Computer Science, Machine Learning and Big Data

The idea of creating and using consumer personas is not new. Marketers have been going through painstakingly long way to understand and define consumer persona for their products. Further, they go through an intricate process of defining and executing elaborate campaigns to acquire consumer information and map the same to required personas. Even after spending big portion of their budget, marketers face various problems in reaching out to the right consumer: Data acquisition is an expensive task, many times data is not authentic or recent; all this starts to affect the conversion rate of the business making the ROI a far-fetched dream. Typical approach used in data acquisition and persona creation suffers from multiple problems: Most personas built today are static. Yes, the practice of updating consumer profile periodically is helpful but not ideal. Second, there are just too many factors (attributes) involved in the consumer’s decision making process. Marketer’s approach of confining consumer to few personas is quite limiting and inaccurate. The answer to these complex problems is to build a multidimensional consumer profile that is always up-to-date. This is possible by engaging the consumers at various stages during their day, be it online venues such as social network, reviews, blogs, opinions, surveys or offline venues such as surveys, transactions, logs and so on. Developing a multidimensional profile that is up-to-date is not a simple task. It is the kind of problem where tools such as big data, data analytics and machine learning can be used most effectively.