The rising user volumes we are seeing here at Floqsta provides us with a basis to continue to optimise the platform's intelligent matching algorithms as well as to experiment with new and innovative app features that drive engagement and retention. 

The AI engine of Floqsta is based on a natural language processing foundation and travel domain language model that has ingested huge volumes of publicly available travel-related content. It is able to establish associations and correlations between travel related terms and phrases. The engine uses this foundation in its compatibility scoring algorithms, inspecting all structured and unstructured fields of a user’s profile other users and establishing the compatibility of that user with a floq and other users within the floq. This drives the behaviour seen by users on the app in terms of the floqs that are presented to them. Our team is generally observing a very high quality of user profiles created by users enabling the platform to gain a good understanding of travel intent, travel preferences and personal interests. 

The machine learning piece of the engine is gradually evolving. The platform is designed to process all the interaction data on the app in a feedback loop (for instance, the floq joining and declining actions as well as chat interactions) to tune the models used in the scoring algorithms. As we continue development of our AI roadmap, this self-learning and optimisation piece is key to ensuring effective matching as user volumes continue to scale and forms a unique differentiator in our platform.

Meanwhile, a number of significant experience user-facing features have been rolled out recently with a focus on the Exploration and Communication stages of the user journey. These include : 

• Private messaging capabilities to enable users to get to know each other outside the group evironments 
• ChatGPT-assisted user prompts and introductions to make it easier for users to introduce themselves within Floqs and Tribes 
• User re-targeting features using Customer.io to encourage installed users back to the app and drive retention.

In May and June , we are expecting to release: 

• A Community section for users to engage more directly with other users who are active on the platform at the same time 
• The ability for a user to nominate their own destinations to seed a floq. This is in response to user feedback with users requesting destinations of their own  
• Local destination floqs, enabling users to tap into more spontaneous gatherings in their local city.  

Floqsta releases features with a product development approach that involves continuous monitoring and measurement through platform analytics (utilising Amplitude and backend homegrown analytics), experimenting with new features, testing outcomes and optimising.