Machine Learning Sales Driver Analysis for Proactive Sales Risk
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Solution Overview
Problem
Conventional sales approaches lack agility in predicting risk factors for sales deals, require resource-intensive efforts, and fail to prioritize sales efforts effectively, leading to skewed results and suboptimal SR performance.
Innovation Solution
A machine learning-based framework that leverages insights aggregation to automate strategic initiatives, predict revenue, identify key sales drivers, and provide proactive risk mitigation, using explainable AI to optimize SR tasks and enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to predict risk factors and optimize sales efforts, then sales representative productivity and performance are improved, but device complexity and system requirements increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that mediate between sales representatives and complex sales data. These models process historical sales data, customer information, and market trends to generate predictions and recommendations, reducing the cognitive load on sales representatives while maintaining high system complexity in the background.
Solution Approach 2:
The patent replaces manual analytical processes with automated machine learning models. Instead of sales representatives manually analyzing complex datasets and identifying patterns, the system uses ML algorithms to automatically process data, predict outcomes, and recommend actions, substituting mechanical manual analysis with automated computational processes.
2Measurement precision
If comprehensive data analysis is performed to identify key sales drivers and predict revenue, then measurement precision and prediction accuracy are improved, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing historical sales data in a structured format before actual predictions are needed. The machine learning models are trained in advance on historical data, allowing rapid inference during actual sales operations without requiring time-consuming real-time analysis.
Solution Approach 2:
The patent changes parameters by transforming raw data into feature representations that are optimized for machine learning processing. The system selects and transforms relevant features from comprehensive datasets, focusing computational resources on the most predictive parameters rather than processing all available data equally, thereby improving accuracy while reducing computational time.
3Ease of operation
If automated insights aggregation is implemented to provide actionable recommendations, then ease of operation and decision-making speed are improved, but loss of information and data processing complexity increase
Solution Approach 1:
The patent extracts only the most critical insights and actionable recommendations from comprehensive data analysis. The machine learning models process vast amounts of data but output only the essential information needed for decision-making, such as predicted revenue figures, key sales drivers, and prioritized activities, filtering out redundant information while maintaining decision-making effectiveness.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from actual sales outcomes and adjusts its predictions and recommendations accordingly. This feedback loop ensures that the system refines its information processing over time, reducing information loss by adapting to changing sales patterns and improving the relevance of extracted insights.
Data Source
AI summary
A method for managing a sales representative's (SR) performance includes: obtaining historical sales drivers (HSDs); generating, using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers; obtaining, based on a target parameter, a trained analysis model that is trained using at least the HSDs; obtaining historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs); analyzing the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR; obtaining, based on the target parameter, a trained insights model that is trained using at least the HKSDs, the IPs, and the EPs; notifying an analyzer about the trained insights model; and initiating, by the analyzer, notification of an administrator about the trained analysis model and the trained insights model.