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Summary of Project:
We’re improving the Atlassian Marketplace rating system to improve the customer evaluation process and empower customers in the Marketplace.
- Publish: Jun 5, 2023
- Discuss: Jun 19, 2023
- Resolve: Jul 3, 2023
Problem
Atlassian’s marketplace provides a review mechanism that enables customers to rate and share their experiences with apps, thereby offering insightful feedback to prospective buyers and partners for enhancement. However, the current approach lacks the granularity to offer comprehensive evaluations, which can prompt users to seek alternative resources for further information. Consequently, Atlassian has implemented measures to counteract fraudulent reviews and improve the rating process. This Request for Comment (RFC) aims to augment the calculation logic and display of ratings within the marketplace.
What is the problem?
The ratings in the Atlassian Marketplace play a crucial role in helping customers evaluate an app. However, our current rating system has several limitations that may hinder customer decision-making. For instance, the absence of average rating numbers in our Marketplace’s sections makes it difficult for customers to determine an app’s usefulness accurately. Additionally, the visual representation of stars as a means of evaluating quality may not be inclusive since the color contrast ratio does not meet accessibility guidelines. Our star rating system’s visual representation also utilizes increments of 0.5, which may obscure distinctions between comparable apps with marginally different ratings. Furthermore, our 4-star rating system may deviate from some customers’ mental models of conventional rating systems, leading to ambiguity and difficulty in understanding reviewer sentiment. Finally, combining ratings across cloud, Data Center, and server versions can lead to imprecise assessments of an app’s performance, particularly when legacy ratings are given equal weightage as more recent ratings. To address these limitations, we are exploring alternative ways of presenting app ratings that provide customers with more precise and reliable feedback.
How do we benefit by improving ratings on Marketplace ?
Establishing trust in the Atlassian Marketplace is of utmost importance to us. As a primary initiative, we have implemented Privacy and Security measures to bolster customer confidence. The evaluation process, including ratings and reviews, serves as a critical trust barometer for customers. Therefore, we are concentrating our efforts on enhancing the review system.
Our objective is to enable customers to provide more comprehensive and insightful feedback, and the rating enhancement will aid them in assessing the efficacy of apps more effectively. Enhancing the review system is conditional upon improving the rating system. At present, the ratings lack the necessary granularity to be truly beneficial for customers. To address this issue, we aim to shift towards a standardized 5-star rating system. This alteration will allow customers to provide more detailed feedback and facilitate better differentiation between apps based on their ratings. With the imminent conclusion of server hosting, it is even more crucial to segment ratings by hosting type. This ensures that customers can concentrate on hosting-specific ratings while providing cloud-oriented partners with an equitable appraisal.
In essence, our goal is to overhaul the current rating system by transitioning to a standardized 5-star rating system, introducing recency bias, and enabling hosting-specific ratings. These endeavors collectively strive to enhance customer trust and provide a more valuable and informative experience in the Atlassian Marketplace.
Proposed Solution and Change in user experience
Accurate representation of ratings: Improving the precision of rating representation involves two significant components: precise numeric ratings and fractional representation. Customers will be able to view the exact numerical rating values, which will facilitate clear visibility and ease of understanding. The rating system will incorporate fractional values in its visual representation, ensuring a higher degree of accuracy than the traditional 0.5-star increments.
Disclaimer: The design presented is tentative and subject to further refinement. It does not represent the final version and is provided for illustrative purposes only.
Displaying ratings in numeric form and utilizing fractional representation can greatly improve user experience. This provides clear visibility, allowing customers to quickly assess an app’s rating and make informed decisions. Fractional representation offers increased precision, enabling customers to distinguish between marginal performance variations and make more accurate evaluations.
Separating ratings by hosting type: It is important to note that each app will receive different ratings for its cloud, Data Center, and server versions. This differentiation acknowledges the inherent disparities and unique attributes of every app with respect to the hosting environment.
Disclaimer: The design presented is tentative and subject to further refinement. It does not represent the final version and is provided for illustrative purposes only.
Separating ratings by hosting type improves the user experience, as it allows customers to evaluate and compare each offering independently. This differentiation acknowledges that performance and features may vary across different hosting environments, which enables customers to make more informed choices based on their specific needs. By having separate ratings for cloud, Data Center, and server versions of an app, customers are provided with specific insights into each offering.
Adding recency bias to the ratings : At present, the computation of average ratings is derived from a basic mean calculation of all ratings. However, by introducing the concept of recency bias, greater significance will be attributed to more recent ratings.
Incorporating recency bias into ratings enhances the user experience by assigning greater importance to recent ratings. This approach offers a more precise reflection of an app’s present performance, which boosts customer trust in the ratings. Furthermore, recency bias addresses past problems and ensures a fair evaluation based on current user experiences. It also provides Marketplace partners with the opportunity to improve their app ratings by enhancing their app and support without being hindered by previous performances.
Moving from a 4-star system to a 5-star system: Adjustments are needed to transition from a 4-star rating system to a 5-star rating system, including Historical Rating Conversion to transform existing ratings. Extrapolation is one way to maintain consistency and accuracy while updating the framework. We are also exploring alternative methods to accomplish this. Additionally, we plan to implement changes that will allow customers to rate apps on Marketplace on a 5-star scale in the future.
The shift from a 4-star rating system to a 5-star rating system provides several advantages for the user experience. This includes an increase in granularity, which allows for more detailed feedback and evaluation of an app’s performance. Additionally, adopting the industry-standard 5-star system promotes consistency and familiarity, making it easier for customers to interpret ratings across different platforms and websites.
By implementing these changes, the app marketplace will provide customers with an improved experience by offering accurate, detailed, and hosting-specific ratings. It will also enhance precision, compatibility, and consideration of recency in evaluating app performance.
Asks
While we would appreciate any reactions you have to this RFC (even if it’s simply giving it a supportive “Agree, no serious flaws”), we’re especially interested in learning more about:
- Overall Rating and Hosting-Specific Ratings: Should we continue to maintain an overall rating in addition to hosting-specific ratings? Alternatively, considering the end of life for server hosting, would it be more appropriate to focus solely on cloud ratings and Data Center ratings, excluding the overall rating?
- Recency Bias in Ratings: We would appreciate gaining insight into our partners’ perspective on the perceived benefits of incorporating recency bias into the rating system. This modification involves assigning greater importance to recent ratings. Your viewpoint regarding the advantages and potential challenges of implementing recency bias would be highly valuable.
- Transition from a 4-Star System to a 5-Star System: We are currently evaluating the possibility of transitioning from our present 4-star rating system to a standardized 5-star system. We would greatly appreciate your professional opinion regarding the potential benefits of this transition, as well as any concerns you may have regarding the use of a simple extrapolation method for converting legacy ratings to the new 5-star system.
We appreciate your time and expertise in providing feedback on these specific points outlined in the RFC. Your insights will play a significant role in shaping our decision-making process.