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Data analysis can prove vital when it comes to app development, from being able to test for errors when debugging, all the way through to measuring user engagement and tracking key performance indicators.
In this post, we’ve gathered insights from some of our favourite technology contributors to cover a handful of the top reasons why you should consider using data analytics to help build your next app.
Contents
Data Analysis For User Experience
When asked to describe some of the benefits of analytics for app developers, Sam Browne, Founder of Find a DJ, responded with the following;
“Data Analysis helps in detailed user experience analysis, produces an overall view of usage and user experience, examines the engagement for each feature or page, and discovers the most desired features as well as pain points for mobile app developers.”
“Data analysis lets app developers figure out which elements of the mobile app get the users engaged and which elements make them leave.”
“They can then leverage this data to generate a list of features that users require and prepare for changes or modifications in the design, enhance the user experience, and maximize engagement.”
Nick Swekosky, CEO of Market Metrics and current maintainer of the GoLang toolkit hosted on the Federal Reserve, also emphasised on the importance of measuring user experience in his response:
“It's best to track app performance by users and events relative to behavioural actions.”
“By attributing specific events to user actions, app developers can define if a new feature is working and better understand the intent of user actions on their service.”
“Furthermore, one can understand the first activation users have on an app that ultimately leads to engagement, retention, and more revenue conversions.”
“Moreover, if a user reports an issue, an app developer can define an error hidden from the user or the actions taken to reproduce the issue.”
“Finally, if one uses the same unique identifier to track app performance as they do a CRM record, one can create customer segments to correlate app usage relative to population characteristics like annual sales volume, location, device, and demographics without the need for personally identifiable information.”
Analysis For Competitor Insights
“Our app developers regularly use data analysis to help them to streamline the testing and development process to speed up and improve their output.”
said Dima Suponau, former engineering lead at Microsoft and CEO at numberforliveperson.com
“They do this by studying comparable apps and their performance as well as analysing the data in order to evolve the finished product.”
“For us, the most important type of data analysis is competitive analysis. This is an incredibly competitive industry and, therefore, our developers are constantly crunching the data to make sure that our app is superior to anything else out there.”
“Using data to understand your customers is key to success for app development companies and should always be treated as a priority.”
Michael Yurushkin, CTO & Founder of data science company BroutonLab also supported how vital competitor analysis is in his response;
“Before starting development, analytics can show useful insights about similar apps, where they went wrong, or what they did right.”
“Comparative and competitive analysis is the first step to go through when developing an app, and it's crucial to use sufficient data for this.”
Toby Zhang, CEO of Shop LIT Live contributed the next four ways that data analysis assists his team in the process of developing apps.
Team Management
"To track our development tasks and progress we use JIRA. JIRA helps us to see how often we are ahead of, in sync with, or behind schedule. The tool also helps us to make near-real time adjustments to our product development schedules"
Feature Analytics
"We use a combination of Google Analytics, Appsflyer, and Facebook Analytics to measure key events within our App."
"This allows us to discover how users use our features (most frequently used features, least frequently used features, user journeys within the app, bottlenecks, bugs, etc) and make adjustments to our features to best fit users' needs and expectations. This helps our developers to prioritize feature releases."
Product Design
"We use Amazon's mechanical turk, Pickfu and traditional high-tech anthropology (aka monitored in person user studies) to analyze how users use our app and help with UI/UX design. This helps our developers to have better confidence in new features we build."
User Acquisition & Optimization
"We use Google Ads and Facebook Ads to drive app downloads. We adjust our creatives and keyword bids to maximize results and minimize cost per acquisition."
The Benefits Of Automated Data Analysis Systems
“Data is an immensely valuable commodity, but it is also easy to get caught up in the sheer volume of information and fail to find anything meaningful”
said Alexander M Kehoe, Co-Founder & Operations Director of Caveni.
“When it comes to any kind of development, the most useful data tends to lead to fixes for common problems that can be applied in the future.”
“It's not uncommon for automation software in bug fixing to have a feature which is able to spot and display bugs that have been previously reported or even bugs which are similar to those previously reported.”
“This alone can cut down on development and testing significantly. In that same spirit, having information on the development of previous features gives the same advantage. That information can be used to quickly create systems which bear similarities to past projects.”
“While some may say that all of that can be accomplished by a regular team, the difference in speed and accuracy between machine systems versus physical team members is extreme.”
“Having automated systems saves a large amount of back-office time and allows more resources to be spent exclusively on the development of new systems.”
Nate Need, CEO at DEV.co also emphasised the need for developers to use an automated system for data analysis in his response:
“Data analysis is critical for the development of any application.”
“Automation tools for application testing are critical for improving speed to delivery and project bug resolution.”
“In addition, operating in an agile framework with all stakeholders can help to bring the right problematic issues to the surface long before they may derail code delivery date(s).”
“The types of data most critical to success for software engineers always include detailed error/bug reports and conflict issues when integrating APIs.”
If you need to automate your data analysis collection with an automated log management system then Logit.io provides this service as well as hosted ELK & log analysis for app developers to improve how they work, compete and develop new features based upon user behaviour.
If you enjoyed this article on how app developers can use data analysis then why not check out our post on how to learn Java with zero prior programming experience?