Loyalty Analytics System for Promotion Effectiveness Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturers and merchants face challenges in accurately calculating the return on investment for marketing efforts, particularly when advertisements are in the form of discounts or coupons, as it is difficult to determine the increase in sales directly attributable to these promotions.
Innovation Solution
A computerized method and system that utilize aggregated point-of-sale data to evaluate transaction activity after promotions, such as prepaid instruments or coupons, by tracking customer behavior and generating reports on subsequent transactions, including returns to the original merchant or other merchants, to assess the effectiveness of promotions in retaining customers and attracting new ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aggregated point-of-sale data is used to track customer behavior after promotions, then measurement precision of promotion effectiveness is improved, but device complexity increases
Solution Approach 1:
A data aggregation and analysis system acts as an intermediary between point-of-sale terminals and marketers. The system collects transaction data from multiple POS terminals, processes it to identify promotion redemptions and subsequent customer behavior, and generates reports on promotion effectiveness. This intermediary approach enables precise measurement without requiring direct complex integration at each individual POS terminal.
Solution Approach 2:
The analysis process is segmented into distinct stages: data collection at POS terminals, data aggregation at a central system, promotion identification through pattern matching, customer behavior analysis, and report generation. This segmentation allows each component to be optimized independently and reduces overall system complexity while maintaining measurement precision.
2Reliability
If detailed tracking of customer transactions is implemented, then reliability of promotion effectiveness data is improved, but loss of information increases
Solution Approach 1:
The system extracts only the specific information needed for promotion effectiveness analysis from transaction data. By focusing extraction on promotion identifiers, transaction timing, and merchant information rather than comprehensive customer profiles, the system maintains data reliability for analytical purposes while minimizing information loss and privacy concerns.
Solution Approach 2:
The system provides feedback mechanisms that allow merchants to see promotion effectiveness metrics without exposing individual customer data. Aggregated reports show promotion performance trends and customer behavior patterns at the group level, enabling data-driven decisions while maintaining customer information privacy and reducing information loss.
Data Source
AI summary
The invention provides the ability to evaluate transaction activity of customers following the redemption of a promotion, such as the redemption of a prepaid instrument, coupon, or the like. For example, the subsequent activity could be where customers shopped after redeeming a coupon. Examples of such reports that could be generated include the percentage of redeemers who returned to the same merchant or who went elsewhere. In some cases, customers could do both, or neither and the reports could indicate such activity. Further reports could show the amount spent for each return visit (both for those who return and go elsewhere), as well as the percentage who shop only with the merchant, and nowhere else.