Planning blind is expensive
Next month's budget shouldn't be a guess — it should be a calculation.
typical revenue forecast error without predictive analytics
on average, businesses discover a problem — when it's already too late to react
of customers leave silently — without churn prediction, you lose them for good
How predictive analytics works
Machine learning on your data — forecasts are updated daily
Historical analysis
AI analyzes 6-12 months of your data: seasonality, trends, anomalies, and correlations between channels.
Three scenarios
Optimistic, baseline, and pessimistic forecasts for 30, 60, and 90 days. External factors are accounted for.
Churn risk
The model scores each customer's likelihood of churning. An at-risk list with retention recommendations.
Recommendations
Specific actions: increase Direct budget by 20%, launch a retention campaign for segment X.
What predictive analytics delivers
Who needs AI forecasting
For businesses with 6+ months of data and 100+ transactions/month
E-commerce
Sales forecasts by category, seasonality patterns, optimal inventory. Churn model for subscribers.
SaaS
MRR forecasting, churn prediction — when a customer will leave and what to do about it.
Services
Next month's capacity, demand forecasting, optimal ad spend allocation.