In marketing, Big Data can be both useful and limiting depending on how it is used, and how management pursues marketing research. In recent years, big data has emerged as a promising wealth of information that would offer companies profound insights into customers. By recording and analyzing different metrics collected from very large groups of people, companies are able to segment their customer base and offer consumer-specific advertisements and products. Companies have equated big data collection and analysis with marketing, which in essence is creating, capturing, delivering, and communicating value to customers.
Predictive analytics analytics has also been a recent buzz-word when integrating data analytics with marketing. Predictive analytics utilizes different models and algorithms to forecast what will happen in the future based on historical data. An example of this includes using linear regression to model the behavior of sales, and then predict whether sales will increase or decrease in the future. This can give companies information about cyclical changes in consumer behavior, as well as allow the marketing department to preemptively act to address changes in consumer behavior. If a company experiences a significant decrease in sales after the holiday season, the marketing team can emphasize the value that the company provides to the customer year round.
Big data and predictive analytics also have drawbacks. While big data can be useful for customer segmentation, additional qualitative research is needed to deeply understand your customers. Big data and predictive analytics often extrapolates conclusions on entire groups of consumers which may not be accurate to everyone. It can ignore the critical factors that drive why consumers act the way they do. For example, a fast food company may utilize big data to segment their customers by age, race, and other factors. The fast food company can then use predictive analytics on these customer segments to see things like how often they get fast food, and are there any consistent cyclical changes that the company can take advantage of. However, qualitative research, like projective interviews and netnography, can offer insights into the specific factors that drive a consumer to decide to get fast food. These factors could include low prices, convenience and consistency. Big data and predictive analytics, while useful tools to implement, do not include the details of the full story about consumers and their behaviors. In order for marketing to be most effective, it would need a mixture of both big data, predictive analytics, and qualitative research methods.
References
Evolution, M. (n.d.). How to use predictive analytics in data-driven marketing. Modern Marketing Measurement & Optimization. Retrieved April 19, 2022, from https://www.marketingevolution.com/knowledge-center/the-role-of-predictive-analytics-in-data-driven-marketing
How to use Predictive analytics in your marketing strategy. Instapage. (2020, January 3). Retrieved April 19, 2022, from https://instapage.com/blog/what-is-predictive-analytics