Make decisions based on data — not based on gut feeling
Data-driven forecasts for better decisions
Machine learning models for revenue forecasting, demand planning, customer churn, and quality assurance — based on your own company data.
Anonymized Reference CaseMid-market food retailer · Rhine-Main region, 180 employees+
Initial Situation
Seasonal demand fluctuations regularly led to overstock or shortages — planning manager estimated order quantities manually based on experience.
Solution & Result
ML forecasting model combining weather data, holidays, promotions, and historical sales data — output as weekly order recommendation in the existing ERP system.
23% less overstock, 18% fewer shortages, €140,000 in storage costs saved in the first year.
Important decisions are made based on reports that were already outdated yesterday
- Excel forecasts are based on experience and historical values — but not on the 50 variables that truly influence your demand
- Customer churn is only noticed when the order does not arrive — not when the first warning signals appear
- Maintenance intervals are fixed even though actual wear varies greatly depending on utilization
ML models that know your data and tell you what happens next
- Forecasting models learn from all available data points — CRM, ERP, sensors, external data — and get better over time
- Churn scores for every customer updated daily: your sales team knows which customers need attention before they leave
- Output directly into your existing dashboards and systems — no new software your team has to learn
Scope of Services
What Predictive Analytics does for you
Revenue Forecasting
Precise predictions of revenue, sales, and demand for better planning and inventory management.
Avg. 15% better planning accuracy
Churn Prediction
Early detection of at-risk customers for proactive customer retention measures.
Early warning 30 days before churn
Predictive Maintenance
Prediction of machine maintenance needs based on sensor data — fewer downtimes.
Avg. 30% fewer unplanned outages
Segmentation
Automatic customer segmentation for personalized marketing and sales strategies.
Personalization at the push of a button
Anomaly Detection
Automatic detection of outliers in process data, financial transactions, and quality metrics.
Anomalies detected in real time
Management Reports
Understandable dashboards and reports — forecasts and KPIs at a glance.
Integrated in Power BI & SAP
Our Approach
How we work
Data Analysis
Assessment of available data quality and quantity as the basis for forecasting models.
Model Development
Training and validation of ML models with your historical data.
Integration
Embedding models into existing systems and dashboards for automated forecasts.
Monitoring & Improvement
Ongoing monitoring of model quality and retraining when data patterns change.
The ML model completely changed our ordering process. We now order what we actually need — not what we assume. The savings in the first year have paid for the project many times over.
Use Cases by Industry
Predictive analytics works wherever historical data exists and decisions are made repeatedly.
Manufacturing & Industry
- → Predictive maintenance: predict maintenance before the machine fails
- → Quality assurance: detect defects before delivery
- → Production planning: optimally utilize capacities
Retail & E-Commerce
- → Demand forecasting: order the right quantity at the right time
- → Churn prediction: identify at-risk customers early
- → Personalization: product recommendations based on purchase patterns
Services & B2B
- → Pipeline forecasting: which leads will become customers?
- → Resource planning: predict team and capacity utilization
- → Payment defaults: risk assessment before accepting orders
Our Proof-of-Concept Approach
Frequently Asked Questions
Everything you need to know about Predictive Analytics at a glance.
01How much data do we need for Predictive Analytics to work?+
As a rule of thumb: at least 12–24 months of historical data for forecasting purposes. In the data assessment we check your specific situation — sometimes less data is sufficient for first useful models. We give you an honest assessment before we begin.
02Do we have to buy new software?+
No. We integrate the models into your existing infrastructure — Power BI, Excel, SAP, your CRM. The dashboard looks like your other reports. No new platform, no new license.
03How accurate are the forecasts?+
That depends on data availability and the forecast horizon. In practice we typically achieve 85–95% accuracy for 4-week revenue forecasts. More importantly: we show you the accuracy on your own data in the proof-of-concept — before you commission the full project.
04Does the data stay in Germany?+
Completely. Training, inference, and data storage on German servers. On request, fully on-premise in your own infrastructure — no data leaves your network.
05What does Predictive Analytics cost?+
Proof-of-concept (one model, your data, clear accuracy measurement): from €4,500. Full analytics project with 3–5 models and dashboard integration: typically €18,000–55,000. Ongoing model maintenance and retraining: from €800/month.
06What happens if the data deteriorates or the business model changes?+
Models are continuously monitored for quality drift. When deviations occur you automatically receive a warning. The maintenance contract includes quarterly retraining — the models stay current even when your market changes.
Free Assessment Workshop — no commitment
In 60 minutes we analyze your current situation and show you concretely which solution makes sense for your business — with a binding offer within 5 business days.