Future Anthem's Content Recommendations module delivers sportsbook personalisation across every surface, from navigation to bet slip, in real time.
Sports betting is dynamic, shaped by player preferences and personal passions. But many sportsbooks rely on static logic that treats all sports bettors the same.
Flagship sporting events are surfaced to everyone in the same way. Navigation is fixed. Bet builders and accumulators are left to the player to construct manually.
The problem is exacerbated during peak moments like the FIFA World Cup, Cheltenham or Wimbledon. It doesn’t matter whether the end user is more interested in football, horseracing, tennis or something else entirely; everyone sees the same thing.
This creates three core problems for operators:
The challenge extends beyond recommending a bet; it is to deliver sportsbook personalisation at scale.
That is the issue Future Anthem’s Content Recommendations module is built to solve with its sports recommendations feature.
"The challenge extends beyond recommending a bet; it is to deliver sportsbook personalisation at scale."
Personalising a sportsbook is a system-level challenge where multiple layers of decisioning must be powered by a shared understanding of each player.
A modern recommendations system needs to operate across every touchpoint:
Each layer requires a consistent behavioural view on what players bet on, how they bet, and when they bet. But it’s also critical to consider how those behaviours evolve over time, particularly during high-intensity periods like major tournaments.
At the heart of sports betting personalisation sits a fundamental tension between showing players more of what they already like, or helping them discover something new.
Favour familiarity too heavily and the experience becomes repetitive. Push too hard on discovery and the relevance drops. But a robust system must do both.
In practice, this means:
This balance is not just a UX principle; it is encoded directly into the ranking and allocation logic of the system.
Content Recommendations builds a behavioural representation of each player and creates recommendations that will resonate with them.
A profile is developed for every player, combining:
Structured behavioural features are combined with learned betting patterns to capture preferences and implicit relationships, such as which markets tend to be selected together or how players structure their bets.
Players are matched with others who behave similarly, not based on identical bets, but on shared behavioural patterns over time.
From these similar players, the platform collects:
This creates a dynamic pool of recommendations, reflecting both peer behaviour and current market activity.
Each candidate is scored using a combination of signals:
These signals are normalised and combined to produce a baseline relevance score.
Additional adjustments are then applied:
The result is a ranked list of recommendations tailored to each user.
One of the hardest problems in sports betting personalisation is the cold start challenge: delivering relevant recommendations for new or infrequent events where direct player signals are limited or absent.
This is particularly acute for a new tournament on the calendar, seasonal events like Cheltenham, or global competitions like the World Cup that produce sudden spikes in activity.
Future Anthem’s Content Recommendations handles this in two ways:
Player preferences are learned at multiple levels — sport, competition, market —allowing the system to infer interest in new events within familiar structures. A player with a strong history of UEFA Champions League betting, for example, is likely to engage with a new European club competition even without prior direct signals.
When direct signals are weak, recommendations are generated using recent activity patterns, market-level popularity and content similarity, ensuring the output remains relevant without defaulting to generic or manually curated content.
The result: every player receives relevant recommendations from day one, in real time, with no manual intervention required.
Multi-leg bets are critical for both engagement and margin, but they are among the hardest products to personalise effectively.
Most approaches attempt to construct synthetic combinations based on individual selection scores. Future Anthem’s Content Recommendations module takes a different approach: rather than constructing synthetic combinations, the module uses real tickets placed by similar players.
Each ticket is evaluated based on:
The result? Accumulator and bet builder recommendations that are more intuitive, credible and likely to drive meaningful engagement by reflecting how players naturally construct bets.
Personalisation can also be applied to the structure of the sportsbook itself.
That means:
The effect is a sportsbook that feels instinctively relevant and reduces friction by ensuring the most relevant content appears earlier in the journey, improving both engagement and conversion.
From an operator’s perspective, the Content Recommendations module is designed to integrate into existing platforms with minimal disruption to existing roadmaps.
Recommendations can be delivered through:
This means personalisation can be deployed consistently across every channel — web, app, and marketing — from a single integration point.
Future Anthem’s Content Recommendations module allows operators to:
Crucially, the module is designed to handle large volumes of players and events with no manual intervention required. That is what sportsbook personalisation looks like in practice.
The impact is measurable, and across deployments the pattern is consistent:
The next frontier for Content Recommendations is real-time intent modelling.
By incorporating live signals such as betslip activity, systems can move from: “What does this player usually do?” to “What is this player trying to do right now?”
This shift enables even more precise and timely recommendations, particularly in fast-moving in-play environments, delivering relevance at the exact point it matters most.
Ready to deliver real-time personalisation at scale — through the new season and beyond? Book a demo