EP-4621693-B1 - PERSONALIZED PROMOTION OFFER GENERATION BY UNDERSTANDING CUSTOMER BUYING INTENT TO MAXIMIZE RETURN ON INVESTMENT

Inventors

• CHAUDHURY, ABHISHEK NARAYAN
• KANDATH, Lakshmidevi
• RAMACHANDRASEKAR, Abhinaya
• MUSTAFA NAINA, MAHMOOD ZAYEEM
• SETH, AMIT KUMAR
• Munshi, Syed Abdus Samy

Dates

Publication Date

20260513

Application Date

20250317

Claims (15)

1. A processor implemented method (200) for promotion offer generation, the method comprising: generating (202), via one or more hardware processors, merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data; obtaining (204), via one or more hardware processors, a promotion percentage based on a customer benefit received by one or more customers among the plurality of customers for one or more transacted products if the promotion redemption flag is set for the product id associated with the customer id, wherein the customer benefit is difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products; generating (206), via one or more hardware processors, a plurality of customer segments within the plurality of customers based on one of (i) Recency, Monetary, Frequency (RFM), and (ii) Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data; binning (208), via one or more hardware processors, a plurality of promotion offers associated with the transaction data into a plurality of bins by grouping of the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog; predicting (210), via one or more hardware processors, promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying a pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories; extracting (212), via one or more hardware processors, a second set of promotion associated features derived from the predicted promotion offer redemption probability for each of the customer segment; generating (214), via one or more hardware processors, an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that maximizes yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and allocating (216), via one or more hardware processors, the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of the RFM or the AOV, and (ii) a category score representation number of unique categories products the customer has purchased, wherein the customer with lower RFM or AOV score is allocated higher promotion bins of promotion offers.
2. The method of claim 1, wherein allocating the one or more offers is based on one or more rules set by the entity, wherein the rules comprise (i) allocating a single promotion offer is to a customer from a collection of multiple eligible offers, wherein the single promotion offer is selected based on a highest inventory, and (ii) allocating one or more promotion offer based on a brand of the product the customer has transacted using category-based offers.
3. The method of claim 1, comprises generating gift cards based on percentage of the allocated promotion offers and a minimum spend requirement from the customer, and an expected AOV uplift set by the entity.
4. The method of claim 1, wherein the first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories comprises: total purchase for a category and a customer segment, total promotion offer purchase of products for the category-customer segment , the promotion price, number of purchases of products in the customer segment, number of promotion offer purchases of the products in the customer segment, the average actual price in the customer segment, customer segment discount purchase, and maximum promotion offer purchase
5. The method of claim 1, wherein the second set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories comprises: incremental margin, incremental yield margin ratio (ymr), number of customers, ymr sum, constant, inverted constants, number of maximum promotion offers, and number of promotion offers that can be allocated to a customer segment-promotion bin-category combination.
6. The method of claim 1, wherein the plurality of constraints comprise: maximize yield based on budget constraints of the entity; a loss margin, indicating expected loss incurred due to the difference between actual price and the promotion price promotion is capped at a predetermined value; and number of promotion offers for each product in the customer segment not to exceed the number of customers in the customer segment.
7. A system (100) for promotion offer generation, the system (100) comprising: a memory (102) storing instructions; one or more Input/Output (I/O) interfaces (106); and one or more hardware processors (104) coupled to the memory (102) via the one or more I/O interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: generate merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data; obtain a promotion percentage based on a customer benefit received by one or more customers among the plurality of customers for one or more transacted products if the promotion redemption flag is set for the product id associated with the customer id, wherein the customer benefit is difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products; generate a plurality of customer segments within the plurality of customers based on one of Recency, Monetary, Frequency (RFM) and Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data; bin a plurality of promotion offers associated with the transaction data into a plurality of bins by grouping of the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog; predict promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying a pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories; extract a second set of promotion associated features derived from the predicted promotion offer redemption probability for each of the customer segment; generate an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that that maximize yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and allocate the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of RFM or the AOV, and (ii) a category score representation number of unique categories products the customer has purchased, wherein the customer with lower RFM or AOV score is allocated higher promotion bins of promotion offers.
8. The system of claim 7, wherein allocating the one or more offers is based on one or more rules set by the entity, wherein the rules comprise (i) allocating a single promotion offer is to a customer from a collection of multiple eligible offers, wherein the single promotion offer is selected based on a highest inventory, and (ii) allocating one or more promotion offer based on a brand of the product the customer has transacted using category-based offers.
9. The system of claim 7, comprises generating gift cards based on percentage of the allocated promotion offers and a minimum spend requirement from the customer, and an expected AOV uplift set by the entity.
10. The system of claim 7, wherein the first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories comprises: total purchase for a category and a customer segment, total promotion offer purchase of products for the category-customer segment, the promotion price, number of purchases of products in the customer segment, number of promotion offer purchases of the products in the customer segment, the average actual price in the customer segment, customer segment discount purchase, and maximum promotion offer purchase.
