Customizing Software Trials via Candidate Data Mining
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Solution Overview
Problem
Software companies face challenges in effectively demonstrating their extensive feature sets to potential customers, as existing methods fail to tailor demonstrations to individual candidates' interests and needs, leading to suboptimal sales outcomes.
Innovation Solution
The technology involves data mining of candidates' biographical and behavioral data to customize software trial demonstrations by selecting relevant features and usage stories that align with their interests, using a feature and story mapping module that integrates browsing history, social media interactions, and business segment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional software demonstration methods are used to showcase extensive feature sets, then all features can be demonstrated, but the demonstration fails to resonate with individual candidates' interests and needs
Solution Approach 1:
The system performs preliminary data mining of candidate biographical and behavioral data before the demonstration to pre-identify interesting features and usage stories. This advance preparation enables personalized customization without adding complexity during the actual demonstration delivery.
Solution Approach 2:
The system creates customized copies of the software demonstration tailored to each candidate's interests by selecting and assembling relevant features and usage stories from the complete feature set. Instead of modifying the core demonstration system, it generates personalized versions through data-driven selection.
2Measurement precision
If data mining of candidate information is performed to customize demonstrations, then personalization accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The system extracts only the most relevant features and usage stories from the complete software feature set based on mined candidate data. By selecting and extracting only the top matching elements rather than processing or presenting all features, it achieves high personalization accuracy while minimizing data processing time.
Solution Approach 2:
The system changes the parameter of demonstration content by dynamically selecting which features and stories to include based on mined candidate attributes. This parameter-based selection approach enables precise personalization without requiring exhaustive processing of all possible demonstration elements.
3Productivity
If comprehensive candidate data is collected and analyzed, then demonstration relevance improves, but system complexity and data handling requirements increase
Solution Approach 1:
The system segments the comprehensive candidate data into distinct categories (biographical data, behavioral data, browsing history, social media interactions) and processes each segment separately through dedicated analysis modules. This segmentation reduces overall system complexity by breaking down the complex data handling task into manageable, specialized components.
Solution Approach 2:
The system introduces a feature and story mapping module as an intermediary between raw mined data and demonstration content. This intermediary component translates complex multi-source candidate data into standardized feature selections and story choices, simplifying the overall system architecture and data handling requirements.
Data Source
AI summary
The technology disclosed describes systems and methods for delivering software trial demonstrations that are customized, with features identified as interesting to a software demonstration candidate, by mining biographical and behavioral data of the candidate. The technology further discloses systems and methods for the customization of trial demonstrations with software usage stories that reflect a software demonstration candidate's interests, identified by analyzing mined biographical and behavioral data about the candidate.