Restaurant Features. These include debits to general bar and restaurants, and fast food chain restaurants like kfc, dominos pizza etc.They are returned in the india/features/restaurants endpoint.Definitions of these features generated by Pngme are:
| Feature | Features Available | Feature Definition | Use Case | Value Proposition | Return Value |
|---|---|---|---|---|---|
| count_bar_and_restaurants_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through bar and restaurants. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer service and build stronger relationships with customers, thereby increasing customer retention by understanding customers’ spending habits and preferences on bar and restaurants. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different catering or food precessing services, helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying food events of customers. | • These features may be indicative of individuals who frequently dine out, possibly suggesting a personal or professional interest in the food and drink industry • Understanding a user’s activity related to food events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_[fastfood_events _categories]_events _{t0_days}_{t1_days} | count_burger_king_events_{t0_days}_{t1_days} count_dominos_pizza_events_{t0_days}_{t1_days} count_kfc_events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through fast food restaurants, where food events are broken down into general kfc, dominos pizza, burger king events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer service and build stronger relationships with customers, thereby increasing customer retention by understanding customers’ spending habits and preferences on fast food restaurant. • Use data to offer personalized services such as budgeting and financial planning tools that track fast food restaurant events, helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying food events of customers. | • These features may be indicative of individuals who frequently dine out • Understanding a user’s activity related to food events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| sum_of_bar_and_restaurants_debits _{t0_days}_{t1_days} | The amount of money spent through bar and restaurants bills. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer service and build stronger relationships with customers, thereby increasing customer retention by understanding customers’ spending habits and preferences on bar and restaurants. • Use data to offer personalized services such as budgeting and financial planning tools that track bar and restaurant events and compare pricing and packages from different restaurants helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying food events of customers. | • These features may be indicative of individuals who frequently dine out, possibly suggesting a personal or professional interest in the food and drink industry • Understanding a user’s activity related to food events can help tailor marketing strategies and develop products that cater to this specific customer segment. | ||
| sum_of_[fastfood categories]_debits _{t0_days}_{t1_days} | sum_of_burger_king_debits_{t0_days}_{t1_days} sum_of_dominos_pizza_debits_{t0_days}_{t1_days} sum_of_kfc_debits_{t0_days}_{t1_days} | The sum of SMS received indicating that the user engaged in spending through food spends, where food spends are broken down into general kfc,burger king, dominos pizza. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer service and build stronger relationships with customers, thereby increasing customer retention by understanding customers’ spending habits and preferences on food spends. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different fast food restaurants, helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying food spends of customers. | • These features may be indicative of individuals who frequently dine out, possibly suggesting a personal or professional interest in the food and drink industry. • Understanding a user’s activity related to food events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float,null} |