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Offline Data-Driven Recommender Systems for Improving Small Business Marketing Strategies.

Hwijae Son1, Sung Woong Cho2, Hyung Ju Hwang3

  • 1Department of Mathematics, Konkuk University, Gwangjin-gu, The Republic of Korea.

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This study introduces a novel recommender system using offline data to identify customers likely to use coupons, boosting conversion rates (CR) and cutting marketing costs for small businesses. The system effectively tackles challenges like data sparsity and cold-start problems.

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Area of Science:

  • Computer Science
  • Marketing Analytics
  • Business Intelligence

Background:

  • Recommender systems enhance user engagement across various sectors.
  • Small businesses face high cost per action (CPA) and low conversion rates (CR) with online marketing.
  • Personalized marketing campaigns are key for identifying high-value customers.

Purpose of the Study:

  • To develop a novel recommender system for small businesses to reduce marketing costs.
  • To leverage offline interaction data for identifying customers likely to use discount coupons.
  • To improve conversion rates (CR) and decrease cost per action (CPA) in marketing efforts.

Main Methods:

  • Utilizing offline interaction data, including store-level coupon and point log data.
  • Implementing tailored data augmentation techniques to address cold-start problems and data sparsity.
  • Evaluating system performance using metrics such as CPA, CR, and root mean squared error.

Main Results:

  • The proposed recommender system significantly outperforms conventional online marketing platforms.
  • The system effectively identifies customers prone to using discount coupons, thereby increasing CR.
  • Demonstrated reduction in marketing costs through improved efficiency.

Conclusions:

  • Incorporating offline data with appropriate augmentation is valuable for cost-effective marketing.
  • The novel recommender system offers a viable solution for small businesses struggling with online marketing expenses.
  • Data augmentation techniques are crucial for overcoming challenges in offline recommender systems.