Effectiveness of Market Segmentation techniques using Data Sharing in the Telecom industry

Abstract

Since the start of the 21st century, the amount of captured data has been continuously increasing in this digital age. With almost 2.5 quintillion bytes of data being generated and captured every day (Liang et al., 2018), researchers and companies have a strong interest in exploring the value that can be created with this data, called big data analysis. Also, since companies strive to be market leaders, they constantly evaluate methods/approaches to discover hidden trends/ potential opportunities. One method of finding hidden trends is through market segmentation, a process which can be defined as a division of a heterogeneous market into several smaller homogeneous markets to precisely understand the desires of consumers. Identifying and targeting the right consumers through market segmentation is highly dependent on the collected data. Due to the usage of obsolete data collection methods and privacy regulations, most often, companies only possess siloed data. If siloed data is used, then companies might not be effective with their segmentation strategies.
One way to ensure that data is complete and consistent might be through data sharing in a ‘data market’ between players to holistically understand the consumers. With this thought in mind, this thesis considers the telecom industry as an example and explores the effectiveness of market segmentation using shared data. The main research question of this thesis study is Before going deeply into the aim of the thesis, let’s first understand the current problems of the telecom industry. Traditionally, telecom firms have generated revenue via three streams i.e. voice, messaging and data. However, over the past decade, the market has witnessed an emergence of Over the top content players such as Netflix, YouTube, and Amazon Prime. These players do not need any association with the telecom firms to provide their services and thereby have impacted traditional telecom companies’ voice and messaging revenue streams. In addition to this with new content frequently being updated in these OTT services, customer preferences are constantly changing, and telecom firms are finding it hard to predict these varying needs with the siloed data present in their databases. This has therefore resulted in low average revenue per user (ARPU) levels for these telecom firms. As the first step in the thesis, we performed a literature review and identified four common segmentation techniques used by the telecom industry. The four techniques are customer value segmentation, customer behavior segmentation, customer lifecycle segmentation, and customer migration segmentation. These techniques are customer-centric and are heavily reliant on data for their effectiveness. To observe if these identified techniques are employed at the industry level and to contemplate the viewpoints of experts on data sharing in market segmentation, we interviewed market segmentation experts from the industry. The following subsections provide an overview of the interviews, questions asked in the interview and findings from the interviews.