What Are the Top 7 KPIs for a Mobile App Testing Services Business?

As mobile app usage continues to dominate the artisan marketplace, the need for reliable and effective testing services has never been more crucial. In an industry where user experience is paramount, understanding and tracking key performance indicators (KPIs) is essential for staying competitive and ensuring customer satisfaction. In this blog post, we will explore 7 industry-specific KPIs that are vital for mobile app testing services in artisan marketplaces. From user engagement and retention to app performance and security, we will provide valuable insights to help small business owners and artisans optimize their mobile apps and drive success in the digital marketplace.

Test Coverage Ratio

Definition

Test Coverage Ratio is a Key Performance Indicator (KPI) that measures the percentage of a software application that is covered by the testing process. It is critical to measure this KPI as it provides insights into the thoroughness and effectiveness of the testing efforts. A high test coverage ratio indicates that most parts of the application have been tested, reducing the risk of undiscovered defects affecting the end user. In the business context, a high test coverage ratio leads to better software quality, higher customer satisfaction, and a lower likelihood of post-launch issues. On the other hand, a low test coverage ratio increases the risk of undetected issues, leading to potential customer dissatisfaction, increased support requests, and reputational damage for the app developer.

How To Calculate

The formula for Test Coverage Ratio is calculated by dividing the number of application components or requirements tested by the total number of components or requirements. This ratio provides a measure of the extent to which the application has been tested, ensuring that a maximum number of features and use case scenarios have been exercised.

Example

For example, if a mobile app has 200 unique features or use case scenarios and 180 of them have been thoroughly tested, the Test Coverage Ratio would be (180/200) x 100, resulting in a test coverage ratio of 90%.

Benefits and Limitations

A high Test Coverage Ratio ensures that the app has been rigorously tested, leading to fewer post-launch issues, higher customer satisfaction, and a stronger reputation for the app developer. However, it's important to note that achieving 100% test coverage may not always be practical or necessary, as some parts of the application may be low-risk or redundant. It's essential to balance thorough testing with practicality and resource constraints.

Industry Benchmarks

Within the mobile app testing industry, typical benchmark figures for Test Coverage Ratio range between 80% to 90% for comprehensive and high-quality testing. Above-average performance would be considered anything above 90%, while exceptional performance levels would be close to 100%. These figures reflect the industry's standard of rigorous testing for mobile applications.

Tips and Tricks

• Adopt a risk-based testing approach to prioritize critical application components for testing, ensuring a balance between thoroughness and efficiency.
• Regularly review and update test coverage goals based on changes in the application's functionality or features.
• Leverage automated testing tools to increase test coverage and efficiently cover a wide range of use cases.

Defect Detection Efficiency

Definition

Defect Detection Efficiency is a key performance indicator that measures the effectiveness of a mobile app testing service in identifying and reporting software defects, bugs, and performance issues. This KPI is critical to measure as it provides insights into the thoroughness and accuracy of the testing process, directly impacting the overall reliability and functionality of the mobile application. By evaluating the Defect Detection Efficiency, businesses can understand the quality of the testing service and make informed decisions to ensure their apps meet high-quality standards before launching.

How To Calculate

Defect Detection Efficiency is calculated using the formula:

Where: - Total Defects Found Before Launch: The number of defects identified before the mobile app launch. - Total Defects Found: The overall number of defects uncovered during the testing process.

Example

For example, if a mobile app testing service identifies 100 defects before the app launch and a total of 150 defects throughout the testing process, the Defect Detection Efficiency would be calculated as follows: (100 / 150) * 100 = 66.67% This means that the testing service was able to detect approximately 66.67% of all defects before the app's release.

Benefits and Limitations

The primary benefit of measuring Defect Detection Efficiency is the ability to gauge the quality of the testing service, enabling businesses to make informed decisions regarding the readiness of their mobile applications for launch. However, a limitation of this KPI is that it does not account for the severity of the defects found, and some minor or cosmetic issues may not significantly impact the overall user experience but are still included in the calculation.

Industry Benchmarks

According to industry benchmarks, the average Defect Detection Efficiency in the mobile app testing services sector ranges from 50% to 70%, with exceptional performance levels reaching 80% or higher.

Tips and Tricks

• Implement both manual and automated testing methods to enhance Defect Detection Efficiency.
• Regularly review and update testing procedures to identify and address defects more effectively.
• Utilize real-time user feedback simulation to replicate the end-user experience and identify potential defects.

Test Execution Completion Rate

Definition

Test Execution Completion Rate is a key performance indicator that measures the percentage of test cases that have been executed out of the total planned test cases. This ratio is critical to measure as it provides insight into the progress of the testing process and the overall efficiency of the testing efforts. In the business context, this KPI is important because it allows the testing team and management to gauge the thoroughness of the testing activities and identify any potential bottlenecks in the process. It is critical to measure because it directly impacts the business performance by ensuring that the testing process is on track, and any issues are identified and resolved in a timely manner, leading to higher quality mobile apps and improved user experience. Ultimately, it matters because a low test execution completion rate could lead to the release of buggy or incomplete applications, resulting in negative user feedback and potential loss of revenue.

