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：我们这一代见证了数字工具采用率的指数级增长。数字工具实现指数级渗透的一个独特领域是数字营销领域，其中商品和服务通过数字广告得到广泛推广。随着这一增长，多家公司利用多种应用和渠道向更广泛的用户群体展示其品牌形象，从而实现了价值数十亿美元的产品在线销售。电子邮件和推送通知已成为发布广告内容、主动与联系人互动的重要渠道。多种营销工具为营销人员提供了用户界面，用于设计数字营销活动的电子邮件和推送消息。营销人员还可以获得所输入主题行的预测开信率。为了让营销人员能够生成有针对性的主题行，近年来已使用了多种机器学习技术，特别是已展现出良好效果和效率的深度学习技术。然而，这些技术需要大量标注训练数据才能获得良好结果。创建此类数据集，尤其是包含特定主题主题行的数据集，是一项具有挑战性且耗时的工作。在本文中，我们提出了一种基于Ngram和LSTM的新型建模方法（NLORPM），用于预测输入主题行的开信率，该方法更易于实现、预测延迟低，且在稀疏数据上表现极佳。为了评估该模型的性能，我们还设计了一种名为“Error_accuracy@C”的新指标，该指标易于理解且对营销人员完全可解读。