3 Ways To Utilize Analytics Beyond Traditional KPIs

Greg Phelps
  • Greg Phelps
  • January 30, 2018
3 Ways To Utilize Analytics Beyond Traditional KPIs

Analytics is a buzzword that often gets associated with Key Performance Indicators (KPIs) – page views, unique visitors, keyword optimization etc. We’ve talked about this on our blog before but there’s a world of data out there waiting to empower your developers and strategists to make better product decisions. Here are three real-world examples (with names removed) of ways to look at your product data that will enhance your understanding of customer behavior and overall customer experience.

1. What percentage of users are actually using each feature

One of the most basic KPIs for a website is view counts for individual pages. Understanding which pages get the most views or keep viewers the longest can be incredibly valuable for a business. Just as you would track page views for different parts of a website, you should be tracking use of your app’s individual features. At Punchkick, our analytics team implements detailed tagging that allows us to take a look at how many users use each feature in an app. Once we know how much traffic each feature is getting (or not getting), we can start answering the bigger question: Why?

Consider this first example, which deals with an app’s navigational menu that contained more than 10 touch targets that link to various features. When we started tracking how often each target was used, a few patterns emerged. The first, two features were wildly more popular and regularly accessed by most users while the rest were used by less than half. Of the eight remaining targets, two were hardly used at all—by less than 10% of users. This spawned a dozen new questions. What was valuable about the most popular features? What were they doing right? Were the other features more bug-ridden? Did they serve less value? Was it just a matter of them being less visible (being at the bottom of the list on the menu)? Were any touch targets redundant (multiple paths to the same feature)?

Diving deeper into the data, we made another discovery. By breaking out sessions where a user had an account versus where they did not, we discovered that a large portion of our users were actually people who had never logged in, and were potentially ignoring features that brought the most value when connected to an account. Users who were logged in had a different usage pattern than those who were not.

The initial question, what percent of users actually use each menu item, generated a healthy backlog of tasks, like investigating why some features were being used more than others, seeking improvements in UI and feature visibility, and looking into why so many people never created an account. When empowered with these kinds of questions, we can spur countless positive change in our products.

2. Common user entries

Customers have come to expect an effortless experience and with every step in a signup or checkout process, a user has ample opportunities to abandon the user flow, and anywhere you can reduce friction is a huge win.

This next example deals with the signup process for a financial app that helps customers make decisions about money. For this app, as part of the initial user flow, customers were prompted to enter a number related to their finances that set a calculation variable in the app. The process felt long – the number of steps to completion was fatiguing users and it wasn’t surprising to see a user drop-off after each additional question. Because each additional question increased abandonment, we needed to reduce the number of steps.

For one question, we found most users entered one of three values – one of which made up over 90% of all submissions. To streamline the flow, we preset the value to the most common entry, which could be altered if needed. This simple change reduced friction for most users, and made the app appear smarter. It should be noted that in some contexts, pre-filling values can bias users, so it’s not a catch-all solution.

Taking a step back to look at the bigger picture, we realized this data told a story about customer segments, too. Since we were dealing with finances, this entry revealed where users might fall on the financial spectrum, which was a valuable insight that could be used for personalizing other parts of the app. We could start using their initial response as a heuristic for customization. The investigation started off as a way to simplify user flows, and revealed user segments as a side effect.

3. Server crashes as another metric for app performance

You’re probably already monitoring server health to make sure your data remains secure and reliable. What you might not realize is that your servers could also be generating a wealth of product information that just needs proper analytics to suss out.

In any server environment, unexpected crashes are inevitable and redundancy plans are necessary (Netflix has a particularly interesting fault-tolerance and redundancy system called Simian Army). In addition to having a backup system in place, consider monitoring the performance of your code from a server perspective to prevent issues in the first place.

This final example deals with a cloud-based app that relied on a group of servers to perform complex calculations across thousands of data points simultaneously. By setting up a monitoring dashboard, server failures were captured and analyzed, which ultimately allowed memory leaks and code failures within the app to become clear. By comparing the server performance of new builds against old versions of the app, the team was able to reject or accept them based on server performance.

From the customer’s perspective, slow response times and app freezes (server crashes) result in abandonment. With a server health analytics dashboard (and a proper test environment), negative customer impact was mitigated and developers proactively tackled leaky server code before it ever reached the customer.

Moving past traditional KPIs

When you move past traditional KPIs and start thinking about every aspect of your product as a possible data point, you can make more data-informed decisions on every level. You might discover a new area where you can differentiate your product, or expose a weakness that isn’t mentioned in customer reviews.

Get creative and optimize your user experience to get the max return on your app or website. As we discuss in our white paper, The ROI of UX, you can’t afford to not invest in UX and advanced analytics—and you never know what kinds of conversations a small data trend might spur. For more Punchkick strategies, tips, and best practices, be sure to check out our latest posts and subscribe to our newsletter.




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