggers via Email/SMS/WhatsApp based on low stock predictions or seasonal offers.
Turning Customers into Promoters
Automating the reputation management loop.
Review Automation
Post-purchase triggers asking for reviews on Google/Amazon only from satisfied customers (NPS > 8).
Promoter Identification
Classifying users as Promoters, Passives, or Detractors based on repeat frequency and basket size.
Real-Time Listening
Tracking TikTok hashtags and Reddit threads. Negative sentiment triggers instant Slack alerts for PR teams.