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Marketing analytics is the practice of measuring and analyzing data to understand marketing impact and identify areas to improve response and ROI (sas.com)
Marketing analytics is helpful as an insight into consumer behavior and preferences. Most people can only judge marketing performance through their own biased lenses, and analytics helps us realize our perceptions may not be the audience's perceptions. Analytics help marketers spend their budgets more effectively and focus on the strategies that work. Studies say that campaigns that utilize data-driven information increase ROI 5 X 8 times more than those that do not leverage analytics (Marketing Evolution, 2019). Marketing analytics ranked higher than any other category when marketers were asked the single most crucial factor in developing their strategies (Marketing Evolution, 2019). Analytics can explain hindsight, give insight, and predict with the foresight to optimize a marketing plan and maximize reach and returns.
Key goals of marketing analytics include (Dooley, J, 2022):
- Understanding your audience and what approaches work
- Measuring ROI from advertisements
- Capturing data from websites to gather insights on content consumption
- Observing how users interact on the website and social media
- Creating segments and more precise targeting in ads and campaigns
- Providing clear data and visualizations to explain to audiences and non-marketing management
- Understand how all the elements play together within the marketing strategy.
Some items that get lost in marketing analytics are security and the effect on ROI if data is misinterpreted. The data collected from analytics is only as good as the person analyzing it. With too little or too much data, you may have overwhelming amounts that are impossible to filter or holes in the data. This can cause marketers to report needed changes that are inaccurate. Per Mela and Moorman, data is not causal (Mela, C. & Moorman, C, 2018). For example, search advertising can be correlated with purchases, but it does not follow those ads caused the sales. If the analytic approach is flawed, then there is no meaningful translation.
Further, without suitable security and knowledge, data analytics can affect customers negatively and present risks for both the company and consumers. When a bias is entered into the artificial intelligence code, it can yield an undesirable result. For example, Twitter shut down a Microsoft chatbot that "learned" how to tweet out racist items, and Facebook was sued by the USDA for its AI having a bias that restricted housing ads to specific individuals (Hosanagar, K, & Iyengar, R, 2020). These items present a significant risk to all involved and exemplify how analytics should be monitored and even challenged.