Sri Sridharan, Vice President, Research Director
Companies have more data at their disposal than ever before. Today’s marketers are equipped and using both data- and science-driven marketing to form predictions about customers’ behaviors. Based on this wealth of data, we’re confident in our insights.
But perhaps we should be more skeptical of the insights we’re pulling from our data. Why? Marketing is a soft science, and data analysts are dealing with human, psychological, and statistical bias. These biases include confirmation bias — seeking out data that confirms our existing beliefs — and also new types of bias like algorithmic bias, which is inherent in code and training data that’s used to create machine-learning algorithms (e.g., Google Photos’ racist algorithm).
This doesn’t mean marketers should use less data and analytics; it simply means marketers must account for these biases when analyzing and interpreting data.
In this episode, Sri Sridharan discusses how companies can balance the desire to be evidence-based with the need for accuracy and precision in insights.