Why does Google personalize your YouTube recommendations?
Why does Facebook show you only recommended posts and not every post from all your friends in the order in which they were posted?
The simple answer to that is this: because the data told them you would like this.
As the world continues to grow smarter, data is critical to competitive advantage, meaning a business’s ability to compete will increasingly be driven by how well, and how quickly, it can leverage data, apply analytics, and implement new technologies.
Every business, across sectors and industries, regardless of size, now needs to be a data business.
Even with all this data coming in, it can still be extremely difficult to anticipate the actual needs and behaviors of consumers. This is where in-app analytics comes in.
In-app analytics gives companies unparalleled insights into the otherwise hidden lives of app users. Similar to traditional web analytics tracking page views and buttons clicks on websites, mobile analytics track user activity throughout your mobile application. The resulting data provides insights vital to improving the user journey, refining content, increasing engagement, and meeting the KPIs by which you measure your app’s success.
Using a tool like Google’s Firebase Analytics, one can quickly create a hierarchy of the data most important to understanding how target users navigate and engage within an application. Once that data is collected, it can be filtered and manipulated to better understand how changes to the application affect various user activities.
This data, along with an A/B testing plan, can guide decision making to aid in application performance optimization, ultimately impacting a business’s bottom line.
Increase Revenue By Personalizing Each User’s Experience
During a typical A/B test, or split test, a random collection of users are shown a slightly different experience than the rest of the application’s users. The success or failure of a test is determined by the difference in conversion metrics between the two groups. This approach ensures that teams aren’t simply guessing at what a user wants or how they will react -- they have the data to prove it.
For a large, core-digital organization, there may be an entire team dedicated solely to planning and executing A/B tests.
But this isn’t realistic for the typical organization.
In order to be more efficient with limited resources, businesses should use data from analytics tools to focus tests on key areas of the application that aren’t converting well, as those areas will likely have the most room for improvement. In the early stages of optimization, attack the low-hanging fruit first. Identify low-effort, high-reward tests that will provide a positive return on investment quickly. Each test should only run for as long as needed to determine success or failure.
Small Changes Lead To Compounding Gains
When gathering data early on, it may seem appealing to look for “big wins.” More likely, however, are small gains that add up over time. At first glance, a one to two percent change in any given metric may not seem like a big improvement. But in the case of an e-commerce site or application, a one to two percent change to average basket size can have a massive impact in the long run. The focus should be on statistically significant improvements iterated across many tests resulting in meaningful KPI shifts.
Optimization Is A Journey, Not A Destination
Performance optimization is not a limited engagement with a beginning, middle, and end. Rather, it is an iterative process that will see as many failures as successes. Trade-offs will need to be made. Tests will not go as expected. Results will be inconclusive. This is a normal part of the process. Sift through the data, find what works, discontinue the tests that fail, execute new tests, repeat.
Information Is Power
Consumers spend 70 percent of their media consumption and screen time on mobile devices, and most of that time is within mobile applications. This means there is huge potential for companies to reach their consumers, but it’s also a highly saturated market. As more businesses compete for market share within the mobile landscape, businesses need to use mobile analytics to gain a competitive edge in building digital experiences and products that stand out.
Without mobile analytics strategies and tools, companies are unable to tell what target consumers engage with, who those consumers even are, what brings them to the application, and why they may leave. They are left flying blind, which means they will fall behind and underperform compared to the competition. With the massive growth in available data, plus the rapidly evolving methods for collecting and monitoring this data, the importance of a business’s analytics strategy will only increase. Those companies that use analytics planning and approach performance optimization strategically are the ones that will succeed in this new data-driven world.