Data Intelligence · 2024
03
Pulse
Analytics
The brief
See what's coming
before it arrives.
A fast-growing retail group had invested heavily in BI tooling but was still making decisions on last week's data. By the time their dashboards showed a problem, it was already costing them revenue. They needed intelligence that was genuinely predictive — not just a better view of the past.
KIJO built Pulse — an ML-powered analytics layer that sits on top of existing data infrastructure and adds genuine predictive capability. Anomaly detection, forecast models, and proactive alerts — all surfacing through a dashboard their operations team actually wanted to use.
Challenge
Reporting the past,
missing the future.
The existing BI stack was excellent at describing what had already happened. Revenue reports, stock reports, footfall reports — all accurate, all too late. Stock-outs were identified after they'd cost sales. Demand spikes were noticed after the peak had passed. The data existed to have predicted every one of these events — but it wasn't being used predictively.
24-48 hour reporting lag meant operational decisions were always made on stale data — too late to prevent issues.
Unusual patterns in revenue or stock required manual analysis to identify — problems weren't found until they were obvious.
Existing monitoring produced hundreds of low-quality alerts daily — the team had learned to ignore them.
Solution
ML-powered
early warning.
Pulse adds a predictive layer to their existing data stack without replacing it. Three ML models run continuously: demand forecasting, anomaly detection, and supply risk scoring. Each produces a small number of high-confidence, actionable alerts — surfaced through a clean operations dashboard and a daily digest to leadership.
Time-series model predicting demand at SKU level with 92% accuracy — 6 hours before real-time systems would flag it.
Unsupervised model detecting revenue and stock anomalies in real time — with contextual reasoning explaining each alert.
Confidence-weighted alerting reducing noise by 94% — only surfacing anomalies worth acting on, with suggested actions.
Forecast
accuracy
Earlier
warnings
Decision
speed
From brief
to live