Our generation has seen an exponential increase in digital tools adoption. One of the unique areas where digital tools have made an exponential foray is in the sphere of digital marketing, where goods and services have been extensively promoted through the use of digital advertisements. Following this growth, multiple companies have leveraged multiple apps and channels to display their brand identities to a significantly larger user base. This has resulted in products, worth billions of dollars to be sold online. Emails and push notifications have become critical channels to publish advertisement content, to proactively engage with their contacts. Several marketing tools provide a user interface for marketers to design Email and Push messages for digital marketing campaigns. Marketers are also given a predicted open rate for the entered subject line. For enabling marketers generate targeted subject lines, multiple machine learning techniques have been used in the recent past. In particular, deep learning techniques that have established good effectiveness and efficiency. However, these techniques require a sizable amount of labelled training data in order to get good results. The creation of such datasets, particularly those with subject lines that have a specific theme, is a challenging and time-consuming task. In this paper, we propose a novel Ngram and LSTM-based modeling approach (NLORPM) to predict open rates of entered subject lines that is easier to implement, has low prediction latency, and performs extremely well for sparse data. To assess the performance of this model, we also devise a new metric called 'Error_accuracy@C' which is simple to grasp and fully comprehensible to marketers.
翻译:我们这一代人在采用数字工具方面出现了飞速增长。数字工具使得指数化信息发光的独特领域之一是数字营销领域,货物和服务通过使用数字广告得到了广泛的促进。随着这一增长,多家公司利用多种应用程序和渠道向一个大得多的用户基础展示其品牌身份。这导致产品,价值数十亿美元,可在线销售。电子邮件和催促通知已成为发布广告内容、积极主动地与联系人接触的关键渠道。一些营销工具为市场设计电子邮件和推介信息提供了用户界面,为数字营销运动提供了一种用户界面。市场者还获得了输入主题行的预测开放率。对于扶持市场者来说,最近使用了多种机器学习技术来生成目标线。特别是,深层次的学习技术已经建立了良好的效益和效率。然而,这些技术需要大量贴有标签的培训数据才能获得良好的结果。创建这样的数据集,特别是有特定主题的数据集,是一项具有挑战性和耗时费的任务。在这个文件中,我们提议了一个新颖的Ngram 和快速的预测行距,我们进入了一个更简易的NC-Remal 模型,用来进行IM 和极易的模型的运行。