Shopping

Shopping Features. These features include supermarket spends etc. They are returned in the india/features/shopping endpoint.Definitions of these features generated by Pngme are:

FeatureFeature DefinitionUse CaseValue PropositionReturn Value
count_shopping_events
_{t0_days}_{t1_days}
The number of SMS received indicating that the user engaged in
spending through shopping events, where shopping events are broken down into general shopping ,retail store,supermarket 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying ecommerce activities of customers.
• These features may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behaviour and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{int, null}
count_supermarket_events
_{t0_days}_{t1_days}
The number of SMS received indicating that the user engaged in
spending at supermarkets. 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying ecommerce activities of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behaviour and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{int, null}
count_ecommerce_platform_events
_{t0_days}_{t1_days}
The number of SMS received indicating that the user engaged in
spending through ecommerce platform 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying ecommerce activities of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{int, null}
count_retail_store_events
_{t0_days}_{t1_days}
The number of SMS received indicating that the user engaged in
spending retail store. 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.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behaviour and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{int, null}
sum_of_shopping_debits_{t0_days}_{t1_days}The sum of debits where user engaged in
spending through shopping events, where shopping events are broken down into general shopping ,retail store,supermarket . 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying shopping spends of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behavior and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{float, null}
sum_of_supermarket
_debits_{t0_days}_{t1_days}
The sum of debits where user engaged in
spending at a supermarket. 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying shopping spends of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behavior and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{float, null}
sum_of_ecommerce_platform
_debits_{t0_days}_{t1_days}
The sum of debits where user engaged in
spending through ecommerce platform. 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying shopping spends of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behavior and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{float, null}
sum_of_retail_store
_debits_{t0_days}_{t1_days}
The sum of debits where user engaged in
spending through retail store, 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 satisfaction and loyalty by offering customers access to exclusive
shopping platform events, such as flash sales, early access events, and
member-only discounts, rewarding their loyalty and encouraging repeat purchases.
• Enhance customer engagement and retention by offering tailored product
recommendations, enhancing their shopping experience and increasing the
likelihood of purchase
• Improve data quality and reliability by using SMS data as a source of truth for
verifying shopping spends of customers.
• This feature may be indicative of an individual’s spending capacity
across a broad spectrum of goods and services, offering insights
into their overall financial behavior and preferences.
• Understanding a user’s activity related to shopping events can help
tailor marketing strategies and develop products that cater
to this specific customer segment.
{float, null}