Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings

Gomes, Miguel Alves; Meyes, Richard; Meisen, Philipp; Meisen, Tobias

doi:10.1145/3511808.3557127

Cited by 11 publications

(16 citation statements)

References 21 publications

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“…Guidance generation also offers great potential for further research. In related areas, such as recommender systems in e-commerce applications that predict customer purchase behaviour, there are approaches to represent interaction sequences in latent vector space [AMMM22]. Approaches to describe visualizations more formally are offered by grammars such as the vega-lite grammar [SMWH17].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Guided Exploration of Industrial Sensor Data

Langer,

Meyes,

Meisen

2024

Computer Graphics Forum

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In recent years, digitization in the industrial sector has increased steadily. Digital data not only allows us to monitor the underlying production process using machine learning methods (anomaly detection, behaviour analysis) but also to understand the underlying production process. Insights from Exploratory Data Analysis (EDA) play an important role in building data‐driven processes because data scientists learn essential characteristics of the data in the context of the domain. Due to the complexity of production processes, it is usually difficult for data scientists to acquire this knowledge by themselves. Hence, they have to rely on continuous close collaboration with domain experts and their acquired domain expertise. However, direct communication does not promote documentation of the knowledge transfer from domain experts to data scientists. In this respect, changing team constellations, for example due to a change in personnel, result in a renewed high level of effort despite the same knowledge transfer problem. As a result, EDA is a cost‐intensive iterative process. We, therefore, investigate a system to extract information from the interactions that domain experts perform during EDA. Our approach relies on recording interactions and system states of an exploration tool and generating guided exploration sessions for domain novices. We implement our approach in a software tool and demonstrate its capabilities using two real‐world use cases from the manufacturing industry. We evaluate its feasibility in a user study to investigate whether domain novices can reproduce the most important insights from domain experts about the datasets of the use cases based on generated EDA sessions. From the results of this study, we conclude the feasibility of our system as participants are able to reproduce on average 86.5% of insights from domain experts.

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Section: Discussion and Future Workmentioning

confidence: 99%

Guided Exploration of Industrial Sensor Data

Langer,

Meyes,

Meisen

2024

Computer Graphics Forum

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“…For this purpose, product embeddings were created and trained [47][48][49][50]. A recent approach using pre-trained embedding features to represent customer behavior was proposed by Alves Gomes et al [51,52]. The authors pre-trained an embedding to encode customers' behavior and used the representation to predict customers' purchase intention.…”

Section: Customer Representationmentioning

confidence: 99%

“…In our experiments, this is represented by interactions that are not included in the training set but in the test set. Therefore, we introduced the "unknown" token which is one way to deal with the out-of-vocabulary problem [51,58]. Note that the created negative examples of the Amazon training set are not included in the trigrams.…”

Section: Data Preprocessingmentioning

confidence: 99%

It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

Alves Gomes,

Meyes,

Meisen

et al. 2024

JTAER

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Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.

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“…2, we started our literature review with reading related surveys. Plenty of research surveys in the field of segmentation prioritize the underlying methodology or class of methods but not their usage in specific domain (Gennari 1989;Rokach 2010;Hiziroglu 2013;Ben Ayed et al 2014;Firdaus and Uddin 2015;Reddy and Vinzamuri 2018;Shi and Pun-Cheng (2016) which reviews customer and marketing segmentation methods and the necessary data. They identify different segmentation approaches and e-commerce process which coincides in some parts with our outcomes.…”

Section: Literature Overviewmentioning

confidence: 99%

“…With embeddings, it could be possible to encode additional behavioral information that could improve the customer targeting process. This was already demonstrated for product recommendation (Vasile et al 2016;Tercan et al 2021;Alves Gomes et al 2021;Srilakshmi et al 2022) or customers' purchase behavior prediction (Alves Gomes et al 2022). Despite the popularity in several e-commerce tasks, no author used an customer embedding representation in the reviewed literature.…”

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confidence: 99%

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Gomes

Meisen

2023

Inf Syst E-Bus Manage

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The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

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Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings

Cited by 11 publications

References 21 publications

Guided Exploration of Industrial Sensor Data

Guided Exploration of Industrial Sensor Data

It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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