Detailed rendering of a flight information board showing all flights delayed.

While a wealth of analytics tools gives us increasingly detailed views into how users navigate through our systems, services and apps, there’s no metric that tells you how your users were feeling at the time. In fact many of the terms that we measure for technology services, such as bounce rate, retention and number of pages viewed, all act as a stand in for how users were feeling – and one emotion in particular: frustration.

Frustration is the silent killer of technology businesses. It’s the feeling that causes users to close the screen, or worse delete the app, without giving you any idea as to why. Sometimes frustration can build over time, meaning that a series of frustrations can lead to a developer mis-attributing the source of lost users. Of course you can survey your users, run user tests and look to your app reviews for indications as to how people felt about your service, but by the time someone has become frustrated or annoyed it’s unlikely they want to spend additional time providing feedback.

However there is one metric that, at least in the mobile app world, can often be a strong indicator of the building level of frustration of your users – and that is time.

Coming from a web marketing world, where “time on page” was a key metric that web owners tried to optimize for, not against, it’s a fairly significant change in thinking. But with such limited real estate on a mobile app screen, there’s not a huge amount to occupy a user’s time on functional pages – such as screens to sign up, options, invite or settings. Increased time spent on these screens can signal that something is overly complicated, difficult to understand or generally frustrating for the user.

There are a number of tools that help app developer measure time spent per screen, although all have limitations and a fair bit of set up required. The one that gives the best indication is Google Analytics for Mobile:

timeonscreen

However to get an accurate reading for this metric, Google requires a lot of advanced set up, including detailed naming of all of the screens within your app. Additionally, Google is great for looking at average results across your whole audience but has significant limitations for drilling down into your audience – it’s much harder to see who is having trouble with your app screens and the range of times it takes different segments of your audience to move through your app.

An alternative is Mixpanel which doesn’t allow you to see the average time that users spend on a specific screen, but does allow you to see the time it takes for user to move through a series of predefined steps in, say, a sign up funnel.

mixpaneltime

Like Google, Mixpanel requires some initial set up, but allows for much greater segmentation of your userbase, to determine which subsets of your users are taking the most time to get through your app. For many independent developers, however, Mixpanel’s higher costs can be off-putting.

Regardless of how you measure the time that users spend on various screens within the app or service, this us an often under-utilized metric that tells you quite a bit about how frustrated or confused your users are. If a screen that takes you 15-20 seconds to pass through has an average use time of over a minute, your audience could be missing the point, unable to find a button or unsure of what to do.

Using time as a proxy for frustration in your app or service can help highlight places where you’re causing users to fall out of love with your product, even if it’s not the place where they give up on your app entirely.


A quick one before a more thorough summary of my week at Mobile World Congress – I gave this talk as part of the Advanced User Interfaces Seminar at Mobile World Congress 2014 (#mwc14) hosted by the UK’s ICT KTN. It covers the challenges and proposes some solutions in designing and testing apps and mobile services geared towards a non-traditional audience such as elderly, disabled or young users.


Image 2014-02-21 at 9.12.30 AM

Amazed and impressed to see this on my Facebook feed. Had completely forgotten! Just goes to show that you can’t judge the boot-strapping startup by their early inability to pay designers.


Self-Aware-Calvin&Hobbes

Reposted from an answer added to a Quora topic I found quite interesting asking “What tools can I use to evaluate brand awareness.

I have a pretty regular routine for checking brand awareness and reach, using a combination of manual and automated tools.

First thing each day, I search for our brand on Google, filtering the results to the last 24 hours. This shows me new press coverage and blog mentions, as well as some social content.

I then check Netvibes (Social Media Monitoring, Analytics and Alerts Dashboard) where I have a series of boards set up to check internet forums, blog searches, Instagram posts, Google Plus, Facebook and other channels. I find that volume of organic mentions on Facebook tends to correlate well to overall brand awareness so that’s a very important metric for me.

Next I check on Tweetdeck for saved searches of variations on our brand name (i.e. 23 Snaps instead of 23snaps) for anything I might have missed on the other searches.

This all takes me about 5 minutes, if that. I also respond to any relevant mentions, either thanking the author for their comment, reposting positive remarks or answering questions.

Finally, I have saved Google Alerts (Monitor the Web for interesting new content) that will ping me an email if anything about our brand gets indexed. I also have saved alerts for our competitors and a few major trends in our industry.

Do you have any additional tools or services you use to monitor brand awareness?


In a slightly departure from my normal posting habits, I wanted to share a quick hack to solve something that has been bothering me in Excel for ages. Finally, with Excel 2013, I’ve discovered there is a way to determine the distinct count of unique items in a list using pivot tables, something that wasn’t previously possible without a fair bit of work. Numerous Google searches on the topic didn’t reveal much (aside from some older forums and a number of Stack Overflow threads) so I thought I’d share here in case anyone else has had similar frustrations.

If you have Excel 2013, then select your data to create a pivot table, and when you create your table, make sure the option ‘Add this data to the Data Model’ tickbox is check (see below).


Tick the box next to ‘Add this data to the Data Model

Then, when your pivot table opens, create your rows, columns and values normally. Then click the field you want to calculate the distinct count of and edit the Field Value Settings:


Edit field value settings

Finally, scroll down to the very last option and choose ‘Distinct Count.’


Choose the option ‘Distinct Count

This should update your pivot table values to show the data as a distinct count of unique values. Job done!