Machine Learning and Predictive Analytics for the Modern CFO

Reduce forecasting and budgeting time/effort by up to 80% whilst improving accuracy and consistency using Machine Learning.

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Machine Learning

OVERVIEW

CFOs are using Machine Learning and Predictive Analytics to slash forecasting and budgeting time/effort by up to 80%, whilst improving accuracy and consistency and even facilitating out-of-cycle re-forecasting.

This is being achieved by using historical data for autonomous predictive modelling, executive and SME overlays, and visualisation with adjustments, resulting in consistent forecast scenarios with accurate seasonality and consideration for every important predictive factor.

Predictive models are customised to factor in elements internal and external to your organisation including demographics and global economic factors. We fine-tune predictions with more context than ever before, and accurately forecast for coming periods.

A base forecast is in an always-ready-state for your immediate re-forecasting needs; especially useful in uncertain times.

Data and Factors Considered

  • Sales, Revenues, Margins and Product lifecycles over time.
  • COGS, labour and overheads.
  • Customer data, POS data and membership data.
  • Microeconomic and macroeconomic factors.
  • Internal and external data

Featurisation and Modelling Process of Machine Learning

  • Workshops with SMEs and Executives to understand predictors/features for Machine Learning and Modelling.
  • Create and test predictive model with high level of accuracy.
  • Forecast ahead for budgeting purposes with the flexibility to incorporate Board, Exec. and SME overlays.
Machine Learning

Outcomes

  • Reduce forecasting and budgeting effort/time by up to 80%.
  • Increase forecast accuracy (including seasonality) and drive consistency between departments and budget owners.
  • Predictions on a SKU basis or any level of required detail for maximum forecast transparency and analytics.
  • Ability to overlay Board, Executive targets and KPIs.
  • Ability to overlay SME adjustments and perspectives.
  • Consider every factor in your forecast for increased accuracy.
  • Create multi-currency budgets and forecasts that can be consolidated based on user-managed FX rates.
  • Facilitate out-of-cycle re-forecasting for periods of uncertainty.
Machine Learning

Example of Automated Seasonal Daily Forecast with Growth Uplift

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