3 research outputs found
Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email
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
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, newssection prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns