◾Favorite product
The "Favorite Product" feature is designed to enhance marketing and gamification possibilities by identifying and categorizing user preferences based on their betting activity.
This feature analyzes recent betting behavior to help you understand which products—Casino, Sports, or Lottery—your users prefer. This allows for more targeted and personalized marketing strategies.
Logic Behind
The feature calculates user preferences by analyzing their betting activity over the last 30 days. Each bet the user places is assigned a weight, where more recent bets carry more influence than older ones.
This is done using a time decay logic, which smoothly decreases a bet's weight as it ages. The weighted bets are then aggregated to determine the percentage share of each product (Casino, Sport, Lottery) that the user engages with. This approach ensures that the shares accurately reflect a user’s current preferences, leading to more precise and timely user categorization.
Available user properties
The following user propes are calculated daily for each active user and can be used in the segmentation for marketing and gamification activities.
core_fav_product_type - indicates the user’s preferred product category based on their engagement with Casino, Sports, and Lottery products. Possible values:
Casino Only
Sport Only
Lottery Only
More Casino, Less Sport
Casino and Sport
More Sport, Less Casino
Mixed Preferences
core_fav_product_casino_p - the percentage of the user’s engagement attributed to Casino products.
core_fav_product_sport_p - the percentage of the user’s engagement attributed to Sports products.
core_fav_product_lottery_p - the percentage of the user’s engagement attributed to Lottery products.
core_fav_game_type_top3 - indicates user’s top 3 most-played games types, ranked by importance (slot, table...)
core_fav_game_top3 - indicates user’s top 3 most-played or preferred games, ranked by importance
Example of usage
Let’s say an operator plans a new promotional campaign focused on Casino products. Using the "Favorite Product" feature, one can segment his/her users based on their preferences and engagement.
Case 1: Segmenting Users
Target Segment: Users categorized under core_fav_product_type 1 (Casino Only), 4 (More Casino, Less Sport), and 5 (Casino and Sport).
Why: These users clearly prefer or strongly engage with Casino products, making them the ideal audience for your Casino promotion.
Case 2: Enhancing Engagement
Track the campaign’s success by monitoring changes in core_fav_product_casino_p. If this percentage increases after the campaign, it indicates successful engagement.
Case 3: Product Adoption
Target players that have a strong focus on Casino games to participate in the Sport betting, build promotional campaigns for such users, address mini-games, missions and tournaments
Favorite Games & Sports
In the Games & Sports reports you can now clearly identify the top favored casino games by name or type over a specifically set window. The report is based on user preferences and engagement and it is based on the number of unique users, total bets, and share of favorite games. The data insight are big as you can also visualize the player activity for specific games or providers and see what the users focus on and what are the current trends.
User Category Distribution
This report shows the daily count of users in each category (core_fav_product_type). This chart will display the distribution of users across different categories—Casino Only, Sport Only, Lottery Only, More Casino Less Sport, Casino and Sport, More Sport Less Casino, and Mixed Preferences—over time.
This report allows you to monitor shifts in user preferences daily. By analyzing trends in the distribution, you can quickly identify changes in user behavior, such as an increase in users preferring Casino games or a decline in Lottery engagement. This insight allows real-time adjusting marketing strategies and promotional efforts, ensuring they remain aligned with current user interests and maximize engagement.
FAQ
Q: What factors are taken as the basis for Product preference calculation
We are taking turnover for casino, sport & lottery.
Q: What is the time interval of data used in the calculation
We take and recalculate data daily using 30-days of historical data
Q: Are there any weights used in the calculation
We use optimized exponential function delivering coefficients for each day, so that highest weight assigned to today's bets and very small to the bets made 30 days ago. This way we achieve smart bias towards players' recent activity.
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