Market Research Framework for Product Feature Testing
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Solution Overview
Problem
Current product development and market research methods are inefficient in testing and evaluating specific features of a product in the marketplace, as they lack a systematic approach to distribute, enable, and gather data on test features across different user groups.
Innovation Solution
A method and system that involves distributing a product with identified test groups based on a selection key, enabling specific test features, gathering usage data, and evaluating the features based on this data to obtain test results, utilizing a market research framework that includes a policy, market research engine, and usage data repository to manage and analyze the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If product testing is conducted using employed testers and usability labs, then product quality can be evaluated, but the testing process is time-consuming and cannot provide real-world market feedback efficiently
Solution Approach 1:
The patent applies preliminary action by embedding test group identification and feature enabling mechanisms into the product before distribution. Test groups are identified using selection keys embedded in the product, and test features are pre-configured to be enabled automatically for these groups, allowing market research to begin immediately upon product deployment without requiring separate testing phases
Solution Approach 2:
The system enables self-service by allowing the distributed product to automatically identify test groups through embedded selection keys and self-enable test features without requiring manual intervention from testers or researchers. The product itself performs the testing coordination by gathering usage data from identified test groups and facilitating automated evaluation
2Loss of information
If manual testing and data collection methods are used, then detailed usage data can be gathered, but the complexity of managing test groups and collecting data increases significantly
Solution Approach 1:
The patent applies universality by creating a multi-functional framework that combines test group identification, feature enabling, usage data collection, and result evaluation into a single integrated system. The market research framework performs multiple functions simultaneously: it identifies test groups using embedded selection keys, enables appropriate test features, collects usage data automatically, and evaluates results - reducing the need for separate manual processes for each function
Solution Approach 2:
The system uses an intermediary mechanism in the form of selection keys embedded in the product that automatically identify test groups and trigger feature enabling. This intermediary eliminates the need for direct manual coordination between researchers and testers, as the selection key automatically mediates the process of identifying test subjects and configuring their testing environment
3Reliability
If features are tested in controlled lab environments, then specific tasks can be monitored, but the testing does not reflect real-world market conditions and user behavior
Solution Approach 1:
The patent applies inversion by reversing the traditional testing approach instead of bringing users into a controlled lab environment, the product is distributed into the real market environment and usage data is collected in situ. Test groups are identified among actual market users through embedded selection keys, and test features are enabled in the users' natural usage context, allowing testing to occur in the reverse direction - from controlled environment to real-world environment
Data Source
AI summary
A method of gathering research associated with a product that includes distributing the product in a market resulting in a distributed product, wherein the distributed product that includes a first test group identified based on a selection key, enabling a first test feature in the first test group, gathering usage data associated with the first test feature from the first test group, and evaluating the first test feature based on the usage data to obtain a test result.