How To Calculate

The formula for calculating Test Execution Completion Rate is the number of executed test cases divided by the total planned test cases, multiplied by 100 to obtain the percentage. The number of executed test cases refers to the actual tests that have been performed, while the total planned test cases represent the entire set of tests that were scheduled to be conducted. By dividing the executed test cases by the total planned test cases and multiplying the result by 100, the test execution completion rate can be determined.

Example

For example, if a mobile app testing project had 300 planned test cases and 240 test cases were actually executed, the test execution completion rate would be calculated as (240 / 300) * 100, resulting in a test execution completion rate of 80%.

Benefits and Limitations

The benefits of measuring Test Execution Completion Rate include the ability to track the progress and efficiency of the testing process, identify potential issues or delays, and ensure that the testing activities are aligned with the overall project timeline. However, a limitation of this KPI is that it does not provide insight into the quality of the tests executed, as focusing solely on completion rate may lead to overlooking the thoroughness and accuracy of the testing activities.

Industry Benchmarks

According to industry benchmarks, the typical test execution completion rate in the mobile app testing industry ranges from 75% to 85%. Above-average performance would be considered a test execution completion rate of 90% or higher, while exceptional performance would be 95% or higher.

Tips and Tricks

• Regularly monitor the test execution completion rate throughout the testing process to identify any potential delays or resource constraints.
• Implement automated testing tools to increase the efficiency of test execution and reduce manual efforts.
• Provide training and resources to testing teams to ensure thorough and accurate test case execution.

Automation Script Reliability

Definition

Automation Script Reliability is a key performance indicator that measures the effectiveness and accuracy of automated testing scripts in identifying bugs and issues within mobile applications. This KPI is critical to measure as it directly impacts the efficiency and effectiveness of the testing process. In the business context, reliable automation scripts ensure that testing is thorough and systematic, allowing for the early detection and resolution of bugs before the app is launched. This KPI is critical to measure as the reliability of automation scripts directly impacts the overall quality and performance of the mobile application. Ensuring that the testing process is accurate and reliable contributes to a smooth user experience, reducing the risk of negative feedback or high abandonment rates.

How To Calculate

Automation Script Reliability can be calculated using the formula: In this formula, the number of successful automated test cases represents the tests that have accurately detected bugs or issues, while the total number of automated test cases includes all tests performed using the automated scripts. By dividing the number of successful cases by the total and multiplying by 100, the percentage of reliable automated test cases is obtained.

Example

For example, if a mobile app undergoes 100 automated test cases, and 85 of those successfully identify bugs or issues, the Automation Script Reliability KPI would be calculated as follows: (85 / 100) x 100 = 85%. This means that the automated testing scripts have an 85% reliability rate in detecting bugs and issues within the mobile application.

Benefits and Limitations

The primary benefit of measuring Automation Script Reliability is that it ensures the accuracy and effectiveness of the testing process, leading to higher quality and performance in the mobile application. However, one limitation is that this KPI does not account for the complexity of the bugs identified or the severity of the issues detected. Therefore, while it measures the reliability of the automated scripts, it may not fully capture the impact of the bugs on the overall user experience.

Industry Benchmarks

According to industry benchmarks, the typical standard for Automation Script Reliability in the mobile app testing industry is approximately 80%. Above-average performance levels would be around 90%, while exceptional performance would exceed 95%.

Tips and Tricks

- Regularly review and update the automated test scripts to ensure they align with the latest app features and functionality. - Incorporate exploratory testing or crowdtesting alongside automated testing to validate the accuracy of the automated scripts. - Analyze the root cause of any failed test cases to identify areas for improvement in the automation process.

Mean Time to Detect (MTTD)

Definition

Mean Time to Detect (MTTD) is a key performance indicator that measures the average time it takes for a mobile app testing service, like AppTegrity TestWorks, to detect and identify a defect or bug in an application. This ratio is critical to measure as it provides insight into how quickly the testing service can discover and address potential issues, allowing app developers to rectify them before they impact end-users. In the business context, MTTD is essential for ensuring the timely delivery of high-quality apps, reducing the risk of poor user experiences, and maintaining a positive reputation in the market. This KPI is critical to measure because it directly impacts the overall business performance, influencing customer satisfaction, retention rates, and the overall success of the app.

How To Calculate

The formula to calculate MTTD is the total time taken to detect and report a bug or defect, divided by the total number of bugs or defects identified. This gives the average time it takes for the testing service to detect each issue, providing valuable insights into the efficiency of the testing process and the frequency of defect identification. The total time includes the time from the start of testing to the detection and reporting of the bug, while the total number of bugs is the sum of all defects uncovered during the testing period.

Example

For example, if a testing service detects and reports a total of 100 bugs during a testing period that lasted 1,000 hours, the calculation of MTTD would be: MTTD = 1,000 hours / 100 bugs = 10 hours per bug on average. This means that, on average, it takes the testing service 10 hours to detect and report each bug, providing a clear indicator of the speed and efficiency of the testing process.

