Dynamic Recommendation Engine for Personalized Upsell
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Solution Overview
Problem
Traditional upselling methods are static, non-customer specific, and limited in number, leading to suboptimal success in increasing revenue as they do not account for individual customer preferences or current trends.
Innovation Solution
A data science-driven engine that uses historical and trend data to provide personalized upsell and add-on recommendations based on customer-specific factors, product popularity, and omni-channel sales data, including social media interactions, to dynamically suggest relevant products and services during transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static upsell offers are used, then implementation simplicity is maintained, but upsell effectiveness and revenue increase are limited
Solution Approach 1:
The patent transforms static upsell offers into dynamic, real-time recommendations by implementing a recommendation engine that processes customer data, transaction data, and product data to generate personalized upsell suggestions during transactions. The system adapts recommendations based on customer behavior patterns, purchase history, and current transaction context, making the upsell process dynamic rather than static.
Solution Approach 2:
The recommendation engine operates autonomously to generate and deliver upsell recommendations without requiring manual intervention. The system self-manages data processing, pattern recognition, and recommendation generation, freeing employees from manual upsell tasks while improving effectiveness through data-driven insights.
2Adaptability or versatility
If generic upsell offers are provided, then ease of implementation is maintained, but customer relevance and personalization are poor
Solution Approach 1:
The patent segments customers into distinct groups based on their behavior patterns, purchase history, and preferences. By dividing the customer base into segments, the system can provide personalized recommendations tailored to each segment's characteristics rather than using generic offers for all customers. This segmentation enables targeted upsell strategies that resonate with specific customer groups.
Solution Approach 2:
The system applies local quality by providing different upsell recommendations to different customer segments based on their specific characteristics. Each customer receives personalized recommendations relevant to their preferences, purchase history, and current transaction context, rather than uniform generic offers. This localized approach ensures high relevance and personalization for each customer interaction.
3Productivity
If limited number of upsell items are offered, then decision-making simplicity is maintained, but revenue optimization potential is lost
Solution Approach 1:
The recommendation engine generates a focused subset of the most relevant upsell recommendations from a larger pool of possible products. Rather than presenting all available products or relying on employee judgment to select a few items, the system uses data analytics to identify and present the optimal few recommendations with the highest probability of conversion, balancing comprehensiveness with ease of selection.
Data Source
AI summary
Various embodiments herein include an engine that uses data science, history, and trends to successfully determine and suggest the complimentary add on and upsell items that are tailored to at least some of the customer, current trends, product popularity, recommendations, and other such factors. One embodiment, in the form of a method, includes receiving, from a process involved in processing an open transaction, data representative of at least one of products and services that are subjects of the open transaction. The method further includes querying a recommendation engine for at least one of product and service recommendations based on the received data representative of products and services and receiving, in response to the query, at least one product or service recommendation. The method may then provide the at least one product recommendation to the process involved in processing the open transaction.