Case Teknos Group Oy Paint Store Transaction Data

Companies operating in challenging business environments, characterized by the proliferation of disruptive technologies and intensifying competition, are obliged to re-evaluate their strategic approach. This has become the norm in the retail industry and traditional brick-and-mortar stores. Particularly local market players with scarce resources are looking into alternative solutions to delivering a unique customer experience with the intention to preserve their profitability. Customer experience has been an integral topic within academic research for decades, and has also substantiated its value in pragmatic contexts. Recent developments in this field have triggered the constitution of customer experience management functions, which aim to adopt a holistic approach to the customer experience. This enforces a quantitative perspective highlighting the role of customer transaction data. Association analysis is one of the most well-known methodology used to detect underlying patterns hidden in large transaction data sets. It uses machine learning techniques to firstly identify frequently purchased product combinations and secondly, to discover concealed associations among the products. The association rules derived and evaluated during the process can potentially reveal implicit, yet interesting customer insight, which may translate into actionable implications. The practical consequences in the framework of this study are referred to as sales increasing strategies, namely targeted marketing, cross-selling and space management. This thesis uses Python programming language in Anaconda’s Jupyter Notebook environment to perform association analysis on customer transaction data provided by the case company. The Apriori algorithm is applied to constitute the frequent itemsets and generate association rules between these itemsets. The interestingness and actionability of the rules will be evaluated based on various scoring measures computed for each rule. The outcomes of this study contribute to finding interesting customer insight and actionable recommendations for the case company to support their success in demanding market conditions. Furthermore, this research describes and discusses the relative success factors from the theoretical point of view and demonstrates the process of association rule mining when applied to customer transaction data

Context aware advertising

IP Television (IPTV) has created a new arena for digital advertising that has not been explored to its full potential yet. IPTV allows users to retrieve on demand content and recommended content; however, very limited research has been applied in the domain of advertising in IPTV systems. The diversity of the field led to a lot of mature efforts in the fields of content recommendation and mobile advertising. The introduction of IPTV and smart devices led to the ability to gather more context information that was not subject of study before. This research attempts at studying the different contextual parameters, how to enrich the advertising context to tailor better ads for users, devising a recommendation engine that utilizes the new context, building a prototype to prove the viability of the system and evaluating it on different quality of service and quality of experience measures. To tackle this problem, a review of the state of the art in the field of context-aware advertising as well as the related field of context-aware multimedia have been studied. The intent was to come up with the most relevant contextual parameters that can possibly yield a higher percentage precision for recommending advertisements to users. Subsequently, a prototype application was also developed to validate the feasibility and viability of the approach. The prototype gathers contextual information related to the number of viewers, their age, genders, viewing angles as well as their emotions. The gathered context is then dispatched to a web service which generates advertisement recommendations and sends them back to the user. A scheduler was also implemented to identify the most suitable time to push advertisements to users based on their attention span. To achieve our contributions, a corpus of 421 ads was gathered and processed for streaming. The advertisements were displayed in reality during the holy month of Ramadan, 2016. A data gathering application was developed where sample users were presented with 10 random ads and asked to rate and evaluate the advertisements according to a predetermined criteria. The gathered data was used for training the recommendation engine and computing the latent context-item preferences. This also served to identify the performance of a system that randomly sends advertisements to users. The resulting performance is used as a benchmark to compare our results against. When it comes to the recommendation engine itself, several implementation options were considered that pertain to the methodology to create a vector representation of an advertisement as well as the metric to use to measure the similarity between two advertisement vectors. The goal is to find a representation of advertisements that circumvents the cold start problem and the best similarity measure to use with the different vectorization techniques. A set of experiments have been designed and executed to identify the right vectorization methodology and similarity measure to apply in this problem domain. To evaluate the overall performance of the system, several experiments were designed and executed that cover different quality aspects of the system such as quality of service, quality of experience and quality of context. All three aspects have been measured and our results show that our recommendation engine exhibits a significant improvement over other mechanisms of pushing ads to users that are employed in currently existing systems. The other mechanisms placed in comparison are the random ad generation and targeted ad generation. Targeted ads mechanism relies on demographic information of the viewer with disregard to his/her historical consumption. Our system showed a precision percentage of 69.70% which means that roughly 7 out of 10 recommended ads are actually liked and viewed to the end by the viewer. The practice of randomly generating ads yields a result of 41.11% precision which means that only 4 out of 10 recommended ads are actually liked by viewers. The targeted ads system resulted in 51.39% precision. Our results show that a significant improvement can be introduced when employing context within a recommendation engine. When introducing emotion context, our results show a significant improvement in case the user’s emotion is happiness; however, it showed a degradation of performance when the user’s emotion is sadness. When considering all emotions, the overall results did not show a significant improvement. It is worth noting though that ads recommended based on detected emotions using our systems proved to always be relevant to the user\u27s current mood

