Punters

Background

The betting performance tracker is a feature that has been added to the Punters iOS app. It was designed with a human centred approach. As product designer at Australia’s leading horse racing publisher Punters, I’m often presented with various feature requests, both internally and externally. Evaluating the worth and purpose of each feature request is just as important as executing it, if not more. So when an internal stakeholder proposed the idea of a betting performance tracker for our iOS app, I of course met the idea with a degree of scrutiny. As this is literally going to consume 2months of 2–3 employees’ time, hence it was important that everyone understood the importance and purpose of such feature. So many questions sprang to mind…

What problem is this solving?

Do people even bet enough to necessitate a betting performance tracker?

Would they even want to see it if they did?

What kind of effect would this have on their morale?

If I’m already asking these questions, think about all those down the line who work on the product, i.e developers, marketers, product managers. They need to feel motivated with enough research and evidence to work on the product too, not just the designer. As a designer, your job is to build empathy and confidence throughout the rest of the team in the products they build. With that being said, I set out to answer the following questions:

Do people even bet enough to necessitate a betting performance dashboard?

A quick look in the backend answered this question. The answer was yes. The average number of bets placed per user was indeed high enough to populate a healthy graph. The stakeholder was right in their hunch and I was wrong.

Would users find betting performance tracking a valuable feature?

I answered this by posting in our online forum. Punters has over 100,000 registered users. A simple google survey answered this, plus a variety of other questions. Turns out that approximately 75% of survey participants already were tracking their betting performance in some way. With close to half of them manually entering in bets into their own spreadsheet!

I also asked the following to get a more specific idea of what they required in a betting performance tracker:

Once we had answered ‘why’, it was time to answer ‘who’. Amongst our users, there’s a wide array of gambling skill level and style. Through a combination of Facebook data, user interviews (both in person and via our live forum) and backend insights into user profiles, I was able to put together this rough generalisation of the type of persona that would be inclined to use this feature. The result was a shrewd and savvy punter, definitely not for the novice.

Low Fidelity Prototyping

High Fidelity Prototyping

Userflow