A breakdown of how User Preferences work and the key use cases to get started
Product Overview
The Bet Recommendation Engine - User Preferences is a pioneering solution designed to redefine user engagement within sportsbook operations.
By leveraging comprehensive historical bet and transaction data, this advanced tool offers profound insights into individual preferences. It empowers sportsbook operators to craft highly personalized front-end components, delivering a unique and tailored user experience.
Key Features
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Preferred Sports Feed: Dive deep into users' historical sports and league preferences using the BRE API. Optimize the user interface dynamically to highlight the most relevant and frequently chosen sports based on past betting behaviour.
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Preferred Tournaments Feed: Utilize the API for smart historical competition suggestions based on users' betting history. Enhance user engagement by offering personalized and targeted betting options through prioritized competitions.
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Preferred Teams and Athletes Feed: Leverage the API to fetch prioritized teams and athletes for each sport, presenting team/player IDs and recommendation rankings derived from historical data. Encourage bets on previously chosen teams and athletes.
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Preferred Markets Feed: Use the API to fetch and prioritize markets for each sport and competition, aligning betting options with user interests based on historical data. Offer a curated list of markets derived from past preferences to enhance user participation.
How It Works
Integration and Data Ingestion
The Bet Recommendation Engine seamlessly integrates with Sportsbook Operators, establishing a robust data ingestion system. This process involves collecting and processing user behaviour, historic betting patterns, and transaction data. The collaboration empowers the engine with a rich dataset, forming the basis for personalized recommendations.
Data Analysis and Machine Learning
Upon integration, the engine employs advanced machine learning algorithms to analyse the extensive dataset. This analysis plays a pivotal role in identifying users' preferred sports, tournaments, teams, athletes, and markets. Through machine learning, the Bet Recommendation Engine transforms raw data into actionable insights, enabling a nuanced understanding of each user's historical preferences.
API-Driven Operation
The Bet Recommendation Engine seamlessly operates through robust API endpoints, ensuring effortless integration for sportsbook operators. This API-centric approach allows for the smooth incorporation of user preference data into systems, serving as a conduit for historical bet and transaction data. Operators initiate integration through API requests, retrieving comprehensive historical data that spans preferred sports, tournaments, teams, athletes, and markets, forming a detailed user profile. This cohesive approach integrates APIs with historical data retrieval, empowering operators with actionable insights for personalized recommendations and an enhanced user experience.
Timely and Relevant Recommendations
The Bet Recommendation Engine emphasizes active pre-match bets, aligning recommendations with users' recent and relevant behaviour for enhanced accuracy. Additionally, the engine provides a 4-day horizon, offering insights into upcoming sports, teams, athletes, tournaments, and markets available for betting. This forward-looking perspective enriches the user experience, enabling proactive planning and engagement with pertinent betting options over the next two weeks.
Dynamic Front-End Optimisation
Operators can dynamically optimize front-end components using insights from the Bet Recommendation Engine. This empowers operators to ensure the prominence of the most relevant and frequently chosen options on their platforms. By prioritizing sports, tournaments, teams, and markets based on historical preferences and user behaviour, the user interface becomes tailored to individual preferences.
API Request Structure and Example Responses
API Request Structure
To seamlessly integrate the Bet Recommendation Engine into your sportsbook platform, follow these detailed integration steps
Set Headers:
Ensure the appropriate headers are set: ‘Content-Type: application/json ‘.
Construct Request Body:
Build the request body as a JSON object with the following fields:
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id_clients (string or array, required): User ID or array of IDs for which recommendations are needed.
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top_n (integer, required): Number of top recommendations to retrieve for each client. Please note that this field is optional. If a request is made without a specified top_n, all relevant recommendations will be returned.
API Endpoints
Utilize the following API endpoints to access personalized recommendations (Note: API endpoint URLs are client specific, please refer to the relevant API documentation for your company).
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Preferred Sports Feed:
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Endpoint: /api/preferred-sports
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Request Method: POST
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Preferred Tournaments Feed:
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Endpoint: /api/preferred-tournaments
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Request Method: POST
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Preferred Teams and Athletes Feed:
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Endpoint: /api/preferred-teams-athletes
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Request Method: POST
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Preferred Markets Feed:
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Endpoint: /api/preferred-markets
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Request Method: POST
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Example API Request
Refer to the provided example for the API request structure. Below is an example of the API request structure:
{
"id_clients": [1011377, 1023029],
"top_n": 3
}
Example Response Structures
Refer to the provided examples for each feed (Sports, Tournaments, Teams/Athletes, Markets) to understand the expected structure of the API response.
Example Response (Sports):
{
"items": [
{
"id_client": "1023029",
"sportPreferences": [
{"sport": "Tennis", "rating": 0.28378427},
{"sport": "Basketball", "rating": 0.100532964},
{"sport": "Volleyball", "rating": 0.03186505}
]
},
// Additional user entries...
