Add Thesis

Predicting customer purchase behavior within Telecom

How Artificial Intelligence can be collaborated into marketing efforts

Written by J. Forslund, J. Fahlén

Paper category

Master Thesis


Business Administration>General




Master Thesis: In the past ten years, physical shopping has undergone a fundamental transformation, transforming into or expanding to digital shopping (ITN, 2018), which can be accessed by anyone from anywhere. As a result, the behavioral patterns of consumers, which companies consider to be very valuable, have changed their trajectory. Knowing the customer is crucial to the company because it can help them operate more effectively in different scenarios from production to sales. However, the process of collecting, analyzing and using customer information has not been developed in tandem with the transformation of shopping (Quinton & Simkin, 2016); sales representatives can easily and intuitively observe that customers stop in front of the T-shirt shelf in order to guide accordingly The customer-on the contrary, when the customer stops in front of the same T-shirt, what measures should be taken and what method should be used-the shirt is online? This lag in adapting to changing consumer behavior creates opportunities for existing companies (Scott et al., 2016). Recent academic research has shown that artificial intelligence (AI) models can help companies gain a competitive advantage by providing obscure and advanced insights about customers (D'arco et al., 2019; Bekavac and Praničević, 2015), thereby improving operational performance (McKinsey, 2016). This research focuses on the Swedish telecommunications industry and related products. An important aspect that needs to be acknowledged is that due to their life cycle and price, telecommunications products such as broadband, mobile post-paid plans and phones are inherently riskier for customers, and therefore require a higher participation rate than buying fast-moving consumer goods ( FCMG), such as T-shirts, drinks or toothpaste (Rossiter, Percy & Donovan, 1991; Rossiter & Percy 1997). This nature of telecommunications products shapes the customer journey of the industry, and thus affects the marketing strategies applied by the company. The client of this thesis project is the telecommunications organization Telia, a leading market participant in Sweden (PTS, 2018). Since April 2019, Telia has been collecting consumer behavior data on its website Using these data, the author aims to investigate the application of artificial intelligence models in the telecommunications industry, which predicts visitors' purchasing propensity to meetings based on online information. This paper will reveal whether this AI model can be a useful tool for marketing strategy decision makers. In addition, the paper will provide fruitful insights in the field of the most advanced AI models applied in the telecommunications industry. industry. In addition, marketing professionals will further understand how AI technology can be used in conjunction with traditional marketing practices. Hawkins and Mothersbaugh (2010) define consumer behavior as "the study of individuals, groups, or organizations and the processes they use to select, protect, use, and dispose of products, services, experiences, or ideas to meet needs and their effects. These processes It has an impact on consumers and social consumers as well as the process they take to purchase products or services." In addition, some people believe that all marketing decisions are based on assumptions and knowledge of consumer behavior. Organizations regularly apply theories and information about consumer behavior, because knowledge about this field is critical to influencing consumers’ decisions about which products to buy. Bamora et al. (2010) claimed that the literature on consumer behavior appeared as a phenomenon in the 1960s, and since then significant progress has been made through obscure and sporadic research. Today, the literature on the field of consumer behavior constitutes a rich cumulative collection of different sub-fields related to consumer behavior knowledge (Barmola et al., 2010). For example, the subfields range from the company’s market segmentation process to personal and contextual characteristics that influence attitude changes (Hawkins & Mothersbaugh, 2010). In addition, consumer behavior is interdisciplinary, developed by scientists, philosophers, and researchers in the fields of psychology, sociology, social psychology, cultural anthropology, and economics (Barmola et al., 2010). In addition, the extensive and diverse cumulative collections that constitute consumer behavior have received many critical reviews, whether from academic or commercial perspectives. Stankevich (2017) summarized the main trends, theories and gaps in consumer behavior and decision-making. It is said that marketing has a goal-to reach consumers at the moments or so-called touch points that most influence their decisions. Stankevich (2017) refers to the 5-stage model as the traditional model of the consumer decision-making process, and believes that this model has become the basis for the development of new and complex concepts, allowing scholars and marketers to understand how touch points and decision-making processes interact. Scott et al. (2016) observed that in the literature of consumer decision-making, widely similar models are mentioned differently, among which he mentioned terms such as "customer journey", "consumer purchase process", "consumer claim acquisition process" and so on. Baines et al. (2016) describes the "consumer claim acquisition process", which is a mapping of every stage that consumers go through from initial motivation to actual purchase and re-evaluation. A further study compared with the Kahneman (2011) system was found in the concept of purchase participation. Products purchased for the first time usually require more participation than products purchased repeatedly (Boyd et al., 2002). Similar information about the different levels of consumer participation in purchasing scenarios stems from cognitive research on the decision-making process. Motivated by other perspectives on how consumers acquire knowledge and which experiences they use, in their purchasing decisions-Belch G. and Belch M. (2009) made decisions about low and high participation. Belch G. and Belch M. (2009) studied different learning methods and their impact on advertising and promotion. They believe that the five-stage model of the consumer decision-making process treats consumers as problem solvers and information processors. Therefore, consumers participate in many psychological processes to evaluate various options to determine the extent to which they may satisfy their needs or purchase motivation. Therefore, in fact, the author believes that the consumer's purchase process may deviate from the theoretical model, depending on the amount of information that consumers have to process and evaluate. The Rossiter & Percy grid is used as a framework to distinguish different types of products. The model is shown in Figure 3, which highlights the risk and motivation levels triggered by certain product types, and which associated cognitive systems are active in the consumer's decision-making process. The shape of the grid is 2x2, and its vertical axis covers the participation rate/risk rate of consumers; the determinant here is the level of perceived risk experienced by individual consumers, and higher risk (price) indicates higher participation. The horizontal axis in the model covers the underlying motivation for purchase; whether it is the purchase part that solves the problem (hence the information motivation) or the emotion (change motivation). The products can be classified to fit the cells in the grid. This model can be a useful tool to help marketers understand which marketing strategy to use based on their products (Rossiter, Percy & Donovan, 1991; Rossiter & Percy, 1997). Read Less