Modelling Customer Behaviour with Topic Models for Retail Analytics

Topic modelling is a scalable statistical framework that can model highly dimensional grouped data while keeping explanatory power. In the domain of grocery retail analytics, topic models have not been thoroughly explored. In this thesis, I show that topic models are powerful techniques to identify customer behaviours and summarise customer transactional data, providing valuable commercial value. This thesis has two objectives. First, to identify grocery shopping patterns that describe British food consumption, taking into account regional diversity and temporal variability. Second, to provide new methodologies that address the challenges of training topic models with grocery transactional data. These objectives are fulfilled across 3 research parts. In the first part, I introduce a framework to evaluate and summarise topic models. I propose to evaluate topic models in four aspects: generalisation, interpretability, distinctiveness and credibility. In this manner, topic models should represent the grocery transactional data fairly, providing coherent, distinctive and highly reliable grocery themes. Using a user study, I discuss thresholds that guide interpretation of topic coherence and similarity. We propose a clustering methodology to identify topics of low uncertainty by fusing multiple posterior samples. In the second part, I reinterpret the segmented topic model (STM) to accommodate grocery store metadata and identify spatially driven customer behaviours. This novel application harnesses store hierarchy over transactions to learn topics that are relevant within stores due to customised product assortments. Linear Gaussian Process regression complements the analysis to account for spatial autocorrelation and to investigate topics' spatial prevalence across the United Kingdom. In the third part, I propose a variation of the STM, the Sequential STM (SeqSTM), to accommodate time sequence over transactions and to learn time-specific customer behaviours. This model is inspired by the STM and the dynamic mixture model (DMM); however, the former does not naturally account for temporal sequence and the latter does not accommodate transactions' dependency on time variables. SeqSTM is suitable for learning topics where product assortment varies with respect to time, and where transactions are exchangeable within time slices. In this thesis, I identify customer behaviours that characterise British grocery retail. For instance, topics reveal natural groups of products that are used in the preparation of specific dishes, convey diets or outdoor activities, that are characteristic of festivities, household or pet ownership, that show a preference for brands, price or quality, etc. I have observed that customer behaviours vary regionally due to product availability and/or preference for specific products. In this manner, each constitutional country of the UK, the northern and the southern regions of England and London show a preference for different products. Finally, I show that customer behaviours may respond to seasonal product availability and/or are motivated by seasonal weather. For instance, consumption of tropical fruits around summer and of high-calorie foods during cold months

Data Mining for Marketing

This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more. Customer Lifetime Value (CLV) is the total monetary value of transactions/purchases made by a customer with the business over an intended period of time and is used as means to estimate future customer interactions. CLV finds application in a number of distinct business domains such as Banking, Insurance, Online-entertainment, Gaming, and E-Commerce. The existing distribution-based and basic (recency, frequency & monetary) based models face a limitation in terms of handling a wide variety of input features. Moreover, the more advanced Deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system which is able to qualify both as effective, and comprehensive yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model which combines the predictions from bagging and boosting models that each is found to perform well individually. Empirical tests have been carried out on an openly available Online Retail dataset to evaluate various models and show the efficacy of the proposed approach.Comment: 11 pages, 7 figure

Will they buy?

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-137).The proliferation of inexpensive video recording hardware and enormous storage capacity has enabled the collection of retail customer behavior at an unprecedented scale. The vast majority of this data is used for theft prevention and never used to better understand the customer. In what ways can this huge corpus be leveraged to improve the experience of customer and the performance of the store? This thesis presents MIMIC, a system that processes video captured in a retail store into predictions about customer proclivity to purchase. MIMIC relies on the observation that aggregate patterns of all of a store's patrons-the gestalt-captures behavior indicative of an imminent transaction. Video is distilled into a homogenous feature vector that captures the activity distribution by first tracking the locations of customers, then discretizing their movements into a feature vector using a collection of functional locations-areas of the store relevant to the tasks of patrons and employees. A time series of these feature vectors can then be classified as predictive-of-transaction using a Hidden Markov Model. MIMIc is evaluated on a small operational retail store located in the Mall of America near Minneapolis, Minnesota. Its performance is characterized across a wide cross-section of the model's parameters. Through manipulation of the training data supplied to MiMic, the behavior of customers in the store can be examined at fine levels of detail without foregoing the potential afforded by big data. MIMIC enables a suite of valuable tools. For ethnographic researchers, it offers a technique for identifying key moments in hundreds or thousands of hours of raw video. Retail managers gain a fine-grained metric to evaluate the performance of their stores, and interior designers acquire a critical component in a store layout optimization framework.by Rony Daniel Kubat.Ph.D

