Data Visualization in Marketing
Abstract
In the last twenty years, the volume of data, and particularly marketing data, has increased exponentially. Hardware and software makers have struggled to keep pace with this explosion in volume. Visualization of marketing data is a solution that we suggest as a way to solve this problem. The four V’s of data, as noted by strategists at IBM, are Volume, Variety, Velocity and Veracity. Types of data, referred to as hierarchical data, are more difficult to evaluate with standard tables, due to the more complex relationships between levels of the hierarchy. The approach we present here addresses all four areas of data explosion observed in recent years, particularly for hierarchical data. In addition to computers and software that have to deal with data overload, according to Miller’s Law, the human mind is easily overwhelmed as it is only able to handle about seven pieces of information when it comes to memory and processing. The overloading problem that the manager’s mind is likely to face can be solved, or at least mitigated, by the visualization software that we present in this study. Visualization is particularly important for hierarchical data, where the individual data points are connected in a tree-like structure, with large clusters of data broken into sub-categories. The hierarchical analyses of data suggested here can help people to see relationships between variables and groups, while making it easy to check on data veracity. We demonstrate the approach using sales data for a technology company. The visualization helps to understand the break-up of sales data into categories, subcategories etc.
Full Text: PDF DOI: 10.15640/jmm.v3n2a4
Abstract
In the last twenty years, the volume of data, and particularly marketing data, has increased exponentially. Hardware and software makers have struggled to keep pace with this explosion in volume. Visualization of marketing data is a solution that we suggest as a way to solve this problem. The four V’s of data, as noted by strategists at IBM, are Volume, Variety, Velocity and Veracity. Types of data, referred to as hierarchical data, are more difficult to evaluate with standard tables, due to the more complex relationships between levels of the hierarchy. The approach we present here addresses all four areas of data explosion observed in recent years, particularly for hierarchical data. In addition to computers and software that have to deal with data overload, according to Miller’s Law, the human mind is easily overwhelmed as it is only able to handle about seven pieces of information when it comes to memory and processing. The overloading problem that the manager’s mind is likely to face can be solved, or at least mitigated, by the visualization software that we present in this study. Visualization is particularly important for hierarchical data, where the individual data points are connected in a tree-like structure, with large clusters of data broken into sub-categories. The hierarchical analyses of data suggested here can help people to see relationships between variables and groups, while making it easy to check on data veracity. We demonstrate the approach using sales data for a technology company. The visualization helps to understand the break-up of sales data into categories, subcategories etc.
Full Text: PDF DOI: 10.15640/jmm.v3n2a4
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