Single Bet Recommendations

A breakdown of how Single Bet Recommendation work and the key use cases to get started.

Overview:

Welcome to the Bet Recommendation Engine: Single Bets – an advanced solution meticulously designed to redefine the user experience on your sportsbook platform. This cutting-edge product goes beyond conventional recommendations, placing a strong emphasis on unparalleled personalization, engagement strategies, and operational efficiency.

Key Features

Personalization Excellence: The engine excels in tailoring recommendations based on intricate user behaviour patterns, ensuring each suggestion aligns precisely with individual preferences and historical betting activities. Through advanced algorithms, the engine continually refines its understanding of user preferences for more accurate and personalized suggestions over time.
Seamless API Integration: The Bet Recommendation Engine seamlessly integrates into your existing platform, streamlining the user experience and allowing for effortless implementation without disruptions. With a user-friendly API, the integration process is straightforward, enabling a quick and hassle-free deployment on your sportsbook platform.
Prioritized Recommendations: Prioritize user engagement with market level recommendations sorted in descending order of relevance, guaranteeing that the most enticing bets capture user attention first. This prioritization is based on a sophisticated ranking system that considers numerous factors, including historical user behaviour, current trends, and event-specific data.
Engagement Amplification: Drive user activity and betting participation by presenting enticing and relevant market level recommendations, creating a dynamic and immersive betting environment. By leveraging the engine's capabilities, you not only increase user engagement but also foster a sense of excitement and personal connection, making the betting experience more enjoyable for your users.

How It Works

Operational Mechanism

The engine's operational mechanism is a nuanced three-step process, ensuring the generation of highly relevant and personalized recommendations:

User Behaviour Analysis:
- In-Depth Exploration: The engine meticulously analyses user behaviour, considering historical bets, preferred sports, competitions, and specific event types.
- Individualized Insights: This analysis goes beyond surface-level trends, delving into individual user preferences and betting patterns.
Algorithmic Processing:
- Leveraging state-of-the-art algorithms, the engine processes the wealth of user behaviour data.
- Predictive Modelling: These algorithms predict and prioritize potential bets based on a comprehensive understanding of user preferences, encompassing recent trends, market dynamics, and individualized parameters.
API Integration:
- Seamless Connection: The engine seamlessly integrates into your sportsbook platform through a user-friendly API.
- Effortless Implementation: Sportsbook operators can effortlessly receive personalized bet recommendations, enhancing the overall user experience without disrupting existing functionalities.
Technical API Details
- To seamlessly integrate the Bet Recommendation Engine - Single Bets - into your sportsbook platform, it is essential to understand the API request and response structure. Follow this comprehensive guide for a clear understanding of the process.

API Request and Response Structure

Request Structure

Endpoint
- URL: The endpoint URL is client specific and will be provided to you separately.
- HTTP Method: GET
Headers
- Content-Type: application/json

Request Body: The request body is a JSON object with the following fields:

id_clients (string or array, required): A string or array of user ids for which recommendations have to be retrieved.
top_n (integer, required): The number of top recommendations to retrieve for each client. If not specified, a total of 250 market level recommendations will be returned.

Example Request Body

{ 

  "id_clients": [1011377, 1023029], 

  "top_n": 3 

}

Response Structure

The expected response from the recommendation service comprises the following components:

items (array of objects): Contains recommendations for each client.
id_client (string): Identification of the user associated with the response.
bet_recommendations (array of objects): List of recommended bets for the user.
eventTime (string): Date and time of the recommended event.
SportName (string): Sport category of the recommended event.
TournamentName (string): Tournament name of the recommended event.
MarketName (string): Market category of the recommended event.
rating (float): Recommendation score for the bet. Please note that the rating order may seem unconventional due to exponential scaling; for instance, 0.3397, 2.978e-1, where 2.978e-1 is a lower but exponentially scaled rating
rec_id (string): Unique identification for the recommended bet.

Please note that the rating order may seem unconventional due to exponential scaling; for instance, 0.3397, 2.978e-1, where 2.978e-1 is a lower but exponentially scaled rating

Example Response

{ 

 "items": [ 

   { 

     "id_client": "1023029", 

     "bet_recommendations": [ 

       { 

         "eventTime": "2023-11-01 20:15:00", 

         "SportName": "Football", 

         "TournamentName": "England EFL Cup", 

         "MarketName": "DoubleChance", 

         "rating": 1.0298758529188778, 

         "rec_id": "12023110140397012" 

       }, 

       // Additional recommendations... 

