Posts tagged analytics
Posts tagged analytics
That’s right, a computer game about football management can help you to develop skills that are useful for your career.. and I don’t mean getting a job as a Football Manager. For those who don’t know what it is, Football Manager (FM) is a simulation game, developed by Sports Interactive (SI), that puts you into the hot-seat of managing a football club.
So how can you weave ‘Playing FM’ into an interview? What is the skill that FM can help you to develop? It’s Analytics, that’s why it fits in this blog. If we look at what skills are currently in demand in the workplace it’s Big data. Companies, everywhere, are looking for people who can analyze the data that they generate.
So how does Football Manager help you develop valuable career skills?
I started playing FM under its previous carnation (Championship Manager), One of the common complaints were that after playing the game over many seasons, some of the newly generated players looked odd and the game appeared unbalanced, e.g (defenders were no longer brave). So I built a tool in my spare time that analyzed player attributes to see if there were differences in how players evolved over time to check for big differences. This taught me to code a little and how to analyze data.
It’s a simple tool that spat out a text file that showed you the differences between the data at 2 different points in time (e.g. do all defenders lose the ability to be aggressive in 20 years time?). SI used it at the time, however, I eventually became too busy to be able to do this, and I’m pretty sure SI developed their own tools to do this much much better than the buggy code I created.
The later versions of FM makes it possible for anyone to develop analytical skills - I’ve purposely selected some screenshots below that show you what Football Manager is all about.
This screen shot shows you how well a player has improved.
This one shows you their training regime.
One aspect of improving any product is testing. This means changing variables and checking the results. For example, in a web page you may want to improve the % of people who sign up. You usually do this by split testing, or A/B testing. Then you analyze the data set afterwards.
Look at those 2 screens… it’s the same principal. You tweak the training program, you assign different players to each program and then you compare the results to see which is more effective. There are a lot of people who are doing this already and unaware about the skills they are developing and how transferable they are to the real world. Take a look at the Tactics and Training Forum and you’ll find a lot of deep analytical talk where people discuss how to tweak training programs to improve players the most. It’s a hotbed of statistical analysis, A/B testing, spllit testing, metrics, measurement… no different to a professional analytics group on LinkedIn.
If you’re looking to develop your skills using FM, I recommend you use FM Genie Scout… some people may call it cheating… but it’s the ability to use it for data analysis that makes it so useful. Look at this screenshot -
It could be Google Analytics. The history function let’s record multiple data points. Here’s how you would perform a split test playing the game and using this tool -
It makes it easier to discover what changes are more effective for which player attribute, not much difference to optimizing a website or product… the fundamental skills are the same. For those who are more advanced, you can spit the data out into a spreadsheet. Once you’ve done this over several data points, you can plot any graph or create pivot tables to analyze player progression.
And that is how Football Manager can help you to develop real skills that are needed today.
I was reading this blog post by Fred Wilson and it occured to me that engaged users are not just about people who create accounts, regularly log in and contribute. But also about the different cross sections of people who are engaging with your app on their first visit, or returning visits on a passive level. The gist of the linked blog post is about twitter and how many people visit it in a month.
Usually, the 300M people who didn’t create an account are not measured as active users or engaged users. If we ignore it, we are missing out on the potential insights of those 300M users if we are not measuring correctly. If we look only at the drop-out points along the account creation funnels and see them only as failed conversions we are ignoring them as active users and failing to utilise the value they are gaining from just observing. These 300M should be segmented further (e.g. to returning and non-returning visitors) and we should analyse what they are doing to give us insights into their behaviour.
How About Mobile Apps?
One key difference between websites and mobile apps is re-discoverability. For many people, if they don’t like an app, they will delete it. For website, a new blog post, a link from a trusted source, a search engine can bring people back to the website, but for apps, once you have deleted it, the hurdles to re-find the app and re-install it are much higher than for websites. This poll shows that 26% of people uninstall an app after only using it once.
For mobile apps that require an account before being able to use the app, this can be a problem. The app could be redesigned to provide value to a user before they created an account. Then we can segment the groups and do some deeper analysis, and potentially reduce the number of app uninstalls. This opens up the chance to retain these “unconverted” users.
What are we measuring?
We are segmenting the users into different levels of engagement, in a similar way to how games can segment people into different ‘levels’ based on their progress in a game. This way we can discover different types of behaviours and insights to potentially convert to higher value actions.
The segments we are effectively using here are
These segments gives us insights into the behaviour of each group, and allows us to optimize the app for each group to increase retention. Engagement is the key, changing the engagement into a revenue event or some other high value event can come later. The more people you keep engaged in these channels the more possible high value events that can occur. An example is referral, people who may not have created an account but are regular passive users may go on to contribute to its organic growth.
Secondarily, high value events for each of these segments may be very different. They are different user groups with different behaviours, what they find high value may be different for each group.
How do I track it and what actionable insights can I get?
Check the number of people who return to your app. Some analytics tools, such as Flurry can provide you with the number of users who are one-session users. Compare this with each iteration of your app and see if you can reduce the % of people who are one-session users. Segment this group so you can drill down into their behaviour. What events are they doing? What events are they not doing when compared to returning users who have not created an account? Can you improve the app to make one-session users less likely to leave? Some examples of things you may want to optimize based on what you discover -
Just by providing more value at the beginning of your app, can help you to retain users and refer users. By identifying, segmenting and drilling down into their behaviours and comparing their behaviours to ‘active users’, it should give you insights on what to focus on to improve your app (or website). By engaging ‘non-active’ users, it’s possible to increase value to ‘active’ users as well.
Image courtesy of Flickr, Ed Yourdon