Buying-Intent Promotion Offers With RFM Segmentation

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Solution Overview

Problem

Existing promotion systems fail to bridge the gap between customer preferences and purchasing behavior, resulting in irrelevant offers and low Return On Investment (ROI) due to non-personalized price discounts and lack of customer engagement, leading to increased churn rates and reduced profit margins.

Innovation Solution

A method and system for personalized promotion offer generation using RFM and AOV scoring to create customer segments, predicting offer redemption probabilities, and optimizing offer allocation through a linear programming model to maximize yield, while considering inventory and customer behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If companies spend 25% of their revenue on promotions, then promotion coverage increases, but Return on Investment decreases due to lack of personalization

Engineering Contradiction:

Improvepromotion coverageVSAvoidReturn on Investment

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The patent segments customers into distinct groups based on RFM (Recency, Frequency, Monetary) analysis and AOV (Average Order Value) scoring. This segmentation enables targeted promotion strategies for different customer segments, ensuring that promotion resources are allocated to customers most likely to respond, thereby improving ROI while maintaining effective promotion coverage.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by providing different promotion offers to different customer segments based on their specific characteristics. Instead of uniform promotions, each segment receives tailored offers aligned with their purchasing behavior and preferences, making promotions more relevant and effective for each local group while optimizing overall investment efficiency.

Inventive Principle:

Principle #3Local quality

2Ease of operation

If segment-based manual rule promotions are used, then implementation simplicity is maintained, but offer relevance to customers decreases

Engineering Contradiction:

Improveimplementation simplicityVSAvoidoffer relevance

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent implements self-service by enabling the system to automatically generate, allocate, and distribute personalized promotion offers without manual intervention. The automated system uses RFM and AOV metrics to dynamically create relevant offers for each customer segment, maintaining ease of operation through automation while significantly improving offer relevance compared to manual rule-based approaches.

Inventive Principle:

Principle #25Self-service

3Speed

If non-personalized price discounts are offered, then offer generation speed is maintained, but customer engagement and conversion effectiveness decrease

Engineering Contradiction:

Improveoffer generation speedVSAvoidconversion effectiveness

Core Design Contradiction:

SpeedVSProductivity

Solution Approach 1:

The patent applies preliminary action by pre-calculating RFM scores and AOV metrics for all customers before promotion campaigns begin. This pre-processing enables the system to quickly generate personalized offers during campaigns without real-time computation delays, maintaining offer generation speed while significantly improving conversion effectiveness through personalized recommendations.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If RFM and AOV based customer segmentation is implemented, then offer personalization improves, but system complexity increases

Engineering Contradiction:

Improveoffer personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces complex manual analysis and decision-making processes with automated computational systems. Machine learning models and algorithms automatically calculate RFM scores, determine AOV, segment customers, and generate personalized offers, reducing system complexity from a manual operations perspective while enabling sophisticated personalization that would be infeasible through manual methods.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250299218A1Personalized promotion offer generation by understanding customer buying intent to maximize return on investment

Publication Date: 2025.09.25 TATA CONSULTANCY SERVICES LTD

AI summary

Unlike rule based offer generation in state of the art, a method and system for personalized promotion offer generation by understanding customer buying intent to maximize return on investment is disclosed. Product and associated customer details are obtained from product catalog and transaction data. RFM score is computed based on transaction data to create customer segments capturing demographics behavioral data for customer. A tree based ensemble classifier predicts promotion offer redemption probability for each customer segment. The linear optimization model assigns an optimal number of promotion offers, to attain an increasing profit margin, thereby generating offers as per relevance. Budget constraint is applied so that maximum revenue can be attained. The final per customer allocation of the offer is based on different rules of personalization as required from the industry. The eligibility of getting gift card is computed by setting threshold of what customer should spend to get gift card.