首页> 外文会议>Workshop on e-Commerce and NLP >'Are you calling for the vaporizer you ordered?' Combining Search and Prediction to Identify Orders in Contact Centers
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'Are you calling for the vaporizer you ordered?' Combining Search and Prediction to Identify Orders in Contact Centers

机译:“你是要你订的汽化器吗?”将搜索和预测相结合,以确定联络中心的订单

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With the growing footprint of ecommerce worldwide, the role of contact center is becoming increasingly crucial for customer satisfaction. To effectively handle scale and manage operational cost, automation through chat-bots and voice-bots are getting rapidly adopted. With customers having multiple, often long list of active orders - the first task of a voice-bot is to identify which one they are calling about. Towards solving this problem which we refer to as order identification, we propose a two-staged real-time technique by combining search and prediction in a sequential manner. In the first stage, analogous to retrieval-based question-answering, a fuzzy search technique uses customized lexical and phonetic similarity measures on noisy transcripts of calls to retrieve the order of interest. The coverage of fuzzy search is limited by no or limited response from customers to voice prompts. Hence, in the second stage, a predictive solution that predicts the most likely order a customer is calling about based on certain features of orders is introduced. We compare with multiple relevant techniques based on word em-beddings as well as ecommerce product search to show that the proposed approach provides the best performance with 64% coverage and 87% accuracy on a large real-life data-set. A system based on the proposed technique is also deployed in production for a fraction of calls landing in the contact center of a large ecommerce provider; providing real evidence of operational benefits as well as increased customer delight.
机译:随着电子商务在全球范围内的发展,联络中心的作用对客户满意度变得越来越重要。为了有效地处理规模和管理运营成本,通过聊天机器人和语音机器人实现的自动化正在迅速得到采用。由于客户有多个、通常很长的活动订单列表,语音机器人的第一个任务是识别他们打电话的是哪一个。为了解决这个我们称之为顺序识别的问题,我们提出了一种两阶段实时技术,以顺序方式将搜索和预测相结合。在第一阶段,与基于检索的问答类似,模糊搜索技术在嘈杂的通话记录上使用定制的词汇和语音相似性度量来检索感兴趣的顺序。模糊搜索的覆盖范围受到客户对语音提示没有或有限响应的限制。因此,在第二阶段,引入了一种预测解决方案,该解决方案根据订单的某些特征预测客户最有可能拨打的订单。我们比较了基于word em beddings和电子商务产品搜索的多种相关技术,结果表明,该方法在大型真实数据集上提供了最佳性能,覆盖率为64%,准确率为87%。一个基于所提出技术的系统也被部署在生产中,用于一小部分在大型电子商务提供商的呼叫中心登陆的呼叫;提供运营效益的真实证据,并增加客户满意度。

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