SYSTEM AND METHOD FOR PROVIDING DYNAMIC RECOMMENDATIONS BASED ON INTERACTIONS IN RETAIL STORES
    2.
    发明申请
    SYSTEM AND METHOD FOR PROVIDING DYNAMIC RECOMMENDATIONS BASED ON INTERACTIONS IN RETAIL STORES 审中-公开
    基于零售店相互作用的动态推荐系统和方法

    公开(公告)号:US20150348163A1

    公开(公告)日:2015-12-03

    申请号:US14451322

    申请日:2014-08-04

    Abstract: An end-user device operable in a retail store records a voice conversation held in the retail store. The recording is transmitted to a server for comparing it to recordings of other conversations from a plurality of other retail stores. The server makes a recommendation based on the comparing. Embodiments of the invention are also directed to an analytics system that collects real-time metrics data for various virtual or physical retail stores associated with a plurality of contact centers. The system performs real-time analytics of the collected metrics data for identifying a product or service. The processor modifies, based on the real-time analytics, a prior service or product to be offered by a particular retail store, with the identified service or product. The processor pushes the modified service or product for display on the end-user device use in the particular retail store.

    Abstract translation: 可在零售商店中操作的最终用户装置记录保存在零售商店中的语音对话。 记录被发送到服务器以将其与来自多个其他零售商店的其他对话的记录进行比较。 服务器根据比较建议。 本发明的实施例还涉及一种分析系统,其收集与多个联络中心相关联的各种虚拟或实体零售商店的实时度量数据。 系统对收集的指标数据执行实时分析,以识别产品或服务。 处理器将基于实时分析,修改由特定零售商店提供的先前服务或产品,以及所标识的服务或产品。 处理器推送经修改的服务或产品以在特定零售商店中的最终用户设备上使用显示。

    SYSTEM AND METHOD FOR BRIDGING ONLINE CUSTOMER EXPERIENCE
    5.
    发明申请
    SYSTEM AND METHOD FOR BRIDGING ONLINE CUSTOMER EXPERIENCE 审中-公开
    制定在线客户体验的系统与方法

    公开(公告)号:US20150350435A1

    公开(公告)日:2015-12-03

    申请号:US14451338

    申请日:2014-08-04

    CPC classification number: G06Q10/06393 G06Q10/0639 G06Q30/0601 G06Q30/0613

    Abstract: A system and method for aggregating a customer's anonymous online experience and associating it to the customer once the customer is known. A server monitors and aggregates data on anonymous interactions between a customer and a website. The collected data is stored in a data store in association with a code. The server detects occurrence of a trigger event as the data is collected, and the code is provided to the customer in response to detecting the trigger event. When the customer presents the code at the retail store, a retail store device sends a message including the code to the server. The retail store device also provides other identification information of the customer. The server may then associate the anonymous data to customer based on the identification information.

    Abstract translation: 一旦客户知道客户的匿名在线体验并将其与客户相关联的系统和方法。 服务器监视和聚合客户和网站之间匿名互动的数据。 收集的数据与代码相关联地存储在数据存储器中。 当收集数据时,服务器检测到触发事件的发生,并且响应于检测到触发事件而将代码提供给客户。 当客户在零售商店提供代码时,零售商店设备将包含代码的消息发送到服务器。 零售商店设备还提供客户的其他身份信息。 然后,服务器可以基于识别信息将匿名数据与客户相关联。

    MULTI-TENANT BASED ANALYTICS FOR CONTACT CENTERS
    8.
    发明申请
    MULTI-TENANT BASED ANALYTICS FOR CONTACT CENTERS 审中-公开
    基于多用途的联络中心分析

    公开(公告)号:US20150347951A1

    公开(公告)日:2015-12-03

    申请号:US14451310

    申请日:2014-08-04

    Abstract: Embodiments of the present invention are directed to a system and method for collecting and analyzing data from a plurality of contact center tenants. A processor collects from a plurality of source devices over a data communication network, real-time metrics data for a plurality of contact centers. The real-time metrics data relates to a plurality of contact center factors. The processor stores the collected real-time metrics data in the data store, and generates benchmark data based on the collected real-time metrics data. The processor determines, for a particular contact center of the plurality of contact centers, performance of the contact center relative to the benchmark data. The processor further outputs a recommendation based on the comparison.

    Abstract translation: 本发明的实施例涉及一种用于收集和分析来自多个联络中心租户的数据的系统和方法。 处理器通过数据通信网络从多个源设备收集多个联络中心的实时度量数据。 实时度量数据涉及多个联络中心因素。 处理器将收集的实时度量数据存储在数据存储中,并且基于收集的实时度量数据生成基准数据。 对于多个联络中心的特定联络中心,处理器确定联络中心相对于基准数据的性能。 处理器进一步输出基于比较的推荐。

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