Best practices after launching your application: hypercare phase, measuring product adoption, KPI measuring tools and more.
After reading this article, you’ll know how to:
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Measure adoption: frequency and depth of user interaction
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Use analytic tools to measure application performance
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Report issues to our support crew
The journey isn't over after the successful launch of your application on Betty Blocks. In fact, it continues within the post-go-live support or hypercare phase - a set of activities to ensure the end-users adopt the system and receive a continuously positive experience. This article will explore some of the best practices for maintaining your application after the launch: measuring adoption, getting the performance data, reporting issues, etc.
Note: Everything mentioned in this document is not part of the product that comes out of the box with Betty Blocks!
Hypercare phase and issues
Having a post-go-live strategy is one of the key factors to application success. Depending on the application’s complexity and level of innovation, you should be ready to keep an eye on how the product performs after being released. This approach is called a hypercare phase, and it demands having the resources at hand, particularly developers on standby to address any issues that may arise after the application goes live.
Unless your product isn’t just a room reservation application or is somewhat basic and plain, you should be prepared to face unforeseen problems once the end-user starts their first interaction. Therefore, our first piece of advice: have the team at hand during the hypercare phase to monitor the application and respond to any type of functional, technical, or security issues. The hypercare support ends only when the system is stable, usually, it takes 30 to 45 days depending on the complexity of your application.
Reporting issues
When facing challenges with the application on Betty Blocks, your team might feel the urge to address the questions directly to our support crew. However, we would like to suggest that you not rush and first have a look at the available documentation. For example, the articles on testing an action or debugging might be extremely helpful in lots of cases. The latter one also has recommendations on how to reproduce issues, that is report them to our support team if you run out of possible solutions. Also, see the Specialized support services for the list of services provided by Betty Blocks support.
Measuring product adoption
Product adoption (or user adoption) occurs when a potential customer finds out about your product and starts using it to accomplish their goals. It’s all about turning customers into users who understand the value of your product and its features.
The product adoption is different from the product activation - the time when a user just turned from an application visitor to a user. During the adoption phase, the end-user realizes the true value of the product and its full potential to fulfill their needs. This happens during the onboarding, reading of product documentation or/and in-app training.
Frequency & depth of user interaction
Measuring the product adoption consists of frequency and depth of user interaction. It’s a strategic approach to understanding user behavior. By measuring it, you’ll be able to see the relevance of the application for completing user’s tasks and its overall satisfaction, and as a result, guide the development team into further development of certain areas and eliminate potential (or current) issues.
Let’s start with tracking the frequency of user interaction. This one will show real user engagement that we later can measure and estimate this data and as a result understand the current state of the application and think about improvements.
There are metrics you should consider:
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Number of logins per user can help identify patterns such as peak usage times or inactive periods.
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Session duration determines the average time spent on the application.
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For instance, shorter durations may suggest users are struggling or finding their way quickly.
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Frequency of key actions, like submission of forms, data entry, usage of specific features, etc.
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As a result, it can highlight areas of the application that are either popular or may need improvement.
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Besides tracking user logins and the number of actions, it's important to assess the depth of user interaction to gain other nuances of their experience. Depth measures the level of the application exploration made by the end users and gives you ideas about future training, readiness of certain features, alignment with expectations, etc.
Some metrics to measure the depth of user experience:
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Feature usage analysis helps identify which features are being used extensively and which are receiving less attention. This data helps prioritize updates and improvements.
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Mapping out the user journey within the application lets you understand the sequence of actions users take from login to completion of tasks. This helps in optimizing the application flow and enhancing user experience.
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Identify power users vs. casual users. The power users engage deeply while the casual ones may be only working with certain features. This is the data gained to improve communications and training resources for each segment and maximize the application's potential.
Combining frequency and depth data points may help you see the bigger picture and understand your needs better. Collect the data-driven action points and make your decisions based on those.
Relevant KPIs
Measuring key performance indicators (KPIs) is closely linked to the frequency and depth of user interaction. It’s the method of gaining quantitative metrics that offer valuable insights into the effectiveness of your application. By gathering and measuring KPIs, you can take further steps for your product more freely as areas for improvement will be extracted.
Let’s have a look at some relevant metrics and tools of KPI measurement that you might want to consider:
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Adoption rate (‘Number of feature users’ / ‘Total number of users’)*100
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It enables you to calculate how many existing users are actively using a specific feature.
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Adoption rate (‘Number of feature users’ / ‘Total number of users’)*100
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It enables you to calculate how many existing users are actively using a specific feature.
It enables you to calculate how many existing users are actively using a specific feature.
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Number of daily/monthly users
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See the number of active users for a specific period - this one is a straightforward way to get an understanding of how well your product is adopted.
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Number of daily/monthly users
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See the number of active users for a specific period - this one is a straightforward way to get an understanding of how well your product is adopted.
See the number of active users for a specific period - this one is a straightforward way to get an understanding of how well your product is adopted.
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Percent of daily/monthly users (‘Daily/monthly users’ / ‘Total number of users’)*100
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This way gives an even clearer picture and shows real user engagement throughout a certain period.
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Percent of daily/monthly users (‘Daily/monthly users’ / ‘Total number of users’)*100
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This way gives an even clearer picture and shows real user engagement throughout a certain period.
This way gives an even clearer picture and shows real user engagement throughout a certain period.
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Average usage frequency / average session duration using the app or its feature.
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These metrics help you understand product adoption on a more detailed level.
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Average usage frequency / average session duration using the app or its feature.
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These metrics help you understand product adoption on a more detailed level.
These metrics help you understand product adoption on a more detailed level.
KPI measuring tools
To gain useful insights into user behavior, you can employ different tools to your liking. Though some popular ones have proven their value:
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Google Analytics
Measure such metrics as active users, session duration, feature usage, and user journey data. Implement Google Analytics tracking codes within the application to capture relevant user interaction data.
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Mixpanel
A platform that helps measure feature adoption, user engagement, convention rates, and retention metrics. Set up event tracking in Mixpanel to monitor specific user actions and behaviors.
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Heap Analytics
Automatically captures user interactions, feature engagement, and user journey analytics, allowing a detailed analysis without explicit event tracking.
You can also develop and integrate your custom analytics solution based on your unique requirements of the application.
All things considered, by addressing most of the aspects described above, your development and support teams can establish a strong foundation for ongoing application maintenance. The proactive and strategic approach to the post-go-live phase builds user trust, keeps their experience positive, and contributes to the long-term success of your product. Take advantage of it!