Video Consultation Hesitation Metrics for Makeup Preferences
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Solution Overview
Problem
It is challenging for individuals to provide precise feedback on preferred cosmetics during makeup consultations, especially when subtle differences exist, and they may feel uncomfortable expressing their preferences to makeup professionals, leading to inefficiencies in recommending cosmetic products.
Innovation Solution
A system that detects video conferencing sessions between makeup professionals and clients, extracts user behavior data related to suggested cosmetic effects, applies weight values to generate hesitation metrics, and displays these metrics in a user interface to aid professionals in making more informed recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If makeup professionals rely on direct user feedback during consultations, then they can understand user preferences, but users may feel uncomfortable expressing their preferences accurately
Solution Approach 1:
The system introduces an intermediary mechanism (behavior tracking software) that objectively captures user interactions with cosmetic products without requiring direct user expression of preferences. This mediator records click patterns, time spent on products, and selection behaviors, translating implicit user actions into explicit preference data that professionals can analyze.
Solution Approach 2:
The system enables self-service by allowing users to naturally interact with the digital cosmetic catalog without needing to verbally express their preferences. Users simply browse and interact with products as they would in a physical store, and the system automatically tracks and analyzes these self-directed behaviors to generate preference insights.
2Measurement precision
If makeup professionals analyze multiple cosmetic options with subtle differences, then they can find the perfect match, but the consultation process becomes time-consuming
Solution Approach 1:
The system performs preliminary analysis by pre-tracking and analyzing user interactions with multiple cosmetic options during the consultation. It continuously monitors user behavior patterns, click sequences, and time spent on different products, preparing preference data in advance so that the final recommendation can be made quickly without requiring time-consuming analysis at the end of the consultation.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring user interactions and immediately processing this data to generate preference insights. The feedback loop allows professionals to see evolving user preferences during the consultation and adjust their recommendations accordingly, reducing the time needed to reach a precise match.
3Measurement precision
If the system tracks detailed user behavior data, then it can generate accurate hesitation metrics, but the data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant behavioral data points needed for calculating hesitation metrics, such as time spent on each product, number of clicks, and selection patterns. By selectively extracting only the necessary data elements rather than processing all possible user interactions, the system maintains metric accuracy while reducing processing complexity.
Solution Approach 2:
The system transforms raw behavioral data into standardized parameters that are easier to process and analyze. It converts complex user interaction sequences into simplified metrics like total time on page, number of product views, and click frequency, changing the parameter representation to reduce processing complexity while preserving the essential information needed for accurate hesitation calculation.
Data Source
AI summary
A server device detects initiation of a video conferencing session between a consultation device utilized by a makeup professional and a client device utilized by a user receiving a makeup consultation from the makeup professional. The server device extracts data from the client device during the video conferencing session, the data characterizing behavior of the user performed on the client device with respect to suggested cosmetic effects transmitted by the makeup professional via the consultation device to the client device. The server device applies weight values to the extracted data and generates one or more hesitation metrics based on the weight values and causes the one or more hesitation metrics to be displayed in a user interface on the consultation device.