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Forecasting

Sales forecasting predicts future revenue by analyzing opportunity pipelines, win probabilities, historical patterns, and account characteristics. This use case enables organizations to generate accurate revenue forecasts across different scenarios (commit, best case, worst case), aggregate forecasts by territory and product, and improve forecast accuracy by incorporating relationship analysis, account insights, and market factors.

The Challenge

Organizations face significant challenges in sales forecasting:

  • Forecast accuracy — Accurately predicting revenue based on pipeline data
  • Probability assessment — Determining accurate win probabilities for opportunities
  • Multiple forecast types — Managing different forecast types (commit, best case, worst case)
  • Territory aggregation — Aggregating forecasts across teams and territories
  • Market factors — Accounting for market conditions and external factors in forecasts
  • Historical patterns — Using historical data to improve forecast accuracy
  • B2B complexity — B2B sales cycles are longer and more complex, making forecasting more challenging

Traditional forecasting systems rely on simple probability calculations and lack the integrated view needed for comprehensive forecasting.

Why EKG is Required

Enterprise Knowledge Graphs provide powerful forecasting capabilities:

  • Integrated data view — Connect pipeline data with account, legal entity, and historical performance data for comprehensive forecasting
  • Pattern recognition — Use graph analysis to identify patterns in historical wins/losses and pipeline progression
  • Relationship analysis — Understand stakeholder relationships and influence that may affect forecast accuracy
  • Account analysis — Analyze account characteristics and relationships to improve probability assessment
  • Multi-dimensional forecasting — Forecast across multiple dimensions (territory, product, account type) using graph queries
  • Scenario planning — Model different forecast scenarios using graph relationships
  • Predictive analytics — Use graph relationships and patterns to improve forecast accuracy

Business Value

  • Improved planning — More accurate forecasts enable better business planning and resource allocation
  • Financial predictability — Accurate forecasting improves financial predictability
  • Risk management — Understanding forecast scenarios enables risk management
  • Resource optimization — Better forecasting enables optimal resource allocation
  • Stakeholder confidence — Accurate forecasts build stakeholder confidence

  • Opportunity Management - Forecasting depends on Opportunity Management for pipeline data and opportunity information.

  • Sales Pipeline - Forecasting uses Sales Pipeline data for revenue projections.

  • Account Management - Forecasting leverages Account Management for understanding account characteristics that influence forecast accuracy.

  • Legal Entity Management - For B2B forecasting, Legal Entity Management provides essential information about entity structures and financial information that may influence forecast accuracy.