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Optimisation and automation have become key buzzwords for the iGaming industry in 2023…Golden Whale Production’s Claudia Heiling sheds insights on the development of GWP’s LOOPS to help operators further refine their business processes by unlocking the power of machine learning.
In the rapidly evolving world of iGaming, if you’re not moving forward, you’re standing still – and while many companies have already started utilising automation and real-time optimisation to streamline their processes, recent advancements in technology mean operators are now free to harness the power of machine learning to further enhance their systems and deliver even more powerful results.
While the industry has been quick to embrace the arrival of new technology, the fact remains that many of the out-of-the-box models on the market currently only offer a one-size-fits-all solution. This means that once a process or “optimal” way of doing things has been established, there is a reluctance to challenge these models and replace their current iterations with newer ones.
At Golden Whale Productions, we identified this issue as an area of the market that could potentially be improved and sought to redefine how process optimisation works through the introduction of LOOPS by GWP.
Essentially a plug-in machine learning infrastructure component that can be attached to any existing system with minimal technical effort, LOOPS are capable of ingesting any form of data, running it through a customised model, presenting actionable input in near real-time and then using semi-automated mechanisms to constantly evolve and adapt with changing user behaviour.
Being an agnostic technological layer, LOOPS are able to analyse any form, shape or size of data depending on the use case at hand. This means they can be used to tackle almost any classification, prediction or optimisation problem that operators might encounter.
As we know, both the actions of operator’s customers and the core features of their product are unlikely to remain static. In addition to that, using a model to drive alterations in the behaviour of your system creates self-inflicted changes in the data you are collecting.
LOOPS can consistently tweaking and updating models and assumptions to ensure that even the most recent data is being considered and delivers feedback into any system within a maximum of 300ms.
LOOPS are therefore able to react to customer input and behaviour the moment it happens, opening up far more efficient ways for operators to personalise their products. At the same time, they are also permanently looking for a new optimal point of operation.
Having been deployed across a wide range of product verticals, we’ve already worked through a number of different scenarios to create a library of reusable, industry-specific LOOPS that are now ready to be deployed. With this dramatically shortening the time-to-market between data contact with LOOPS and actual use in operation, companies can get the technology up and running right away, while at the same time knowing their models will only improve with time as further information is ingested. The goal is clear; to amass as many relevant and working examples as possible in order to accelerate the automation surrounding these processes to the absolute maximum.
Naturally, as the library of use-cases for LOOPS grows, so too will the practical applications for operators. Not only will they be able to select from an ever-expanding range of pre-configured LOOPS that can be immediately applied to their systems, but they can also easily develop their own to tackle problems that may be specific to them. Over time, this should ensure that almost every conceivable problem will have been considered by LOOPS, meaning the system will continue to grow exponentially.
This means that by using LOOPS, operators will be able to give each user precisely what they need at every point of their journey in real-time, whether that be offering them a bonus, a message of support or even a recommendation to try a different game. By providing recommended actions for all these scenarios and more, LOOPS effectively operate as a trusty co-pilot that’s always on hand to help operators make machine learning-informed decisions to improve their daily business in areas like CRM, marketing and BI – and it’s then up to them to decide how many of these actions they choose to utilise.
By constantly updating and retraining their models, LOOPS are able to provide the most relevant and up-to-date recommended actions, which will in turn enable operators to consistently remain ahead of the curve. As such, they’ll be able to deliver the most optimised user experience possible, no matter how the industry develops.
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Claudia Heiling – Co-Founder & COOGolden Whale Productions
