Statistical revenue forecasting, demand planning, ML predictive models, and scenario analysis — giving your leadership team the foresight to make confident, data-backed planning decisions.
Statistical revenue forecasting using historical sales data, seasonal patterns, market trends, and leading indicators — giving your leadership team reliable, confidence-interval-backed revenue predictions for planning.
Product demand forecasting using sales velocity, seasonal trends, and external factors — enabling accurate inventory planning that reduces stockouts and overstock simultaneously.
Machine learning predictive models — churn prediction, lead scoring, price elasticity, customer lifetime value — built on your historical data and validated for real predictive accuracy before deployment.
External market trend analysis — competitive landscape movements, consumer behaviour shifts, industry growth indicators, and regulatory environment changes — contextualising your business data in the broader market.
Multi-scenario financial models — base, best, and worst case — stress-testing your business plan against realistic assumptions and helping leadership understand which variables have the most impact on outcomes.
Live forecasting dashboards that automatically update predictions as new data arrives — giving your team always-current revenue forecasts and leading indicator dashboards without manual model re-running.
A statistical modelling specialist who has built revenue forecasting models for businesses across retail, SaaS, and financial services. Aisha's models consistently achieve 90%+ accuracy and her detailed validation methodology gives clients the confidence to stake planning decisions on her outputs.



"We were planning budgets based on gut feel and last year's numbers. Protechplanner built a revenue forecasting model using 3 years of sales data, seasonal patterns, and our sales pipeline data. The Q3 forecast was within 4% of actual revenue. That level of accuracy transformed how our board plans capital allocation."
"Stockouts were costing us ₹8L per month in lost sales during peak season. Protechplanner's demand forecasting model — trained on 2 years of sales data with seasonal decomposition — reduced stockouts by 60% while actually reducing total inventory value by 15%. The model paid for itself in the first month."
"We needed to predict which trial users would churn before their first paid renewal. Protechplanner built a churn prediction model with 84% accuracy — meaning we contact at-risk users 2 weeks before they leave. Our trial-to-paid conversion improved 19% in 3 months just from targeted intervention."
Questions about forecast accuracy, data requirements, or model types?
Message a Forecasting ExpertOur forecasting specialists will build models that give your leadership team the confidence to plan, allocate, and invest with clarity rather than guesswork.