Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

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.

Customers are increasingly using online channels to buy products. For e-commerce companies, this offers new opportunities to tailor the shopping experience to customers' needs. Therefore, it is of great importance for a company to know their customers' intentions while browsing their webpage. A major challenge is the real-time analysis of a customer's intention during browsing sessions. To this end, a representation of the customer's browsing behavior must be retrieved from their live interactions on the webpage. Typically, characteristic behavioral features are extracted manually based on the knowledge of marketing experts. In this paper, we propose a customer embedding representation that is based on the customer's click-events recorded during browsing sessions. Thus, our approach does not use manually extracted features and is not based on marketing expert domain knowledge, which makes it transferable to different webpages and different online markets. We demonstrate our approach using three different e-commerce datasets to successfully predict whether a customer is going to purchase a specific product. For the prediction, we utilize the customer embedding representations as input for different machine learning models. We compare our approach with existing state-of-the-art approaches for real-time purchase prediction and show that our proposed customer representation with an LSTM predictor outperforms the state-of-the-art approach on all three datasets. Additionally, the creation process of our customers' representation is on average 235 times faster than the creation process of the baseline.

Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.

E-commerce has grown into a billion-dollar industry in recent times with an ever-increasing number of individuals using it regularly. Thus, e-commerce companies can gather interaction data from their customers and analyze it to create focused and personalized marketing campaigns. For large companies, it is possible to tap into these data for personalization using deep learning-based methods that require enormous computing resources. Small companies, on the other hand, cannot afford this. Furthermore, this level of tailor-made addressing necessitates an accurate customer representation. Nevertheless, the exploration of universal representations applicable across various tasks has been limited despite the advantages they offer. We propose a universal customer representation learned only from customer interaction data. We demonstrate that self-supervised trained embeddings of the customer interaction context are a suitable universal customer representation for various e-commerce tasks. To demonstrate the effectiveness of our approach, we conducted experiments comparing four different state-of-the-art approaches and their capabilities in different prediction tasks. Not only do we show that our method outperforms others in most cases, but it also meets other important criteria for real-world applications. It is particularly important to emphasize that our approach does not require a significant amount of resources, and furthermore, is data protection compliant by not using personal information.

Nowadays, recommendation systems are a central component in a wide range of online services. One major challenge for them is to generate suitable recommendations for individual users who differ, among other things, in their interests and preferences. In this paper, we propose a deep learning-based product-to-product recommendation system that uses product embeddings to incorporate content-based product information and user data in form of historical checkouts. On top of that, we add a filter that rearranges the models' recommendations based on the individual user preferences to a product category that is derived from past activities. In order to evaluate and show the impact of our approach, we conduct tests on historical data and an A/B-test in a real online service that offers thousands of different customer packaged goods (CPG) to millions of users. The results show that our approach delivers suitable product recommendations to users and outperforms the current system in use.