The Evolving Brand-Consumer Relationship - The Impact of Business Cycles, Digital Platforms, and New Advertising Technologies

Unprecedented technological progress and pronounced business cycles were the defining factors of the past two decades and disrupted consumers’ everyday lives as well as brands’ established modus operandi. For example, the severe global financial crisis and the subsequent European debt crisis forced many consumers to tighten their belts and change what, where, and how they shop. As a consequence, long established relationships with brands were put to the test as consumers adopted and habituated new shopping behaviors that not only shaped their purchases during the recessions but even beyond (Lamey 2014; Lamey et al. 2007). On the technological side, digital platforms such as AirBnb and Uber unhinged entire industries (Eckhardt et al. 2019; Parker, Van Alstyne, and Choudary 2016). At the same time, digital platforms have allowed brands to be consumers’ constant companions in various areas of their life such as money management, personal health, exercising, nutrition, and more (Ramaswamy and Ozcan 2016, 2018). Technological progress has also produced ever more sophisticated advertising tools that allow brands to target consumers with pinpoint accuracy and allow any brand irrespective of its advertising budget to address their specific (niche) target consumers using highly engaging ad formats such as online video advertising (Anderson 2006; Bergemann and Bonatti 2011; Van Laer et al. 2014). Evidently, these fundamental forces—business cycles, digital platforms, and new advertising technologies—have substantially affected consumers, brands, and their relationship. In three essays, my co-authors and I show empirically, experimentally, and conceptually how brands and consumers have reacted and adjusted to these changes and how their relationship thus evolved. In the first essay, we find that while business cycles put established consumer-brand relationships to the test, brands remain important to consumers even in recessions. They adjust their shopping strategies to allow themselves to keep consuming branded products, for example by switching to cheaper outlets or buying on promotion. The second essay shows that digital platforms are a powerful tool that allows brands to create and orchestrate superior value for consumers and thus become increasingly influential in their daily lives. We discuss how this development profoundly elevates the brand-consumer relationship. The third essay, presents insights into skippable ads, an advertising format specific to digital channels. It transforms consumers’ traditional role in the advertising context from a captive audience to an empowered one that is granted the option to skip ads. My results show that, counter-intuitively, this is not only perceived positively by consumers but may disrupt their advertising viewing experience. Thus, I present strategies for advertisers that mitigate the adverse effects of skippable ads and improve branding

Analysis of out-of-town expenditures and tourist trips using credit card transaction data

Credit card transaction data contains a vast amount of valuable information that can indicate consumer behaviour patterns and mark out human mobility. In this study we analyse the transactions carried out by a sample of 10.000 Istanbul-based customers of a Turkish bank to scrutinize expenditures incurred out of Istanbul. In our preliminary descriptive analysis, we examine the relation between demographic attributes and spending measures, as well as investigate the extent to which the population and the number of points of interest imply higher or lower credit card expenditure by visitors. We develop a methodology to extract tourist trips from consecutive credit card transactions. Subsequently, we implement a hierarchical clustering method to evaluate what the purpose of these trips might have been. Our results indicate 5 clusters of purpose: ’Leisure’, ’Business’, ’Acquisition’, ’Visiting Friends and Relative’ and ’Package Holiday’. The same clustering method is applied to segment provinces of Turkey based on which product and service categories visitors prefer. We deploy a number of predictive models to estimate tourist expenditure and whether a person would embark on a trip in the upcoming months. The predictive power of these models are generally moderate; nevertheless, several of the most useful predictors are behavioural or are related to previous trips, factors that have not been considered in literatur