WO2017124414A1 - 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
WO2017124414A1
WO2017124414A1 PCT/CN2016/071671 CN2016071671W WO2017124414A1 WO 2017124414 A1 WO2017124414 A1 WO 2017124414A1 CN 2016071671 W CN2016071671 W CN 2016071671W WO 2017124414 A1 WO2017124414 A1 WO 2017124414A1
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user
consumption
label
period
recording
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PCT/CN2016/071671
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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/CN2016/071671 priority Critical patent/WO2017124414A1/zh
Publication of WO2017124414A1 publication Critical patent/WO2017124414A1/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 is sent more specifically according to the user's customary consumption period, and the user can also quickly grasp the latest consumption information that suits his consumption habits.
  • a method of automatically recommending a discount comprising the following steps:
  • a period in which the label with the most repetition is located is a period of consumption of the user
  • a coupon having the consumption habit tag is sent to the user during the user's consumption habit tag period.
  • 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 a consumer product, recording a user-purchased consumer product type label and a purchase time;
  • 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 and time 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 in a specific time period, a period in which the label with the most repetition is located as a user's consumption habit label period;
  • 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 during the consumption habit tag period of 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 according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own 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 and the purchase time.
  • 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 the consumer product type label and the time 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. Recording the consumption time can analyze the time period that the user often consumes, and the average user will consume it when he is off work or on weekends.
  • the time period in which the tag with the most repetition is located is determined as the user's consumption habit tag period according to the number of repetitions of the tag in a certain time period.
  • step S103 a coupon having the consumption habit tag is transmitted to the user during the consumer habit tag period of the user.
  • the label with the most repetition is determined as the user's consumption habit label, for example, the user likes to shop at the mall on Saturday, like to buy what brand or price item, and send the purchase ⁇ in advance.
  • the user also enjoyed the corresponding discount.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own 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:
  • Recording unit 21 located locally, for recording the type label corresponding to the consumer product, recording the user's purchase of the consumer product type label and the purchase time;
  • 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 and time to the cloud;
  • the time period in which the label with the most repetition is located is determined as the consumption habit period of the user according to the number of repetitions of the label in a certain period of time;
  • 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 having the consumption habit tag to the user during a consumption habit tag period of the user.
  • the user is in the recording unit 21
  • the type label corresponding to the consumer product is recorded
  • the uploading unit 22 uploads the user purchase consumer product type label and time to the cloud
  • the analyzing unit 23 Determining, according to the number of repetitions of the label in a specific time period, the period in which the label with the most repetition is located is the user's consumption habit label period, and the sending unit 24 A coupon having the consumption habit tag is sent to the user during the user's consumption habit tag period.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own consumption habits.

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  • Game Theory and Decision Science (AREA)
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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及购买时间（S100）；向云端上传所述用户购买消费品类型标签及时间（S101）；根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段（S102）；在所述用户的消费习惯标签时段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根据用户习惯的消费时段发送消费卷，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根据用户习惯的消费时段发送消费卷，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根据用户习惯的消费时段发送消费卷，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及购买时间；
向云端上传所述用户购买消费品类型标签及时间；
根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段；
在所述用户的消费习惯标签时段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及购买时间；
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及时间；
分析单元，位于云端，用于根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段；
发送单元，位于云端，其输入端与分析单元输出端连接，用于在所述用户的消费习惯标签时段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根据用户习惯的消费时段发送消费卷，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及购买时间。
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及时间。
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，记录消费时间可以分析用户经常消费的时段，一般用户都会在下班或者周末休息时进行消费。
在步骤 S102 中，根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段。
在步骤 S103 中，在所述用户的消费习惯标签时段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及时间，将重复次数最多的标签确定为用户的消费习惯标签，例如用户喜欢在周六到商场购物，喜欢买什么牌子或者价位的物品，提前发送消费劵，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根据用户习惯的消费时段发送消费卷，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及购买时间；
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及时间；
分析单元 23 ，位于云端，用于根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段；
发送单元 24 ，位于云端，其输入端与分析单元 23 输出端连接，用于在所述用户的消费习惯标签时段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及购买时间，上传单元 22 向云端上传所述用户购买消费品类型标签及时间，分析单元 23 根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段，发送单元 24 在所述用户的消费习惯标签时段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根据用户习惯的消费时段发送消费卷，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及购买时间；
    向云端上传所述用户购买消费品类型标签及时间；
    根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段；
    在所述用户的消费习惯标签时段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及购买时间；
    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及时间；
    分析单元，位于云端，用于根据所述标签在特定时段的重复次数，将重复次数最多的标签所在的时段确定为用户的消费习惯标签时段；
    发送单元，位于云端，其输入端与分析单元输出端连接，用于在所述用户的消费习惯标签时段向用户发送具有所述消费习惯标签的优惠券。
PCT/CN2016/071671 2016-01-21 2016-01-21 自动推荐优惠劵的方法以及推荐*** WO2017124414A1 (zh)

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US20140310080A1 (en) * 2012-06-04 2014-10-16 Visa International Service Association Systems and methods to process loyalty benefits
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CN101493925A (zh) * 2009-03-09 2009-07-29 浙江工商大学 一种采用增量式挖掘的零售行业折扣券生成方法
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***
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