Accurate Sales Forecasting for Tech Enterprises: Powered by AI

Enables consistent revenue attainment and boosts sales productivity across business units, geographies and product lines

Sales organizations are constantly exploring avenues to improve sales visibility & transparency, predictability, intervention timing and sales rep enablement. With the rise in CRM adoption and integration of Ai powered analytics, sales organizations have started warming up to the potential of algorithm driven real-time insights.

BRIDGEi2i’s Sales Decision Engine is an AI-powered analytics engine that integrates with your business systems to provide accurate real time sales forecasts and help mitigate current and future state pipeline risk.

Predictability

Improves predictability of current and future state pipeline risk

Intervention

Identifies at risk teams – geographies, business units and product lines

Enablement

Recommends strategy to mitigate current and future state risk

Adoption

An AI engine that embeds within existing processes and systems

Sales leaders

Early visibility into pipeline risk & Strategies to mitigate pipeline risk

Sales operations

Reduced time to identify risk & Automated analysis provided winning strategies

Sales reps

Deal prioritization – upsides, commit risks, slippages

Leverage the power of AI to mitigate current and future state pipeline risk

SDE is an analytics engine that embeds within your current business processes and systems

Predicted landing models

Dynamically segments your SKU-Location portfolio into a large number of micro-segments – each having homogeneous behavior in terms of service levels, volumes, and demand volatility

Dynamically segments your SKU-Location portfolio into a large number of micro-segments – each having homogeneous behavior in terms of service levels, volumes, and demand volatility

Opportunity scoring models

Consumes leading indicators of demand such as promotions, events and macro-economic factors to help machine intelligence learn causality

Opportunity classification

12 conventional statistical models develop an accuracy and bias baseline for more advanced models to beat

Key driver models

4 families of ML Models predict demand based on meaningful and interpretable causal effects. An Ensemble Model then weighs each ML Model on accuracy and builds the final forecast

Pipeline coverage models

Planners can visualize forecast accuracy metrics and the value-add over baseline models along with a custom list of metrics and charts. Planners can also simulate various demand scenarios and study the effect on Inventory Levels and Buffers.

A Fortune 500 enterprise virtualization company used SDE to transform sales: 19% four quarter MAPE across business units, geographies and product lines | 30% increase in identification of at-risk teams |
5000+ forecasts generated

 

Why partner with BRIDGEi2i?

Our obsession with “value to customer” and “time to value” has shaped our engagement philosophy and we partner with our clients to make the journey from Information to Insight to Impact easier, faster, and sustainable. We leverage our proven problem-solving frameworks, extensive domain expertise, data science capabilities and proprietary AI accelerators to help companies accelerate their transformation journeys and build digital enterprises of the future.

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