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Applying ranges in a natural language query is essential for grouping data into logical segments, making it easier to analyse trends and patterns across different categories. Ranges are particularly useful when visualising data through bar charts or pie charts, as they help break down large datasets into more digestible and comparable groups. This approach is especially valuable when evaluating customer segments, such as age groups, spending behavior, or activity levels.
For example, queries like "Show the total active sportsbook players this year and age range (0 to 25, 26 to 35, 36 to 45, 46 to 60, more than 60)" allow for a clearer understanding of player demographics, enabling better decision-making and targeted strategies.
Other examples
list total sportsbook ngr and age range (0 to 25, 26 to 35, 36 to 45, 46 to 60, more than 60)
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list total sportsbook bonuses and age range (from 0 to 25, 26 to 35, 36 to 45, 46 to 60, 61+)
results in:
list total bingo bonuses and age range (from 0 to 25, 26 to 35, 36 to 45, 46 to 60, 61+)
results in:
