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3.2 Junction of Analytical Methods

3.2.1 Evaluation of Analytical Models

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to understand the attitudes and mindsets of participants. A qualitative analysis displays an orientation toward unique cases and is context sensitive, which is, as mentioned in Chapter 2, an increasingly relevant aspect. In this manner, "qualitative research can be used to vividly demonstrate phenomena or to conduct cross-case comparisons and analysis of individuals or groups" (University of Southern California, 2018a). The focus is on complex inter-dependencies of qualitative parameters that can include questions about occupation, attitudes, values, lifestyle, knowledge, benefits, consumption habits and usage occasions. Despite the limitations of qualitative research methods, such as non-replicability, they can deliver valuable insights that can assist in interpreting the gained quantitative results. Further, they can facilitate the validation of prototypes and measurements of improvements. To that end, the combination of qualitative and quantitative data can ensure that limitations of one type of data are balanced by the strengths of another. The purpose of combining data is to enrich, examine and explain the results. This leads to a triangulation of confirming, reinforcing and rejecting of results. On the basis of the listed benefits, the multi-dimensional listener segmentation presupposes the inclusion of somewhat-qualitative parameters.

However, because qualitative methodologies would not allow for extraction of insights of large sample sizes, another option had to be found. This is an imperative, because the derived approach aims to derive an overarching audience framework based on superior, context-related attributes that is elevated above demographic and genre limitations. The challenge was in finding an option to best aggregate and concatenate data points that refer to behavior, interaction, sentiments, knowledge and activity in large amounts. Those can all be derived from the API of the respective music streaming platform. The later-derived framework serves as the qualitative instance for the verification process, based on the motivation and prototypical contexts for each audience segment.

To guarantee the option for numeric cross-referencing, two data types assist: categorical variables which, are also known as discrete or qualitative variables, and continuous vari-ables, which are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables. Hence, some of those insights have been transformed into numerical states and others were conceptualized by concatenating multiple measured behavioral and consumption metrics. To derive distinguishable and reproducible statements from those variables, it is necessary to align all continuous and categorical pa-rameters. In the beginning, Gower’s distance metric seemed to offer a solution to facilitate the combination of numeric and categorical data. However, further investigations revealed that although the metric "displays the potency to reach high fitness, it could possibly hold a tendency for biases, which could be enforced by the chosen weights and data"(van den Hoven, 2015, p.2). As such, transformation of the data types was conducted in two slightly

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diverging manners to attain a pure data set. First, variables were established to measure the distance between different parameters or instances. Second, nominal variables were transformed into continuous variables to replace the current variable with N binary variables.

In this case, N is the number of values the nominal variable could be. Those methods allow for generation of aligned data formats, which are needed to perform clustering and further statistical analysis.

The selection of the metrics was made more tangible by categorizing those into three superordinate streams of data. The three overarching categories shaping the analytical procedure were context, consumption and sales data. The section of consumption can further be broken down by underlying data sets, which were of behavioral, acoustic and demographic nature. Subordinate metrics can be further expanded as required for particular cases, as exemplified in Section 3.4. In this manner, behavioral consumption data can enhance numerical consumption data. Thus, instead of only focusing on numerical sales and consumption figures, the results provide a more holistic view about the user, his behavior and the consumption cycle. This is ultimately due to a focus on consumer- over sales-centric parameters, which in its last instance may be compared to characteristics of different audience segments in a verification-like process, which is established in the following.

Many underlying data points are needed to achieve personalized or categorized target-ing of music. The insights on the drawbacks of current methods in music analytics help disclose new ways to drive understanding of music listeners. This would in the end enable determination of not only what and how much music is being consumed but also by whom, when and how. This could eventually enable the automatic creation of personalized user experiences tailored to one’s listening type to attain higher acquisition and retention rates.

For this actions, needs and behavioral patterns of the users are increasingly analyzed and combined within multi-layered approaches. Different metrics may be called on depending on the demands of a query and the focus of the musical content that is to be created. One will only hardly ever encounter the need to pair all mentioned metrics simultaneously. However, the outline of the full spectrum is necessary because the repetitive process of data acquisition needs to be laid out in such a manner that the available datasets are accessible from all metric levels. The data infrastructure needs to be set up holistically, offering options to gain insights from a user as well as a music content perspective, to guarantee instant access.

This is the case if permanent infrastructures for repetitive API calls are desired. However, if only singular queries are demanded, individual data infrastructures can be created that ultimately demand less processing power as well as storage capacity, which makes these kinds of requests feasible for more actors within the music and adjacent markets.

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