Unified Sales Advisor System for Opportunity Win Prediction
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Solution Overview
Problem
Current sales journey tools lack the ability to support organizations effectively from onboarding opportunities to generating pitches, often leading to decisions based on emotions rather than facts, and there is no single tool that can intelligently assist in winning business opportunities by aggregating relevant data and insights.
Innovation Solution
A data-driven intelligent cloud advisor that accesses and analyzes data associated with an opportunity, using interconnected machine learning-trained classifiers to generate proposals, providing insights, and recommendations to enhance the chances of winning by centralizing information, leveraging global knowledge, and identifying competitors' strengths and weaknesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate tools are used to support different stages of the sales journey, then specific sales functions can be addressed, but the overall sales process cannot be intelligently supported end-to-end and decisions remain emotion-driven rather than fact-driven
Solution Approach 1:
The patent combines multiple separate sales support tools into a single unified sales journey platform that covers the entire sales process from opportunity identification to closing. This single platform integrates data aggregation, analysis, proposal generation, and decision support functions that were previously distributed across multiple tools, thereby achieving end-to-end sales journey support while reducing the number of separate tools needed.
Solution Approach 2:
The unified sales journey platform performs multiple functions across different sales stages including opportunity management, competitor analysis, proposal generation, and decision support. This multi-functional system replaces the need for specialized separate tools for each sales function, providing versatile support throughout the entire sales journey while maintaining comprehensive capabilities.
2Reliability
If comprehensive data aggregation and analysis are performed to generate intelligent insights, then win probability increases, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the comprehensive data aggregation and analysis system into modular functional components including data collection modules, data processing modules, analysis modules, and output generation modules. Each module handles specific aspects of the data pipeline, making the overall complex system more manageable and maintainable while still achieving comprehensive data-driven insights for reliable decision-making.
Solution Approach 2:
The system introduces intermediary components such as data normalization layers, standardized data models, and intermediate processing stages that bridge the gap between raw diverse data sources and the final analysis outputs. These intermediaries simplify the complexity of handling heterogeneous data by providing standardized interfaces and processing routines, thereby reducing overall system complexity while maintaining comprehensive analytical capabilities.
3Loss of information
If extensive user interaction is required to gather all relevant data for an opportunity, then data completeness improves, but the time required to generate proposals and insights increases
Solution Approach 1:
The system performs preliminary actions by proactively gathering and pre-processing data from multiple sources before the user actually needs to make a decision or generate a proposal. This includes automated data collection, initial data validation, and pre-computation of relevant insights, so that when the user needs to act, the data is already prepared and available, reducing both the interaction burden and the time to generate comprehensive proposals.
Solution Approach 2:
The system implements self-service capabilities by automatically gathering data from various sources, performing analysis, and generating proposals with minimal user intervention. The system autonomously executes data collection tasks, processes information through analytical models, and produces output documents, thereby maintaining data completeness while significantly reducing the time and user effort required compared to manual data gathering and proposal preparation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity. The server provides the win percentage and the plurality of data insights for output.