App Analytics: The Path to Better Apps

App Analytics: The Path to Better Apps

In last week’s post, I had talked of m-commerce, and how companies could take advantage of the popularity of mobile devices to leverage their brands. In this post, I will examine application analytics and how they can change the way your company handles data.

App analytics refers to a group of methods related to optimizing the performance of an application. App analytics does this by looking at three key factors: how the application performs, who is using it, and how those users are accessing the application.

The analysis of these three factors allows the developers of that application to improve how the application’s overall functionality, thus giving a better customer experience. Imagine being able to know what problems happened and why, before customers even complain. Or even finding out what features are highly used and which features are fat that can be deprecated. That kind of insight is what app analytics provides.

3 Types of App Analytics

1. Transaction Analytics
2. Log Analytics
3. Access Analytics

Within the larger scope of the app, analytics are 3 major types of modules. Each can provide companies with a different way to answer the question of how to better serve customers. These modules are transaction analytics, log analytics, and access analytics.

Transaction Analytics

This module is used for evaluating the connection between the performance of the applications and the customers’ purchasing patterns. In particular, Transaction Analytics zeroes in on the numerous steps in the transaction process. These include adding to the cart, the editing of what’s in the cart, and the entering of user data while ordering. By figuring out how to streamline the process at every step, businesses can improve the chances of users completing the transaction as well as better position themselves to have return customers if applicable.

Log Analytics

Log analytics is conducted by analyzing the recorded data of diagnostic checks of your application. By looking at how the app is performing, you can identify common problems, and reduce the time needed to fix those problems. Further, Log Analytics lets you track user behavior, which can mean better customer service and an improved understanding of what your customers want.

Access Analytics

The last module type of note is Access analytics. It focuses on evaluating how the customers are accessing your application; this can be one of the biggest differentiating factors because it helps you understand your user base and grants you the ability to make the kind of changes that will be best received by that audience. For example, if you find that your most popular channel is on mobile, you can put more effort into improving that platform and increasing your focus on m-commerce. Conversely, if you find that your customers buy most from the desktop, you can optimize by offering products that can use the more robust graphical and processing capabilities of a computer.

To maintain consistency in your application’s quality, it is important to apply all three types of analytics to your application. This is in large part because an issue detected by one of these methods can play a part in issues with other parts of the application.

So now that we know what app analytics is, how do we use them?

Using App Analytics

At its core, app analytics focuses on improving an application, but it can be applied more abstractly. For example, you can apply app analytics to real-world applications. We can look, for example, at a fast-food restaurant like McDonald’s.

First, let us look at McDonald’s through the lens of Transaction Analysis. The latter lets you see what your customers are ordering most, and in what quantity. This information lets you know which items on the menu can be reduced in cost to increase the likelihood that users will purchase them now that price is less of a factor. Transaction Analytics might also give you the ability to see what kind of policies your cashiers should adopt to improve the transaction process.

Next, let us consider what can be learned through Log Analytics. With this type, you can see the demographics of customers who are buying from you, and the times they are buying. If you have a large student demographic, especially at times when school has just let out for the day, it might behoove you to implement a special deal to secure business and reduce the number of students going to your competitors. Log analytics will also help you identify what foodstuffs, such as buns, cheese, lettuce, etc, are being used most, so you can adjust the kitchen’s production to reduce the amount of wait time to produce your customer’s meal.

Finally, Access Analytics evaluates how users are visiting your restaurant: are they more the dine-in types or are they coming for a take-out? Do they come into the store, or are they drive-thru customers? By applying App Analytics, you can better appeal to your existing demographics, and implement new features to shore up the weaknesses in your other demographics.

How McDonald’s Used App Analytics to Push Sales

In the real world, McDonald’s has applied these types of app analytics to their restaurants; More McDonald’s now offers table service and electronic ordering, to speed up the transactional process and incentivize the dine-in experience. It has also implemented a ‘McPickTwo’ meal deal to allow it to improve the consumption of its products. Finally, the fast-food chain has increased hours of breakfast service to full time. The results speak for themselves: McDonald’s stock today is up 18% versus this time last year.

Next week, we will learn how to take m-commerce and app analytics and integrate them into a mobile app analytics strategy.