]
}
Example Response (Tournaments):
{
"items": [
{
"id_client": "1011377",
"tournamentPreferences": [
{"tournament": "England Premier League", "rating": 55.417243559665984},
{"tournament": "Poland Ekstraklasa", "rating": 41.964815912794066},
{"tournament": "Spain LaLiga", "rating": 37.83963079882878}
]
},
// Additional user entries...
]
}
Example Response (Teams and Athletes):
{
"items": [
{
"id_client": "1011377",
"playerPreferences": [
{"preference": "Austria", "SportName": "Football", "TournamentName": "International European Championship Qualification", "rating": 0.0033773411114522566},
{"preference": "Greece", "SportName": "Football", "TournamentName": "International European Championship Qualification", "rating": 0.003223825606386245},
{"preference": "Netherlands", "SportName": "Football", "TournamentName": "International European Championship Qualification", "rating": 0.003070310101320233}
]
},
// Additional user entries...
]
}
Example Response (Markets):
{
"items": [
{
"id_client": "1011377",
"marketPreferences": [
{"market": "DoubleChance", "tournament": "International European Championship Qualification", "rating": 0.05},
{"market": "DoubleChance", "tournament": "USA NHL", "rating": 0.04870263912774858},
{"market": "EuropeanHandicap-0:1", "tournament": "International European Championship Qualification", "rating": 0.009933774834437087}
]
},
// Additional user entries...
]
}
Use Cases and Implementation
1. Dynamic Front-End Components
Leverage insights from the Preferred Sports Feed to dynamically organize and present sports categories on the front end.
For a user consistently betting on football, prioritize football-related content on the homepage, side menus, and event pages. This ensures a tailored front-end experience, increasing the likelihood of user interaction with preferred sports.
2. Team-Centric Navigation
Make use of the Preferred Teams and Athletes Feed to create team-centric navigation options.
For a user showing a consistent preference for a particular basketball team, prioritise team-specific content such as upcoming games, team statistics, and exclusive team-related promotions. This targeted approach optimises the front-end for users with specific team preferences.
3. Market-Focused Presentation
Leverage the Preferred Markets Feed to curate market-focused sections within the front-end components.
If a user regularly engages with markets like "Asian Handicap," prominently display this market type on the homepage and prioritise markets in the event pages. This front-end optimisation ensures that users quickly find and navigate to their preferred markets.
4. Tailored Browsing Menus
Utilise insights from all feeds to dynamically optimize side menus for personalised browsing.
For a user with diverse preferences, structure the side menu to offer quick access to their most engaged sports, tournaments, and teams. This personalised menu streamlines the user's navigation, providing a more intuitive and efficient front-end experience.
5. Prominent Recommendations Section
Incorporate curated recommendations from all feeds into a prominently displayed section on the homepage.
Highlight personalised recommendations, such as upcoming matches, favoured teams, and recommended markets, in a dedicated section on the homepage. This ensures users are immediately exposed to content aligned with their historical preferences, enhancing their front-end interaction.
Targeted Marketing Campaigns
1. Sport-Specific Promotions
Utilize insights from the Preferred Sports Feed to create targeted marketing campaigns for specific sports.
For users with a strong historical preference for soccer, run promotional campaigns offering exclusive bonuses, free bets, or loyalty rewards tied to upcoming soccer events. This targeted marketing approach increases overall enagagement across players and increases overall game diversity.
2. Tournament-Centric Campaigns
Leverage the Preferred Tournaments Feed to design marketing campaigns centred around specific tournaments.
Identify users consistently engaging with major tennis tournaments and launch campaigns featuring bonuses, odds boosts, or challenges related to those tournaments. This targeted marketing strategy increases the relevance of promotions and captures the attention of users with a demonstrated interest in specific events.
3. Team-Driven Incentives
Make use of the Preferred Teams and Athletes Feed to create marketing incentives based on users' favourite teams.
For users displaying a consistent preference for a particular basketball team, design marketing campaigns offering bonuses, cashback, or exclusive promotions related to that team's upcoming games. This targeted approach enhances user engagement by aligning incentives with individual team preferences.
4. Market-Specific Offers
Leverage the insights from the Preferred Markets Feed to craft marketing campaigns around specific betting markets.
Identify users regularly participating in markets like "Over/Under" and launch targeted marketing campaigns featuring enhanced odds, special promotions, or cashback offers for those markets. This ensures that promotions resonate with users' preferred betting options.
5. Personalised Email Campaigns
Combine insights from all feeds to create personalised email campaigns tailored to individual user preferences.
Send targeted emails featuring a combination of sport-specific promotions, tournament-centric offers, team-driven incentives, and market-specific bonuses. This holistic approach ensures that each user receives a personalised email highlighting promotions aligned with their unique historical preferences.