Make decisions based on data — not on gut feeling
Data-Driven Forecasts for Better Decisions
Machine learning models for revenue forecasts, demand planning, customer churn and quality assurance — based on your own company data.
Anonymized reference caseMid-sized food retailer · Rhine-Main region, 180 employees+
Starting point
Seasonal fluctuations in demand regularly led to overstock or shortages — the planner estimated order quantities manually based on experience.
Solution & result
ML forecasting model combining weather data, public holidays, promotions and historical sales data — output as a weekly order recommendation in the existing ERP system.
23% less overstock, 18% fewer shortages, €140,000 in inventory costs saved in the first year.
Important decisions are made on the basis of reports that were already outdated yesterday
- Excel forecasts are based on experience and past values — but not on the 50 variables that really influence your demand
- Customer churn is only noticed when orders stop coming in — not when the first warning signs appear
- Maintenance intervals are fixed, even though actual wear varies greatly depending on utilization
ML models that know your data and tell you what will happen next
- Forecasting models learn from all available data points — CRM, ERP, sensors, external data — and get better over time
- Churn scores updated daily for every customer: 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 Forecasts
Precise predictions of revenue, sales and demand for better planning and inventory management.
Avg. 15% better planning accuracy
Churn Prediction
Early detection of customers at risk of churning for proactive retention measures.
Early warning 30 days before churn
Predictive Maintenance
Prediction of machine maintenance needs based on sensor data — less downtime.
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
Easy-to-understand dashboards and reports — forecasts and KPIs at a glance.
Integrated into Power BI & SAP
Approach
How we work
Data Analysis
Assessment of existing data quality and quantity as the basis for forecasting models.
Model Development
Training and validation of ML models with your historical data.
Integration
Embedding the models in existing systems and dashboards for automated forecasts.
Monitoring & Improvement
Ongoing monitoring of model quality and retraining when data patterns change.
The ML model has completely changed our order planning. We now order what we actually need — not what we assume. The savings in the first year paid for the project several times over.
Use Cases by Industry
Predictive analytics works wherever historical data exists and decisions are made repeatedly.
Manufacturing & Industry
- → Predictive maintenance: forecast maintenance needs before a machine fails
- → Quality assurance: detect defects before shipment
- → Production planning: make optimal use of capacity
Retail & E-Commerce
- → Demand forecasting: order the right quantity at the right time
- → Churn prediction: identify customers at risk of leaving early
- → Personalization: product recommendations based on purchase patterns
Services & B2B
- → Pipeline forecasting: which leads will become customers?
- → Resource planning: forecast team workload and capacity
- → Payment defaults: risk assessment before accepting an order
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 enough for the first useful models. We give you an honest assessment before we start.
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 the data and the forecast horizon. In practice, we typically achieve 85–95% accuracy for 4-week revenue forecasts. More importantly: in the proof of concept, we show you the accuracy on your own data — 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. Complete 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 gets worse or the business model changes?+
Models are continuously monitored for quality drift. If there are deviations, you automatically receive a warning. The maintenance contract includes quarterly retraining — the models stay current even when your market changes.
Free assessment workshop — no obligation
In 60 minutes, we analyze your current situation and show you exactly which solution makes sense for your business — with a binding quote within 5 business days.