Taking the recommendation engine route up hamburger hill



Taking the recommendation engine route up hamburger hill


Here is a phrase for you to chew on – the hamburger menu. You would likely have to be a UX designer to know what this term actually means, but we all know instinctively what it represents. We can also have an educated guess as to just why UX designers would have a problem with it.


On a mobile device the hamburger menu or sidebar button is almost inevitably the cause of usability friction, adding one further step or click to get the user through to their objective. It obscures more than it explains.


According to the UsabilityGeek.com blog, in a piece published earlier this year, along with linear navigation which forces each individual user to navigate the app the same way, the hamburger menu is one of three navigation issues that is “plaguing mobile apps”.


As the blog goes on to suggest, as app designers attempt to remove these navigational obstacles, we can expect to see more “creative navigation along the lines of Amazon Shopping’s functionality”, which allows users to browse categories and sub-categories without needing to go down any specific path to get them there.


There are clearly lessons to be learned here for sports-betting apps. Trends in mobile sports-betting app design has demonstrated that the issue of hard-to-fathom navigation is perhaps the most obvious block on providing consumers with a truly personalised offering.


True personalisation in UX involves providing a layout designed around the individual user, yet getting to the point of being able to offer this type of service is about more than just a navigational design issue. Responsive design has previously been about enabling any given site to work regardless of the device it will be viewed on; but making it responsive to each individual user is a far harder task.


Indeed, the whole process of building a suitably tech-enabled yet personalised mobile sports-betting offering is no simple task. A system needs to be able to collect large amounts of information about any given player, analyse and learn from that data, identify player behaviours, categorise players by their similarities, predict what they are likely to want to bet on next and subsequently offer one-to-one marketing messages.


It is a task which has been tackled by many of the most popular apps out there. Whether it is the aforementioned Amazon, or other giants of the mobile app space such as Spotify and Netflix, personalisation is now the standard and helps define what users expect from their online mobile experience.


They may be working across diverse fields in music, film and TV and mass-market retail, but what all of these have in common is that they employ recommendation engines that define the service each individual customer receives.


Recommendation engines are what will define the winners in the developing app ecosystem and that applies as much in sports-betting as it does in the wider universe of consumer-facing mobile offerings. The ability to offer the right product at the right time is true customisation, and given the correct rewards – and without having to rely on possibly intrusive blind bonusing and push notifications – it will see players coming back time and again to your offering.


Machine-learning technology – as part of an artificial intelligence-led offering – holds the key in mobile sports-betting as much is it does with the likes of Amazon, Spotify and Netflix, providing the capability to offer a tailored sports-betting experience. Particularly effective in sports is the hybrid recommendation system – the same as is employed by Netflix – whereby collaborative filtering (where information is collected from many other users) is matched with content filtering (utilising user metadata).


Developments along these lines are effective for all markets whether they are developed – such as in Europe – or are in the more formative stages. Indeed, in markets across Africa where mobile is the primary access route to the internet – but where smartphone penetration levels are running at lower levels – a recommendation engine-driven app can work even better with less sophisticated devices.


These are fundamental moves for the sports-betting industry. As seems obvious to all who watch developments with the hardware, as the mobile devices of the future progress it offers up the prospect of faster offerings, with better and more potent functionality. It also means the apps that accompany these devices will need to be even more robust, easy to use and personalised.



To go deeper on the matter and visit our Panel at the iGaming super show in Amsterdam. Dont forget to book a meeting! See you there.

1 thought on “Taking the recommendation engine route up hamburger hill”

  1. Pingback: Acting wisely for retention and acquisition of players: Focus on engagement - BtoBet

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