Benefits and Limitations

The advantage of effectively measuring MTTD is that it allows the testing service, such as AppTegrity TestWorks, to identify areas for improvement, optimize testing methodologies, and enhance the overall efficiency of bug detection. However, a potential limitation is that this KPI alone does not address the resolution time for identified bugs, which may also impact the overall quality and user experience of the app.

Industry Benchmarks

According to industry benchmarks, the average MTTD for mobile app testing services in the US is approximately 24 hours for detecting and reporting a bug. However, top-performing companies achieve exceptional MTTD of 6-12 hours, demonstrating their ability to swiftly identify and address defects in mobile applications.

Tips and Tricks

• Implement continuous testing methodologies to shorten the MTTD.
• Leverage automation tools for rapid bug detection and reporting.
• Analyze historical data to identify patterns and common sources of defects, enabling proactive bug detection.

Post-Release Bug Count

Definition

The post-release bug count KPI measures the number of bugs or defects identified in an application after its official release to the market. This ratio is critical to measure as it provides valuable insights into the quality and reliability of the mobile app, impacting user experience and overall business performance. A high post-release bug count indicates a higher possibility of negative user feedback, decreased app retention rates, and can damage the reputation of the app and its developers. Therefore, measuring and managing this KPI is crucial for ensuring customer satisfaction and success in the competitive app market.

How To Calculate

The formula for calculating the post-release bug count KPI involves counting the total number of bugs identified in the application after its release. This can be achieved through user feedback, customer support interactions, bug reports, and internal testing. The total count of post-release bugs is divided by the total number of app downloads or active users during a specific period to obtain the bug count ratio.

Example

For example, if a mobile app has 500 active users and 30 bugs are reported after its release, the calculation of the post-release bug count KPI would be 30 / 500 = 0.06. This means that for every active user, there are 0.06 reported bugs in the app post-release.

Benefits and Limitations

Effective measurement of the post-release bug count KPI allows businesses to identify areas of improvement in app development and testing processes, leading to increased customer satisfaction and retention. However, it's important to note that not all reported bugs may indicate critical issues, and the KPI alone does not provide information about the severity or impact of each bug on user experience.

Industry Benchmarks

In the mobile app industry, a typical post-release bug count of less than 0.1 per active user is considered to be desirable. Above-average performance would fall between 0.1 and 0.2, while exceptional performance would be below 0.1 per active user.

Tips and Tricks

• Implement rigorous testing processes before app release to minimize post-release bugs.
• Regularly collect and analyze user feedback to identify and address reported bugs promptly.
• Leverage beta testing programs to identify and fix potential bugs prior to official release.

Client Satisfaction Score

Definition

The Client Satisfaction Score is a key performance indicator that measures the level of satisfaction or dissatisfaction of clients with the products or services provided by a business. This ratio is critical to measure as it reflects the overall performance and quality of the products or services offered, as perceived by the end users. In the business context, client satisfaction is directly linked to customer retention, brand loyalty, and word-of-mouth referrals, all of which play a crucial role in the long-term success of a company. By measuring client satisfaction, businesses can gauge the effectiveness of their offerings and identify areas for improvement, ensuring sustained growth and profitability.

How To Calculate

To calculate the Client Satisfaction Score, the formula typically involves gathering feedback from clients through surveys, ratings, or reviews, and then aggregating the data to obtain an overall satisfaction score. The components of the formula may include factors such as the number of positive reviews, the frequency of repeat purchases, or the average net promoter score (NPS). By combining these components, businesses can derive a comprehensive client satisfaction score that represents the collective sentiment of their client base.

Example

For example, a mobile app testing service like AppTegrity TestWorks can calculate its Client Satisfaction Score by taking into account the number of positive reviews received from clients, the rate at which clients return for additional testing services, and the average NPS obtained from client surveys. By adding these components and dividing by the total number of clients, the business can derive an accurate representation of client satisfaction, driving insights for strategic decision-making and service enhancements.

Benefits and Limitations

The Client Satisfaction Score is advantageous as it provides businesses with a clear and quantifiable measure of client sentiment, enabling them to make data-driven decisions to enhance customer experience and strengthen brand loyalty. However, a limitation of this KPI is that it may not capture the sentiments of all clients, particularly those who do not actively participate in surveys or provide feedback. Therefore, businesses should complement this KPI with other metrics to gain a more comprehensive understanding of client satisfaction.

Industry Benchmarks

Within the mobile app testing industry, a Client Satisfaction Score of 90% to 95% is considered typical for above-average performance, signifying a high level of client contentment with the testing services provided. Exceptional performance in this context would be reflected by a score exceeding 95%, indicating outstanding client satisfaction and a strong reputation for delivering superior testing solutions.

Tips and Tricks

• Regularly collect client feedback through surveys, reviews, and direct communication to gauge satisfaction levels.
• Implement improvements based on client suggestions and concerns to enhance overall satisfaction.
• Provide exceptional customer service to address client needs promptly and effectively.
• Leverage positive client testimonials and case studies to build trust and confidence among potential clients.