Stochastic Timeline Recommendation System Using Matrix Factorization
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Solution Overview
Problem
Existing CRM systems provide static, generalized advice based on 'Accepted Best Practices' that do not account for individual differences in selling style or client type, limiting their effectiveness in optimizing sales outcomes.
Innovation Solution
A computer-implemented method that collects and processes data on historic and current opportunities to generate static and dynamic variables, using matrix factorization to predict actions for achieving milestones in ongoing opportunities, thereby providing personalized recommendations for future actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static advice based on accepted best practices is used, then implementation simplicity is improved, but recommendation accuracy and relevance deteriorate
Solution Approach 1:
The system transitions from static, pre-defined best practices to dynamic, data-driven recommendations that adapt to individual sales opportunities. Machine learning models continuously learn from historical data and update recommendations in real-time, allowing the system to evolve and personalize advice based on actual performance patterns rather than generic guidelines.
Solution Approach 2:
The system changes the parameters of recommendation generation by moving from fixed, manually-curated best practices to variable, algorithmically-generated insights. Multiple features and variables are analyzed and weighted dynamically based on their predictive power, allowing the system to adjust recommendation parameters based on the specific context of each opportunity.
2Device complexity
If generalized best practices are applied, then system complexity is reduced, but individualization and adaptability worsen
Solution Approach 1:
The system performs self-learning and self-adjustment by automatically analyzing historical sales data, identifying successful patterns, and generating personalized recommendations without requiring manual configuration for each salesperson or opportunity. The machine learning models continuously improve their understanding of individual selling styles and client types through automated data processing.
Solution Approach 2:
The system segments sales opportunities into distinct categories based on multiple features such as client type, selling style, industry, and deal characteristics. This segmentation enables the system to provide tailored recommendations for different segments rather than applying one-size-fits-all advice, while the underlying infrastructure remains unified and automated.
3Loss of information
If manual data gathering and analysis is used, then data processing depth is improved, but processing speed and automation deteriorate
Solution Approach 1:
The system replaces manual data gathering and analysis with automated machine learning pipelines that continuously process historical sales data. Algorithms automatically extract features, identify patterns, and generate insights without human intervention, maintaining deep analytical capabilities while achieving full automation. The system processes structured and unstructured data from multiple sources automatically.
4Stability of the object's composition
If static feature vectors are generated for all opportunities, then processing consistency is improved, but computational efficiency for dynamic predictions worsens
Solution Approach 1:
The system performs preliminary processing by generating static feature vectors that capture invariant properties of sales opportunities upfront. These pre-computed features provide a consistent foundation for analysis, while dynamic features and interactions are computed only when needed for specific predictions, optimizing the balance between consistency and efficiency.
Solution Approach 2:
The system merges static feature vectors with dynamic interactions and contextual information to create comprehensive prediction models. By combining pre-computed static features with real-time dynamic data, the system achieves both processing consistency from the static component and prediction efficiency through selective dynamic computation.
Data Source
AI summary
System and method comprising: collecting, using automated data collection, data about a plurality of opportunities that include historic opportunities and at least one current opportunity; performing matrix factorization to compute, based on the collected data, an approximated interaction matrix that includes predictions for a set of dynamic variables for the current opportunity; and outputting a recommendation of one or more actions for achieving the milestone for the current opportunity based on the predictions for the set of the dynamic variables for the current opportunity.