     ] 

   }, 

   // Additional items... 

 ] 

}

Expected response when one or more clients are new or missing from our database

Request:


{ 

  "id_clients": [1011377, 2345673, 2345321], 

  "top_n": 3 

}

Response:

{ 

  "items": [ 

    { 

      "id_client": "1011377", 

      "bet_recommendations": [ 

        { 

          "eventTime": "2023-11-01 20:15:00", 

          "SportName": "Football", 

          "TournamentName": "England EFL Cup", 

          "MarketName": "DoubleChance", 

          "rating": 0.9135270790769161, 

          "rec_id": "12023110169094262" 

        }, 

        { 

          "eventTime": "2023-11-01 20:15:00", 

          "SportName": "Football", 

          "TournamentName": "England EFL Cup", 

          "MarketName": "UO_Goals-1.5", 

          "rating": 0.8933097427109823, 

          "rec_id": "12023110169094271" 

        }, 

        { 

          "eventTime": "2023-11-01 20:15:00", 

          "SportName": "Football", 

          "TournamentName": "England EFL Cup", 

          "MarketName": "UO_Goals-0.5", 

          "rating": 0.8873393105654175, 

          "rec_id": "12023110169094285" 

        } 

      ] 

    } 

  ], 

  "missing_clients": { 

    "missing_client_ids": ["2345321", "2345673"], 

    "message": "We could not find any recommendations for the missing  
	 clients. This may be due to these clients being new, with fewer than 
	 10 bets, or because these client IDs do not exist in our database." 

  } 

}

Expected response when the client list is empty

Request:


{ 

  "id_clients": [], 

  "top_n": 3 

}

Response:

The 'id_clients' field is missing or not a valid array.

Understanding these structures will enable your sportsbook platform to seamlessly integrate and leverage the Bet Recommendation Engine - Single Bets, providing users with personalized and relevant bet recommendations.

Practical Use Cases

Explore real-world applications of the Bet Recommendation Engine: Single Bets and understand how to leverage personalized bet recommendations in diverse scenarios.

Dynamic Content Integration for Individualized User Dashboards: Dynamically integrate personalized bet recommendations into user dashboards to present users with relevant and enticing betting opportunities, ultimately driving increased engagement.
Targeted Email Campaigns with Personalized Recommendations: Tailor email campaigns with personalized bet recommendations to boost effectiveness. Users receiving emails featuring bets aligned with their preferences are more likely to open, engage, and act on the recommendations, resulting in increased user activity.
Retention and Reactivation Strategies through Personalized Banners: Utilize personalized bet recommendations in banners to enhance targeted communication. Users with decreased activity receive banners highlighting bets tailored to their preferences, effectively communicating with individuals based on their historical behaviour and increasing the likelihood of reactivation.
VIP and Loyalty Personalization in Exclusive Sections: Leverage personalized bet recommendations for VIP and loyalty programs to create exclusive sections offering a tailored VIP experience. VIP users encountering personalized recommendations within designated areas further solidify their loyalty, contributing to a premium and personalized user journey.

Operational Impact and Business Benefits

Understand the tangible operational impact and business benefits of implementing personalized bet recommendations on your sportsbook platform.

User-Centric Engagement: Practical use cases demonstrate a user-centric approach, ensuring that every interaction with the platform is personalized and relevant to individual preferences.
Higher User Satisfaction: Personalized recommendations cater to individual preferences, leading to higher user satisfaction as players find bets that align with their interests.
Improved Retention: The personalized player experience fosters stronger connections with the platform, contributing to improved user retention rates over time.
Increased Bet Relevance: Users are more likely to find bets that resonate with their preferences, resulting in increased betting activity and a more enjoyable betting experience.
Increased Conversion Rates: Operational impacts such as targeted communication, dynamic content integration, and efficient cross-selling contribute to increased conversion rates, prompting users to act on personalized recommendations.
Streamlined Marketing Strategies: Personalized bet recommendations streamline marketing strategies, making them more effective and aligned with user behaviour. This results in enhanced user satisfaction and platform loyalty.