Food Features. These features include spend on food stores, food distributors etc. They are returned in the india/features/food endpoint.Definitions of these features generated by Pngme are:
| Feature | Definition | Use Case | Value Proposition | Response value |
|---|---|---|---|---|
| count_catering_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through catering. 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. • 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. | • This feature may be indicative of individuals who frequently spend on catering, possibly suggesting spends on life events like graduation, wedding. • 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_food_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through food events, where food events are broken down into general food spending, bar and restaurant, catering, food processor and distributor, food store, wine and spirit 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 food events. • 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. | • This feature 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_food_processor_and_distributor_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through food processor and distributor. 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. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different processing 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. | • This feature may be indicative of individuals who frequently spend on food distributors, possibly suggesting a business that gets food in bulk or cost efficiency. • 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_food_store_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through food stores. 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. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different processing 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. | 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_catering_debits _{t0_days}_{t1_days} | Total of catering debit transactions across depository accounts. The transactions 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 events. • 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. | • This feature may be indicative of individuals who frequently spend on catering, possibly suggesting spends on life events like graduation, wedding. • 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_food_debits _{t0_days}_{t1_days} | Total of food debit transactions across depository accounts. where food events are broken down into general food spending, bar and restaurant, catering, food processor and distributor, food store, wine and spirit events. The transactions 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 events. • 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. | • This feature 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} |
| sum_of_food_processor_and_distributor_debits _{t0_days}_{t1_days} | Total of food processor and distributor debit transactions across depository accounts. The transactions 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 processor and distributor events. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different processing 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. | • This feature may be indicative of individuals who frequently spend on food distributors, possibly suggesting a business that gets food products in bulk or cost efficiency. • 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_food_store_debits _{t0_days}_{t1_days} | Total of food store debit transactions across depository accounts. The transactions 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 stores. • Use data to offer personalized services such as budgeting and financial planning tools that track food events and compare pricing and packages from different processing 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. | 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} |