Solution Design and Validation

Solution design and validation involves developing and testing potential solutions to the problems identified through your research. This process can include creating prototypes, conducting usability tests, and gathering feedback from users. Solution design and validation is crucial for ensuring that your product effectively solves user problems and delivers value. It is also sometimes called Hypothesis testing.

Example

Continuing with the Tinder example, after identifying the problem and conducting customer discovery and research, the Product Manager and their team have decided to test two new features. The first feature is an improved matching algorithm that takes into account shared interests, and the second is an enhanced chat feature that is more user-friendly.

To validate this solution, the Product Manager decides to create a minimum viable product (MVP) of the two features. The MVP is a simplified version of the feature, providing just enough functionality to test the concept with users. The Product Manager collaborates with the design and engineering teams to develop the MVP, ensuring it aligns with the app's existing design and functionality. The improved matching algorithm is designed to prioritize potential matches who share significant interests with the user, and the enhanced chat feature includes features like read receipts and the ability to send GIFs and stickers.

Once the MVP is ready, the Product Manager decides to conduct an A/B test to evaluate its effectiveness. They release each feature to a small percentage of the user base. In a company like Tinder who has 75M users, you’d likely only release it to about 0.1% (or ~75,000) users.

The Product Manager and their team monitor the usage of the new features among these users over a period of two weeks. They track metrics such as how many matches are made using the improved algorithm, how many messages are sent using the enhanced chat feature, and whether there was any change in user satisfaction and engagement.

After two weeks, the Product Manager (and usually a data analyst) reviews the data to find that users who were matched using the improved algorithm had a 20% increase in match satisfaction and that users who used the enhanced chat feature sent 30% more messages. These results indicate that the new features are addressing the problems identified during the customer discovery and research phase.

Based on these positive results, the Product Manager decides to roll out the features to a larger group of users for further testing. They increase the group size to 1% of users and repeat the testing process. The results from this larger group confirm the initial findings, showing a similar increase in match satisfaction and chat engagement.

With these positive results, the Product Manager presents the findings to the leadership team and proposes a full roll-out of the features. The leadership team approves, and the features are added to the product roadmap for full implementation.

Throughout this process, the Product Manager continues to gather feedback from users and makes adjustments to the features based on this feedback. This iterative process of design, testing, and refinement helps to ensure that the final features are well-received by users and achieve the desired impact of improving user satisfaction and engagement.

Pain Points

Solution design and validation can be challenging due to the complexity of designing effective solutions and the need for iterative testing and refinement. It requires a deep understanding of user needs, strong collaboration with design and engineering teams, and the ability to interpret and apply user feedback effectively. Additionally, the results of a test can change dramatically with the size of the sample group.

Practical Exercise

Think about a product you really enjoy using. What solution does it provide to the problem it solves? How could this solution be tested and validated?

Related Research Topics

• Prototyping methods [ Google | Perplexity ]

• Usability testing [ Google | Perplexity ]

• User feedback analysis [ Google | Perplexity ]

• Hypothesis testing [ Google | Perplexity ]

• UX Research [ Google | Perplexity ]