Web Betting Game for Customer Preference Elicitation
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Solution Overview
Problem
Traditional methods for eliciting customer preferences, such as surveys and conjoint analysis, face challenges like maintaining participant interest, high costs, and consideration biases due to lack of incentives and awareness of other participants, leading to inaccurate and incomplete preference data.
Innovation Solution
A computer-enabled betting game platform where participants register, bet on product features they think will be preferred by the general public, with scoring and prizes for accurate predictions, providing a monetary incentive and aggregating preference information without disseminating it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional survey methods are used to elicit customer preferences, then participation costs can be controlled, but participant interest and answer quality deteriorate due to lack of incentives
Solution Approach 1:
The patent transforms the static survey format into a dynamic betting game where participants actively wager money on their preference predictions. This dynamic element creates genuine engagement and incentive, improving answer quality while the web-based platform keeps operational costs manageable through automation.
Solution Approach 2:
The patent changes the fundamental parameter of participant motivation from extrinsic (payment for participation) to intrinsic (monetary incentive through betting). This parameter change fundamentally alters participant behavior, leading to more thoughtful and reliable responses without proportionally increasing costs.
2Productivity
If participants are paid to participate in preference studies, then participation rates improve, but total study costs increase significantly
Solution Approach 1:
The betting game structure allows participants to self-select their level of engagement and wagering. They voluntarily invest their own money in the game, eliminating the need for researcher-funded payments while maintaining high participation rates through the inherent appeal of the game format.
Solution Approach 2:
The web-based platform serves multiple functions: it recruits participants, conducts the preference elicitation, manages betting transactions, and analyzes results—all in one system. This multi-functionality reduces overall study costs by eliminating separate operational components.
3Measurement precision
If participants are isolated to reduce social biases, then response accuracy improves, but implementation complexity and costs increase substantially
Solution Approach 1:
The web-based betting game platform acts as an intermediary that mediates between participants and the research process. It anonymizes participant identities and separates them from each other while still collecting aggregate preference data, achieving measurement precision without the complexity of physical isolation.
4Reliability
If participants spend more time considering preference questions, then answer quality improves, but participant willingness to continue deteriorates due to time investment
Solution Approach 1:
The betting game creates dynamic engagement where participants are motivated to spend time analyzing preferences because their monetary wagers are at stake. The game format naturally encourages deeper consideration without feeling like a time-consuming survey, resolving the contradiction between thoroughness and participant patience.
Data Source
AI summary
A method of eliciting customer preferences includes establishing a computer-enabled facility accessible to the target audience of participants, determining features to collect bets on, determining which participants may register, determining a betting period, determining betting rules, determining how to decide winners and prizes, registering the participants participation using the website, instructing the participants about procedures of abetting game and list of possible product features, betting, by the participants, on the product features the participants think will be preferred by the general public, determining whether an end of the betting period has been reached, determining, at the end of the betting period, average bets for each of the product features, predicting average customer preferences based on the average bets, determining a score of each participant who made bets, determining which of the participants has a best score, and using resulting preference information for production planning.