Understanding Consumer Journey using Attention based Recurrent Neural Networks

Zhou, Yichao; Mishra, Shaunak; Gligorijević, Jelena; Bhatia, Tarun N.; Bhamidipati, Narayan

doi:10.1145/3292500.3330753

Cited by 35 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another is Recurrent neural networks (RNN), typically designed to recognise sequences and patterns in speech, handwriting and text applications (Mandic et al 2001) . RNN research suggests (Zhou et al 2019) it could aid conversion in complex user journeys, guide creative design (text and images), and lead to higher product effectiveness which demonstrates great potential for our model.…”

Section: -Ai Landscape Researchmentioning

confidence: 96%

Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?<b> </b>

Choudhury¹

2022

Preprint

View full text Add to dashboard Cite

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

show abstract

Section: -Ai Landscape Researchmentioning

confidence: 96%

Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?<b> </b>

Choudhury¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Also, more prominently, models that capture information from the sequence such are RNNs have been proposed recently [1,5,6,21] and they reportedly perform significantly better than their non-sequential counterparts. Moreover [1] and [22] have used sequences of events from diverse data sources, while [1] has additionally proposed adding temporal information of events as an additional source of information to better model representations for conversion attribution task.…”

Section: Modeling Users' Conversion Predictionmentioning

confidence: 99%

“…The goal of prospective targeting is the opposite of retargeting -users who have shown interest into advertisers business in the recent past should be excluded, and the goal becomes to generate new users as both visitors and converters for the advertiser. While the definition of retargeting users may significantly vary from one advertiser to another, in terms of the general advertising funnel (stages in which users are placed with respect to their probability of conversions [22]), prospective targeting should focus on users who are in the upper funnels (users further away from the conversion stage). Conversely, in terms of the advertising funnel, retargeting focuses on users in the lowest funnel stages (users very close to conversion).…”

Section: Introductionmentioning

confidence: 99%

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

View full text Add to dashboard Cite

Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability. CCS CONCEPTS • Information systems → Display advertising; Web mining; • Computing methodologies → Machine learning.

show abstract

“…Recent studies have examined the problems of website segmentation in hotel chains [8], dating platforms [9] and cruise operators [10]. Little systematic research exists on digital user behaviour according to the conversion funnel stages [11][12][13], and much less is known about how to segment cultural users with structured data from manually extracted and SEO software sources. While there are sporadic research works of websites of museums [1,[14][15][16], empirical validation of online museums' segmentation thereof is still scarce.…”

Section: Introductionmentioning

confidence: 99%

Online Museums Segmentation with Structured Data: The Case of the Canary Island’s Online Marketplace

Meneses

Estupinán-Ojeda

Vilkaitė-Vaitonė

2021

JTAER

View full text Add to dashboard Cite

This paper’s primary objective is to segment the online marketplace of the Canary Islands’ museums by using different conversion funnel metrics. Little systematic research exists on digital user behaviour, and much less is known about how to segment cultural users with structured data from manually extracted and SEO software sources. With this aim in mind, we built a database with data related to the different phases of the conversion funnel of the museums to segment this online museum marketplace. In the findings, not only do we acknowledge the existence of different segments, but we also provide insight into the user’s digital behaviour by considering different metrics from the different phases of the conversion model process (awareness, consideration, conversion and loyalty). The originality of this paper is multifold. Firstly, it estimates the potential optimisation of these websites to improve the digital marketing implemented by the museum sector of the Canary Islands. Secondly, it sheds light on what benchmarking tactics and statistics procedures can be followed to carry out a non-hierarchical segmentation with standardised and comparable data. Thirdly, it contributes to the literature of digital marketing by eclectically combining the conversion funnel model, benchmarking techniques and non-hierarchical segmentation procedures.

show abstract

Understanding Consumer Journey using Attention based Recurrent Neural Networks

Cited by 35 publications

References 15 publications

Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?<b> </b>

Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?<b> </b>

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Online Museums Segmentation with Structured Data: The Case of the Canary Island’s Online Marketplace

Contact Info

Product

Resources

About