WO2017124418A1 - 自动推荐优惠劵的方法以及推荐*** - Google Patents

自动推荐优惠劵的方法以及推荐*** Download PDF

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Publication number
WO2017124418A1
WO2017124418A1 PCT/CN2016/071676 CN2016071676W WO2017124418A1 WO 2017124418 A1 WO2017124418 A1 WO 2017124418A1 CN 2016071676 W CN2016071676 W CN 2016071676W WO 2017124418 A1 WO2017124418 A1 WO 2017124418A1
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user
label
consumption
unit
recording
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PCT/CN2016/071676
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French (fr)
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赵政荣
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赵政荣
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Priority to PCT/CN2016/071676 priority Critical patent/WO2017124418A1/zh
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention belongs to the field of the Internet, and in particular, to a method and a recommendation system for automatically recommending a discount.
  • Big data analysis refers to the analysis of large-scale data. Big data can be summarized as 5 V, large amount of data (Volume) , Velocity, Variety, Value, Veracity. Big data as the hottest IT nowadays Industry vocabulary, the resulting data warehousing, data security, data analytics, data mining, etc. around the business value of big data The use of the industry has gradually become the focus of profits that the industry is vying for. With the advent of the era of big data, big data analysis has emerged.
  • the present invention needs to provide a kind of situation that the current advertisement delivery is not targeted and cannot be accurately delivered according to the user's consumption habits.
  • the consumer volume can be sent more targetedly, and the user can also quickly grasp the latest consumption information that suits their consumption habits.
  • a method of automatically recommending a discount comprising the following steps:
  • a coupon with the consumer habit tag is sent to the user.
  • An embodiment of the present invention further provides a recommendation system, where the recommendation system includes:
  • Recording unit uploading unit, analyzing unit, sending unit, wherein:
  • a recording unit located locally, for recording a type label corresponding to the consumer product, and recording a user-purchased consumer product type label;
  • the uploading unit is located at the local end, and the input end thereof is connected to the output end of the recording unit, and is configured to upload the user-purchased consumer product type label to the cloud;
  • the analyzing unit is located in the cloud, and is configured to determine, according to the number of repetitions of the label, a label having the highest number of repetitions as a consumption habit label of the user;
  • the sending unit is located in the cloud, and the input end thereof is connected to the output of the analyzing unit, and is configured to send the coupon with the consumption habit tag to the user.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted, and the user can also quickly grasp the latest consumption information that suits his consumption habits.
  • FIG. 1 is a schematic flow chart of a method for automatically recommending a discount according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a recommendation system according to an embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of a method for automatically recommending a discount according to an embodiment of the present invention. For the convenience of description, only parts related to the embodiment of the present invention are shown.
  • step S100 the type label corresponding to the consumer product is recorded, and the user purchases the consumer product type label.
  • the technology is prior art. At present, the user will consume the card through a bank card or a credit card, or the cash will also store the consumption record at the counter cash register.
  • step S101 the user purchases a consumer product type tag is uploaded to the cloud.
  • the consumption type label can record the user's consumption more carefully, such as 'Sichuan hot pot', 'cycling supplies' and other types.
  • step S102 the tag with the highest number of repetitions is determined as the user's consumption habit tag according to the number of repetitions of the tag.
  • step S103 a coupon having the consumption habit tag is sent to the user.
  • the user recorded by the above-mentioned record purchases the consumer product type label, and according to the number of repetitions of the label, the label with the most repetition is determined as the user's consumption habit label, such as what brand the user likes to buy or the price, and sends the same to the user. After the consumption of the label, the user also enjoys the corresponding discount while the advertisement is being advertised.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted, and the user can also quickly grasp the latest consumption information that suits his consumption habits.
  • FIG. 2 is a schematic structural diagram of a recommendation system according to an embodiment of the present invention, where the recommendation system includes:
  • the recording unit 21 is located at the local location and is used for recording the type label corresponding to the consumer product, and recording the user-purchased consumer product type label;
  • the uploading unit 22 is located at the local location, and its input terminal and recording unit 21 The output end is connected to upload the user-purchased consumer product type label to the cloud;
  • Analysis unit 23 In the cloud, used to determine the label with the most repetitions as the user's consumption habit label according to the number of repetitions of the label;
  • the sending unit 24 is located in the cloud, and its input end and analysis unit 23 The output is connected to send a coupon with the consumption habit tag to the user.
  • the working principle is: the user records the type label corresponding to the consumer product in the recording unit 21, records the user purchase consumer type label, and uploads the unit. 22 uploading the user purchase consumer type label to the cloud, and the analyzing unit 23 determines the label with the most repetition as the user's consumption habit label, and the sending unit 24 A coupon with the consumer habit tag is sent to the user.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted, and the user can also quickly grasp the latest consumption information that suits his consumption habits.

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

一种自动推荐优惠劵的方法以及推荐***，属于互联网领域，所述方法包括：记录消费品对应的类型标签，记录用户购买消费品类型标签（S100）；向云端上传所述用户购买消费品类型标签（S101）；根据所述标签的重复次数，将重复次数最多的标签确定为用户的消费习惯标签（S102）；向用户发送具有所述消费习惯标签的优惠券（S103）。通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息。

Description

自动推荐优惠劵的方法以及推荐*** 技术领域
本发明属于互联网领域，尤其是涉及 一种自动推荐优惠劵的方法以及推荐*** 。
背景技术
海景现房，小户型精装修，详情来电致电 ' 、 ' 现有宝马、奔驰、奥迪***车，最低 10 万 '…… 翻看您的手机，相信绝大多数人的短信里充斥着此类卖房、卖车、打折促销甚至开***、办贷款的垃圾广告短信，有些是以手机号发来，而绝大多数都是 '106' 开头的商业号码。这些短信，九成以上都是 ' 被接收 ' 。
上述这些垃圾广告短信及目前大多数广告都存在着一个问题，无法针对用户消费习惯进行投放，因此为了扩大覆盖面，只能想尽办法，从过短信，网络，户外广告牌等等大范围覆盖，成本高收益小。特别对于类似强行发送短信这类广告，效果适得其反。
大数据分析是指对规模巨大的 数据 进行分析。大数据可以概括为 5 个 V ， 数据量大 (Volume) 、速度快 (Velocity) 、类型多 (Variety) 、 Value （价值）、真实性 (Veracity) 。大数据作为时下最火热的 IT 行业的词汇，随之而来的 数据仓库 、 数据安全 、数据分析、 数据挖掘 等等围绕大数据的 商业价值 的利用逐渐成为行业人士争相追捧的利润焦点。随着大数据时代的来临，大数据分析也应运而生。
进入大数据时代，网络平台样式和消费者购物***台信息、移动终端、 搜索 引擎等多个平台去接触消费者，挖掘数据，从而进行综合评估和分析
综上，需要针对当前广告投放针对性不强，无法根据用户消费习惯精确投放的情况，需要提供一种 通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息 。
 技术问题
本发明实施针对当前广告投放针对性不强，无法根据用户消费习惯精确投放的情况，需要提供一种 通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息 。
技术解决方案
本发明是这样实现的： 一种自动推荐优惠劵的方法 ，包括以下步骤：
记录消费品对应的类型标签，记录用户购买消费品类型标签；
向云端上传所述用户购买消费品类型标签；
根据所述标签的重复次数，将重复次数最多的标签确定为用户的消费习惯标签 ;
向用户发送具有所述消费习惯标签的优惠券。
本发明实施例还提供了推荐***，所述推荐***包括：
记录单元，上传单元，分析单元，发送单元，其中：
记录单元，位于本地，用于记录消费品对应的类型标签，记录用户购买消费品类型标签；
上传单元，位于本地，其输入端与记录单元输出端连接，用于向云端上传所述用户购买消费品类型标签；
分析单元，位于云端，用于根据所述标签的重复次数，用于将重复次数最多的标签确定为用户的消费习惯标签；
发送单元，位于云端，其输入端与分析单元输出端连接，用于向用户发送具有所述消费习惯标签的优惠券。
 有益效果
该发明通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息。
附图说明
图 1 是本发明实施例提供的一种自动推荐优惠劵的方法 的流程示意图 ；
图 2 是本发明实施例提供的推荐***的结构示意图。
 本发明的实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
图 1 是本发明实施例提供的一种自动推荐优惠劵的方法 的流程示意图 ，为了便于说明，只示出了与本发明实施例相关的部分。
在步骤 S100 中 , 记录消费品对应的类型标签，记录用户购买消费品类型标签。
该技术为现有技术，目前用户消费时会通过银行卡、******，或者现金也会在柜台收款机储存消费记录。
在步骤 S101 中 , 向云端上传所述用户购买消费品类型标签。
消费类型标签可以更加细致的记录用户消费情况，例如'四川火锅'，'骑行用品'等类型。
在步骤 S102 中，根据所述标签的重复次数，将重复次数最多的标签确定为用户的消费习惯标签。
在步骤 S103 中，向用户发送具有所述消费习惯标签的优惠券。
通过上述记录的用户购买消费品类型标签，并根据所述标签的重复次数，将重复次数最多的标签确定为用户的消费习惯标签，例如用户喜欢买什么牌子或者价位的物品，并向用户发送具有同样标签的消费劵，商家打了广告的同时，用户也享受了相应的折扣。
该发明通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息。
图 2 是本发明实施例提供的推荐***的结构示意图，所述推荐***包括：
记录单元 21 ，上传单元 22 ，分析单元 23 ，发送单元 24 ，其中：
记录单元 21 ，位于本地，用于记录消费品对应的类型标签，记录用户购买消费品类型标签；
上传单元 22 ，位于本地，其输入端与记录单元 21 输出端连接，用于向云端上传所述用户购买消费品类型标签；
分析单元 23 ，位于云端，用于根据所述标签的重复次数，用于将重复次数最多的标签确定为用户的消费习惯标签；
发送单元 24 ，位于云端，其输入端与分析单元 23 输出端连接，用于向用户发送具有所述消费习惯标签的优惠券。
其工作原理是：用户在记录单元 21 记录消费品对应的类型标签，记录用户购买消费品类型标签，上传单元 22 向云端上传所述用户购买消费品类型标签，分析单元 23 将重复次数最多的标签确定为用户的消费习惯标签，发送单元 24 向用户发送具有所述消费习惯标签的优惠券。
该发明通过记录用户消费情况，分析用户消费习惯，从而更加有针对性的发送消费卷，同时用户也能更加快速的掌握最新的符合自己消费习惯的消费信息。
以上仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (2)

