Predictive Analysis
& Forecasting AI
Stop driving while looking in the rearview mirror. Our DigitalMirror Forecast protocol transforms your historical data into revenue and inventory forecasts with >90% accuracy.
What is Predictive Analytics?
Predictive Analytics is the branch of Business Intelligence that uses historical data, statistical algorithms and Machine Learning to identify the probability of future outcomes. Unlike descriptive analysis (which explains the past), Digital Mirror's predictive approach allows you to anticipate customer churn, predict peak demand and optimize company cash flow.
Stacks Scientific
The technologies that power the DigitalMirror Forecast.
Python
The standard language for Data Science
Prophet
Temporal Forecasting Models (Facebook)
Scikit-learn
Classic and robust machine learning
Data Viz
Interactive and clear visualizations
Evolution Analyses
Descriptive
It tells you what happened yesterday.
Static
Manual Excel reports that get old quickly.
Responsive
Act only when the problem has already exploded.
Prescriptive
It tells you what will happen tomorrow.
Live
Dashboards connected to real-time data.
Proactive
The system intervenes before the margin drops.
The Method DigitalMirror Forecast
We transform a service into a proprietary product. It's not just analytics, it's a predictive growth engine.
1. Data Lake Unification: We unify fragmented data from CRM (Salesforce/HubSpot), ERP, Google Analytics, and Excel files into a single, clean, normalized source of truth.
2. Custom Model Training: We don't use generic models. We train specific algorithms (based on Meta Prophet or ARIMA) on your seasonality and industry trends.
3. Deployment & Alerting: The model not only produces graphs, but actions. Receive automatic alerts (e.g. "Stock-out risk in 7 days") directly on Slack, Email or Management.
The future is already in your data.
Don't wait for it to happen. Predict it.
Request Feasibility StudyFrequently Asked Questions
Q.What is the difference between Business Intelligence and Predictive Analysis?
A.Business Intelligence looks to the past (reporting). Predictive Analysis looks to the future (forecasting). We use the first to understand the 'why', and the second to decide 'what to do now' to maximize profit.
Q.Do you need a lot of historical data?
A.For precise models, ideally you need at least 12-24 months of history. However, we can start with simpler models and refine them over time as we collect new data.
Q.Is it safe for my data?
A.Absolutely. The data is anonymized and processed on secure, GDPR compliant servers. They are never used to train other customers' models.