Discovering Generalized Profile-Association Rules for the Targeted Advertising of New Products

[[abstract]]We propose a data-mining approach for the targeted marketing of new products that have never been rated or purchased by customers. This approach uncovers associations between customer types and product genres that frequently occurred in previous transaction records. Customer types are defined in terms of demographic attribute values that can be aggregated through concept hierarchies; product types can be generalized through product taxonomies. We use generalized profile-association rules (GP association rules) to identify the advertising targets for a given new product. In addition, we propose two algorithms—GP-Apriori and Merge-prune—to mine GP association rules and develop a value-based targeted advertising algorithm to select prospective customers of a new product on the basis of the discovered rules. We evaluate the proposed approach using both synthetic data and library-circulation data

The Role of Influentials in the Diffusion of New Products

This dissertation comprises three separate essays that deal with the role of influentials in the diffusion of new products. Influentials are a small group of consumers who are likely to play an important role in the diffusion of a new product through their propensity to adopt the product early and/or their persuasive influence on others’ new product adoption decisions. The literature labels these consumers as opinion leaders, social hubs, innovators, early adopters, lead users, experts, market mavens, and boundary spanners. This dissertation integrates two perspectives that researchers have mostly studied independently: market-level, which investigates the spread of a new product (e.g., total number of products sold) across markets over time as a function of aggregate-level marketing and social parameters; and individual-level, which considers how to identify influentials and their impact on the adoption behaviors of others. The first essay reviews and integrates the literature on the role of influentials in the diffusion of new products from a marketing management perspective. The study develops a framework using the individual- and market-level research perspectives to highlight five major interrelated areas: the two theoretical bases of why influentials have a high propensity to adopt new products early and why they considerably influence others’ adoption decisions, the issues concerned with how marketers can identify influentials and effectively target them, and how significant individual-level processes lead to significant market-level behavior. The study synthesizes the relevant research findings and suggests future research directions for improving our knowledge of the role of influentials in the diffusion of new products.The second essay explores firms’ decisions regarding the selection of target consumers for seeding—providing free products to enhance the diffusion process. The study examines the profit impact of targeting five groups of potential consumers for seeding under alternative social network structures. The findings suggest that seeding programs generally increase the net present value of profits. Moreover, social hubs—the most connected consumers—offer the best seeding target under most conditions that were examined. However, under certain conditions firms can achieve comparable results through random seeding and save the resources and effort required to identify the social hubs. Finally, the interactions among several variables—the choice of seeding target, consumer social network structure, and variable seeding cost—impact the returns that seeding programs generate and the ‘optimal’ number of giveaways.The third essay explores the adverse impacts of three types of consumer resistance to new products—postponement, rejection, and opposition—on firm profits. The study investigates these effects across five groups of consumers and alternative social network structures. The findings suggest that complex interactions between three groups of parameters—resistance, consumer social network, and diffusion parameters—affect the relationship between resistance and profits. Moreover, opposition reduces firm profits to a degree that is significantly greater than rejection and postponement. Finally, influential resister groups generally have stronger adverse impacts on profits than do randomly designated resisters