  1. 一种自动推荐优惠劵的方法，其特征在于，所述方法包括如下步骤：
    记录消费品对应的类型标签，记录用户购买消费品类型标签；
    向云端上传所述用户购买消费品类型标签；
    根据所述标签的重复次数，将重复次数最多的标签确定为用户的消费习惯标签 ;
    向用户发送具有所述消费习惯标签的优惠券。
  2. 一种推荐***，其特征在于，所述推荐***包括：
    记录单元，上传单元，分析单元，发送单元，其中：
    记录单元，位于本地，用于记录消费品对应的类型标签，记录用户购买消费品类型标签；
    上传单元，位于本地，其输入端与记录单元输出端连接，用于向云端上传所述用户购买消费品类型标签；
    分析单元，位于云端，用于根据所述标签的重复次数，用于将重复次数最多的标签确定为用户的消费习惯标签；
    发送单元，位于云端，其输入端与分析单元输出端连接，用于向用户发送具有所述消费习惯标签的优惠券。
PCT/CN2016/071676 2016-01-21 2016-01-21 自动推荐优惠劵的方法以及推荐*** WO2017124418A1 (zh)

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JP2006252160A (ja) * 2005-03-10 2006-09-21 Dainippon Printing Co Ltd クーポン発行システム
CN102087733A (zh) * 2010-12-08 2011-06-08 天津市翔晟远电力设备实业有限公司 一种顾客消费习惯的分类统计方法及***
CN103544632A (zh) * 2013-07-22 2014-01-29 杭州师范大学 一种网络商品个性化推荐方法及***
US20140233430A1 (en) * 2013-02-18 2014-08-21 Tekelec, Inc. Methods, systems, and computer readable media for providing targeted services to telecommunications network subscribers based on information extracted from network signaling and data traffic
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006252160A (ja) * 2005-03-10 2006-09-21 Dainippon Printing Co Ltd クーポン発行システム
CN102087733A (zh) * 2010-12-08 2011-06-08 天津市翔晟远电力设备实业有限公司 一种顾客消费习惯的分类统计方法及***
US20140233430A1 (en) * 2013-02-18 2014-08-21 Tekelec, Inc. Methods, systems, and computer readable media for providing targeted services to telecommunications network subscribers based on information extracted from network signaling and data traffic
CN103544632A (zh) * 2013-07-22 2014-01-29 杭州师范大学 一种网络商品个性化推荐方法及***
CN104881802A (zh) * 2015-06-26 2015-09-02 深圳市华阳信通科技发展有限公司 智能装置及其关联推荐商品的方法
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