Predictive Analytics
Know What Happens Next Before It Does
We build machine learning models that forecast customer behaviour, demand, churn, and revenue — giving your business the ability to make proactive decisions rather than reactive ones.
85%
Forecast Accuracy Achieved
30%
Reduction in Excess Inventory
25%
Churn Reduction with Intervention
3×
Faster Business Decisions
From Reactive Reporting to Proactive Decision-Making
Predictive analytics shifts your organisation from understanding what happened to anticipating what will happen. Instead of reacting to stockouts, churn, or fraud after they occur, predictive models give you a window of time to intervene — and that window is where competitive advantage lives.
We build custom predictive models on your data — demand forecasting, churn prediction, price optimisation, fraud detection, or any output you need to anticipate. Every model is built for production: validated on held-out data, monitored for drift, and connected to the systems that act on its outputs.
We do not just deliver a model — we deliver a feedback loop. Predictions connect to dashboards, alerts, or automated actions. Business users see the output in terms they act on. And the model is instrumented to tell you when it needs retraining.
- Custom models trained on your historical data — not generic pre-trained assumptions
- Production-grade: validated, monitored, and connected to action systems
- Explainability layer — decision-makers see why the model made each prediction
- Drift monitoring with automated retraining triggers
85%
Forecast Accuracy Achieved
Predictive Analytics Capabilities
A complete suite — from initial workflow mapping through to production deployment and ongoing optimisation.
Demand Forecasting Models
Predict product demand at SKU level with time-series models accounting for seasonality and promotions.
Customer Churn Prediction
Identify customers showing churn signals early and score them for targeted retention campaigns.
Revenue & Sales Forecasting
Pipeline forecasting and revenue prediction models integrated with your CRM and sales data.
Customer Lifetime Value Models
Predict CLV for customer segments to prioritise acquisition spend and retention investment.
Fraud Detection Models
Real-time transaction scoring and anomaly detection to catch fraud before it impacts your business.
Recommendation Engines
Personalised product and content recommendations driven by collaborative filtering and behavioural ML.
Use Cases in Production
Real deployment patterns — the specifics vary per client, but the structure stays the same.
Retail & E-commerceDemand Forecasting for a Seasonal Retailer
A seasonal retail brand with 4,000 SKUs was ordering inventory based on last year's sales — a model that did not account for trend shifts, promotional uplift, or external demand signals. Overstock and stockouts were costing £2.1M annually.
A demand forecasting model integrating historical sales, promotional calendar, web traffic, social trend signals, and weather data. The model produces 12-week rolling forecasts at SKU level, directly feeding the procurement system with suggested order quantities.
- Overstock reduced by 34% in the first trading season
- Stockouts on hero SKUs eliminated during peak period
- £680K inventory cost reduction in year one
SaaSCustomer Churn Prediction Model
A SaaS platform with 8,000 active accounts was losing 4.2% of revenue monthly to churn. The customer success team had no early warning — they only found out a customer was at risk when they submitted a cancellation request.
A churn prediction model using 60-day behavioural signals — login frequency, feature adoption depth, support ticket sentiment, and payment history. At-risk accounts flagged 45 days before predicted churn with a recommended intervention playbook.
- CS team intervened on 340 flagged accounts — 61% retained
- Monthly churn rate reduced from 4.2% to 2.7% in 6 months
- Model ROI: £1.8M ARR protected in the first year
FinanceTransaction Fraud Detection System
A payment processing company was losing £180K per month to fraudulent transactions. Their rules-based system had too many false positives (blocking legitimate customers) and too many false negatives (missing novel fraud patterns).
A machine learning fraud detection model trained on 3 years of transaction history, incorporating device fingerprinting, behavioural biometrics, network graph signals, and velocity features. Real-time scoring at the point of transaction with auto-decline above threshold.
- Fraud losses reduced by 73% in the first quarter
- False positive rate down 41% — fewer legitimate customers blocked
- Model processes 50,000 transactions per minute with <20ms latency
Why Choose Us for Predictive Analytics
Accurate Revenue Forecasting
ML models that predict sales, demand, and revenue with measurable accuracy — replacing gut-feel planning.
Customer Churn Prevention
Identify at-risk customers before they leave and trigger targeted retention interventions automatically.
Demand & Inventory Optimisation
Reduce excess stock and stockouts with demand forecasting models tuned to your business seasonality.
Actionable, Not Just Analytical
Predictions are connected to actions — models feed directly into your CRM, ERP, or marketing automation.
Our Delivery Process
A structured path from business process discovery through to live deployment and continuous improvement.
Problem & Data Definition
Define the prediction target, identify data sources, and assess data quality and volume requirements.
Model Development & POC
Build baseline models, evaluate accuracy, and validate on held-out data before production investment.
Production Build
Full model engineering, feature pipeline build, API development, and integration with your systems.
Monitor & Retrain
Ongoing accuracy monitoring, drift detection, scheduled retraining, and model performance reporting.
Industries We Serve
Technologies We Use
Industry-proven tools chosen for performance, reliability, and long-term support.
Python
TensorFlow
AWS
DockerFrequently Asked Questions
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