Same-Game Parlays (Designing with Data)
Introduction
At Avid Gaming, staying ahead of user expectations was crucial for the success of our betting product, Sports Interaction. One powerful approach to achieving this was through data-driven design, where user behaviour and preferences guide the evolution of our digital products. The key was not just in gathering data about our customers behaviour but asking the right questions of that data to translate it into workable guidelines for our product progression. Below is the story of how we created a product we called ‘Same-Game Parlay’ in response to user desire.
A ‘Same-Game Parlay’ is a combination of different bets on the same game.
Asking the right questions
We had a record of what our users were doing – what deposits they made, when they withdrew, what bets they placed and when. We could also watch customers interacting with the product and determine what got in their way when they were trying to perform a task. We could not say for certain what they might want in the future. The challenge was in deciphering this based on historic behaviour.
I had a hypothesis that customers would really benefit from being able to place combination bets on the same event. It would make watching a game much more interesting amongst friends. Bragging rights for winning a full combination would be pretty high!
A user would have a slightly more complex bet card (the part of the interface where bets are placed) but it would be more useful and more fun. The business would also benefit because combination bets are more profitable than singles.
We were seeing new competition in the Canadian market and we wanted to compete on betting features. This was a key one of those.
This all made a lot of sense to me, but maybe I was wrong. Until I could prove its value it remained a hypothesis.
Before pushing my idea on the wider leadership group, I wanted to ask some questions of our data that would confirm or deny that users were demonstrating an appetite for such a feature.
The first question was whether customers were demonstrating an assumption that we offered this feature already. I wanted to know if they were thinking about more complex bets on a single game. We could learn this by looking at what errors were most commonly coming up when attempts were made to combine bets. The most frequent error was the message that ‘these selections cannot be combined’. Within this, half the instances of this error message related to selections from the same game. I had one strong pointer in favour of the hypothesis.
The second thing to look at was what customers were saying. This is something worth hearing but it is unwise to take this feedback literally.
There was a marketing survey done for Sports Interaction when we were acquired by Entain. This survey asked if potential customers would be interested in same game parlays. The survey concluded that they would not be interested. Potentially one piece of research against my hypothesis. It was a little murky however as the question was asked in a manner biased towards UK bettors. Our market was Canadian – customers with an altogether more fun outlook and without a culture of betting lingo. There was a danger that they simply did not understand the question.
The next thing I wanted to know was whether customers would find the same-game betting feature fun. Fun usually meant enjoying the anticipation of an event without having to learn too much new stuff or put in much work to make it happen. Really, the only way I could get an answer for this one was to monitor users interacting with a version of the product. I needed to do this in a cost-effective way, learn from the data gathered and then move forward, or back as the case may be.
Gathering data by MVP
We wanted to see if there was an appetite for same-game parlays but we wanted to do this using the least design and development effort and the least time. We needed a minimum viable product taster from which we could learn.
To achieve this, we made pre-combined parlays that customers could just add to the bet card. They were fun, frivolous bets that customers could place alongside their more sensible bets.
While this wasn’t exactly the product we were proposing, it would certainly give us some good pointers about customer appetite.
We saw that customers were really going for this feature, and while it was not the same as a fully functional same game parlay, we saw this activity as a sign that there would certainly be interest. This meant that whatever resources we were putting into making same game parlays were not as much at risk.
Next stage was a true MVP for the product feature. A pared-back version of the big picture.
The MVP would allow certain combinations but not others. We allowed the full parlay using certain markets only. The deciding factor was the speed at which the prices could be returned from Sports IQ, our third-party odds provider.
We delayed more complex features until later – like sub-sets of the full combination.
We were clever with how we used the prices delivered by Sports IQ so that we didn’t need to ask for all the prices, we could derive prices on our bet card. This would be the thing that would allow us to leapfrog the competition in the future.
We soft-launched the feature and allowed customers to discover it for themselves. This meant a minimum disturbance in the user experience.
Iterating and Refinement
Armed with the knowledge gleaned from user data, the design team embarked on an iterative process to create a solution that addressed the identified need. Prototypes were developed and refined based on user feedback, ensuring that the final design not only met the technical requirements but also resonated with the user base.
The culmination of the data-driven design process resulted in the introduction of a new feature—placing combination bets on the same game. This enhancement allowed users to diversify their betting strategies within a single match, combining different outcomes and increasing the overall excitement of the betting experience.
Following the implementation of the new feature, the platform actively sought user feedback. Data analytics continued to play a pivotal role in assessing how well the addition resonated with the audience. This feedback loop enabled the team to make further refinements, ensuring that the feature aligned seamlessly with user expectations.
Result
The data-driven design approach not only addressed a specific user need but also had a broader impact on the platform's success. By catering to the evolving demands of users, the online sports betting product saw increased user engagement, retention, and overall satisfaction. The combination bets feature became a key differentiator in a competitive market, showcasing the tangible benefits of a data-centric design approach.
The proportion of combination bets placed saw a rise in the period measured immediately post launch – 1 month.