Cross-Channel Promotion Platform for Real-Time Ad Targeting
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Solution Overview
Problem
Current retail systems face challenges in delivering relevant advertisements to customers in real-time, often overwhelming them with irrelevant ads, which wastes network bandwidth and processor resources, and fails to effectively leverage customer attention at the point of decision.
Innovation Solution
A cross-channel promotion platform that uses customer data from mobile, online, and in-store channels to provide personalized promotions by identifying customer interests and preferences, allowing suppliers to bid on ad placement, and delivering targeted offers through various channels, including mobile devices and in-store beacons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large selection of advertisements is sent to ensure at least one relevant ad reaches the consumer, then the probability of reaching the consumer improves, but the customer is inundated with irrelevant advertisements and network bandwidth is consumed
Solution Approach 1:
The system performs preliminary actions by collecting customer data, analyzing preferences, and pre-segmenting customer profiles before ad delivery. This allows the system to predict which ads will be relevant to which customers in advance, eliminating the need to send large volumes of ads blindly and reducing network bandwidth consumption while maintaining delivery reliability.
Solution Approach 2:
The system enables self-service through automated customer profile analysis and ad matching algorithms that automatically identify relevant advertisements without manual intervention. This automated matching process ensures high ad delivery reliability while minimizing the volume of irrelevant ads sent, thereby conserving network bandwidth.
2Reliability
If a large selection of advertisements is sent to ensure at least one relevant ad reaches the consumer, then the probability of reaching the consumer improves, but processor resources are wasted as each advertisement still needs to be handled
Solution Approach 1:
The system performs preliminary filtering and matching of advertisements to customer profiles before delivery. By pre-analyzing customer preferences and pre-selecting relevant ads, the system reduces the number of ads that need to be processed by customer devices, thereby maintaining delivery reliability while conserving processor resources.
Solution Approach 2:
The system extracts and isolates only the most relevant advertisements from the full inventory based on customer profile analysis. This extraction process removes irrelevant ads from the delivery pool, ensuring that customers receive only pertinent advertisements, which maintains effectiveness while reducing processor resource consumption.
3Adaptability or versatility
If traditional matchmaking tools like shelf placement and coupons are used, then the retailer can influence customer purchasing, but the services offered to enhance matchmaking are limited
Solution Approach 1:
The system implements multi-functionality by integrating multiple matchmaking capabilities into a single platform. It combines shelf placement optimization, coupon distribution, personalized ad delivery, and real-time customer behavior analysis into one unified system. This universal platform enhances matchmaking versatility without proportionally increasing complexity, as all functions operate through a common architecture.
Solution Approach 2:
The system introduces an intermediary layer between the retailer and customer that processes and coordinates multiple matchmaking functions. This intermediary platform manages shelf placement data, coupon effectiveness, customer profiles, and ad delivery, coordinating these elements to enhance overall matchmaking capability while maintaining manageable system complexity through centralized control.
Data Source
AI summary
A platform for providing real-time promotions determines a customer interested in a product or service. The platform identifies customer information associated with the customer, as well as a set of products of interest to the customer. The platform identifies a supplier or advertiser associated with each of the products and receives promotion bids from one or more of the suppliers or advertisers. The platform generates a score for each of the received promotion bids, selects at least one of the promotion bids for transmission to the mobile device, and transmits the selected at least one promotion bids to the mobile device. The platform tracks customer behavior associated with the at least one promotion bids and provides the tracked behavior information back to the suppliers or advertisers.