Marketers across the board have their task cut out in the current environment where the pressure to deliver on brand performance targets and justify cross-channel spends is immense. All of them are aggressively adopting advanced analytics technologies to mine and curate vast volumes of data aggregated from multiple internal and external sources, including social media.
However, interpreting a slew of statistics presented in spreadsheets and other text formats, post data analysis, can be tricky, not to mention tedious, given the rather bland nature of non-visual information.
This is where data visualization can come in handy. If data is represented in design-driven formats, such as charts, graphs and maps, marketers can analyze and interpret information in a logical, easy-to-understand manner. After all, the human brain processes visual information 60,000 times faster than it decodes text.
Data visualization tools also enable decision makers to uncover new trends, patterns and correlations, through slicing and dicing of various data sets in an interactive way. Besides, they help users understand the different factors influencing customer behavior, sales and other core business metrics.
Choosing the Right Tool
Marketers can be spoilt for choice, and frankly a bit confused, when it comes to selecting the right data visualization tool. The range of options available is pretty exhaustive and companies should pick one that caters to existing business requirements, in terms of functionalities, ease of use, licensing costs and compatibility with different in-house technology systems and data sources.
For the visualization tool to yield the desired results post installation, marketers should also upgrade the memory, processing speed and other aspects of the underlying system hardware. This is essential for organizations to be able to crunch huge data volumes, and glean actionable business insights in near real time.
Data Quality Matters
For data visualization to drive informed and swift decision-making, data quality assurance is critical. Organizations should proactively institutionalize robust data governance processes for effective data cleaning and management because great data will give greater results.
Selecting the Right Representation
A picture is worth a thousand words. Selecting the right representation from a wide range of chart types, styles and methods available for rendering data can be quite a challenge for users. A bad chart can be difficult to understand, leaving readers clueless about what the underlying numbers imply.
To keep things simple, brand managers should pick different charts depending on the task at hand. For example, line charts are useful for depicting the relationships between two data points, while scatter plots can better represent data distributions. On the other hand, bar charts are a good choice for comparing data points involving differences between brands/products, and pie charts for portraying a combination of data.
In a nutshell…
Marketers can certainly realize their goal of generating on-demand, data-driven insights on brand performance, if they embrace new ways of interpreting data through various rich graphical representations. By capturing refined data in a visual context, they will be able to truly understand the significance of that data, and optimize brand performance across multiple dimensions.
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