11. The system of claim 7, wherein the second set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories comprises: incremental margin, incremental yield margin ratio (ymr), number of customers, ymr sum, constant, inverted constants, number of maximum promotion offers, and number of promotion offers that can be allocated to a customer segment-promotion bin-category combination.
12. The system of claim 7, wherein the plurality of constraints comprise: maximize yield based on budget constraints of the entity; a loss margin, indicating expected loss incurred due to the difference between actual price and the promotion price promotion is capped at a predetermined value; and number of promotion offers for each product in the customer segment not to exceed the number of customers in the customer segment.
13. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: generating merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data; obtaining a promotion percentage based on a customer benefit received by one or more customers among the plurality of customers for one or more transacted products if the promotion redemption flag is set for the product id associated with the customer id, wherein the customer benefit is difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products; generating a plurality of customer segments within the plurality of customers based on one of (i) Recency, Monetary, Frequency (RFM), and (ii) Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data; binning a plurality of promotion offers associated with the transaction data into a plurality of bins by grouping of the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog; predicting promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying a pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories; extracting a second set of promotion associated features derived from the predicted promotion offer redemption probability for each of the customer segment; generating an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that maximizes yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and allocating the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of the RFM or the AOV, and (ii) a category score representation number of unique categories products the customer has purchased, wherein the customer with lower RFM or AOV score is allocated higher promotion bins of promotion offers.
14. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein allocating the one or more offers is based on one or more rules set by the entity, wherein the rules comprise (i) allocating a single promotion offer is to a customer from a collection of multiple eligible offers, wherein the single promotion offer is selected based on a highest inventory, and (ii) allocating one or more promotion offer based on a brand of the product the customer has transacted using category-based offers.
15. The one or more non-transitory machine-readable information storage mediums of claim 13, comprises generating gift cards based on percentage of the allocated promotion offers and a minimum spend requirement from the customer, and an expected AOV uplift set by the entity.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application claims priority to Indian application no. 202421020543, filed on March 19, 2024. TECHNICAL FIELD The embodiments herein generally relate to the field of data science for automated promotion offer generation and, more particularly, to a method and system for personalized promotion offer generation by understanding customer buying intent to maximize Return On Investment (ROI). BACKGROUND Availability of consumer data, and rapid advancement data analytics techniques, has enabled entities, for example retailers to acquire insights on customers' shopping behavior and preferences and provide personalized services with the aim of generating more sales. Promotion offers is one of the strategies to boost the sales. In the world of retail, the competition between different stores to promote their merchandise is very much necessary to increase their profit margin. In the objective to increase profit margin, one of the very important ways is to decrease the churn rate and increase conversions, which can be attained by promotion via gift cards and different offers. Even though there should be a positive correlation between offers and customer buying habits, it is also observed that even though companies spend 25% of their revenue on promotions, it results in a very little increase in conversions. The reason behind this is that for customers there are too many offers with less relevance and personalization resulting in poor ROI (return on investment). Promotions are mainly classified into two categories: 1. Segment based: Manual, rule-based promotions by tiers or segments resulting in lack of relevance to many. 2. Siloed: Channel centricity as there are different promotion/offer systems for different channels (Online, Store). This gap not only is affecting the retailers in terms of churn rate and yield margin but also customers are dissatisfied due to relevance. Generating only the relevant offers instead of allocating offers to customers is more critical to improve yield margin in turn ROI for the retailer. The existing solutions consider the offer allocation mainly in terms of rules or loyalty programs. But the solutions are unable to bridge the gap between what a customer wants and what the customer can buy. Normal paradigms of customer segmentation is not effective in comparison to RFMbased segmentation, wherein RFM analysis is a model for segmenting customers based on three parameters that define their purchase habits: Recency (Number of days since last purchase), Frequency (Number of purchases for each customer), and Monetary value (Total value of all purchases for each customer). Another recent work titled "Price discounts and personalized product assortments under multinomial logit choice model: A robust approach" analyzes customer behavior data. It jointly focusses on offer and an assortment of products. However, the offers or price discounts generated are non-personalized price discounts for each product and then upon the arrival of customers, the retailer offers a personalized assortment to each type of customer. The non-personalized price discount lacks the relevance to customer, , also has reduced effectiveness, and lower customer engagement. Based on this assortment, the customer then makes a purchase decision according to the Multinomial logit choice model. This approach focusses on the customer's want but may not ensure customer buying intent, thus rate of conversion of discount to sales is not guaranteed. US patent application US2023/306471 is further background art. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for personalized promotion offer generation is provided. The method includes generating merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, an order id and appended with a promotion redemption flag of each of a plurality of transacted products bought by a plurality of customers from among the plurality of products. Further, the method comprises obtaining a promotion percentage based on a customer benefit received by one or more customers among the plurality of customers for one or more transacted products if the promotion redemption flag is set for the product id associated with the customer id, wherein the customer benefit is difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products. Further, the method comprises generating a plurality of customer segments within the plurality