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The use of recommender and decision support systems for sales personalization in a mobile application

机译：在移动应用程序中使用推荐和决策支持系统进行销售个性化

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摘要

In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs.udBy defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales.udThis information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations.udWith the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns.ududOn the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions. ud
机译：在购物过程中，当今的用户面临着大量信息以及各种各样的产品和服务，这使他们无法就在特定时间和特定时间购买他们实际需要的产品和服务做出理性的决定。 ud通过定义和确认用户所面临的问题，我们从分析，设计，开发，测试和实施用于个人化销售的信息和推荐系统开始。 ud此信息系统基于业务模型进行操作，在该业务模型中，用户将获得特别的优惠或忠诚度积分，以换取提供重要的反馈。缺乏有关客户，他们的习惯，将来的购买和过去的经验的定性数据是阻止公司实施有效的个性化的关键因素之一。因此，即使是实时，公司也无法回答有关营销，销售和业务运营的重要问题。 ud借助推荐和决策系统并通过处理大量智能数据，我们可以为客户提供个性化的产品和服务从而加速并增加销售量，同时改善用户和购物体验。在信息和推荐系统的分析和开发中，我们提出了一个假设，即通过使用有关用户需求，需求，过去经验和未来购买的定性数据，我们可以为用户提供更多个性化的特殊优惠。个性化还可以提高特价商品视图和相关响应之间的CTR（点击率）转换，或者更确切地说，可以执行销售活动。 ud ud基于已开发并经过测试的推荐系统，我们得出的结论是对于我们而言，最合适的解决方案是使用混合推荐技术，该技术根据不同类型的情况，结合其他决策规则和条件来实现CF或CB过滤方法。 ud

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    Kosec Damjan;

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  • 年度 2016
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