Biometric Purchase Ordering for Personalized In-Store Retail
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Solution Overview
Problem
Retail customers in larger cities and crowded urban areas desire increased efficiency in placing orders in brick and mortar stores, while also seeking a personalized 'small town' feel from local retail stores.
Innovation Solution
A system comprising a customer device, key fob, merchant device, and financial service provider device, utilizing biometric identification and customer preference data to facilitate quick in-store purchases through a notification system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional in-store ordering methods are used, then customers can place orders, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-storing customer preference data, product information, and order histories in databases before the customer enters the store. When the customer approaches the store, the system automatically retrieves relevant information and prepares the ordering interface, enabling rapid transaction completion without requiring customers to review or re-enter information.
Solution Approach 2:
The system enables self-service ordering by automatically identifying customers through biometric authentication, retrieving their preference data, and presenting personalized ordering options without requiring staff assistance. The automated ordering system processes transactions independently, reducing both customer effort and time required.
2Productivity
If automated ordering systems are implemented, then purchase efficiency increases, but the personalized touch is reduced
Solution Approach 1:
The system applies local quality by customizing the ordering experience for each individual customer based on their stored preference data, purchase history, and personalizations. While the system is automated, it adapts the interface and recommendations to match each customer's specific preferences, maintaining a personalized touch despite the automated process.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously learn from customer interactions and adjust future ordering experiences accordingly. By analyzing customer behavior patterns and preferences, the system refines its automated recommendations to better match individual customer tastes, ensuring personalization improves over time.
Data Source
AI summary
Systems and methods for in-store purchases are provided. An exemplary method may include receiving by a customer device associated with a customer, customer data including customer preference data. The method may include storing the received customer data and identifying a merchant at a location of the customer. The method may also include determining a customer order for the identified merchant based on the customer preference data. Further, the method may include transmitting a notification to a merchant device associated with the identified merchant, the notification including the determined customer order.