It is not only the exponentially increasing amount of data that is needed to understand the human behavior and preparation of intimate responses back as marketing communication but also the design of messages based on relevant cultural background, complex attitude sets of targeted audience and more appropriate technology to reach them are needed. This can be more complete with the non-technical side of the story rooted in both social sciences and information technology.
To make this work, our approach is taking the challenge of developing new analysis models for the marketing message delivery under various cultural constraints as an addition to big data analytics, adding on top of it. Using stochastic methods or implementation of AI techniques will only help if the cultural context is properly drawn. But, for a more effective result this context shall be studied with the glasses of social scientists, which in most cases do not receive required attention in the design.
Technology plays an important role in changing life of consumers with an unexpected speed of innovative developments for the last several decades. Most of them were disruptive and shaped not only behavior of consumers but also empowered them for searching for better products and services. These changes took place in media, communication, information management of socialization and collaboration. The digitization revolution is a continuum until when people and machines will embrace a common ground while improving life of consumer. Today, new marketing tools are developing on the basis of integrated machine learning, such as analysis of customer conciseness, prediction of behavior and perceptive marketing, which will be used extensively through digital platforms, new media, social web and in everyday devices for targeted marketing.
Engagement with customer for facilitation of innovation by developing collaborative knowledge about products and services gains importance. This is also a significant marketing competence of successful companies which can be characterized as innovation oriented organization. Machine learning methods such as classification, regression, clustering, and cross-validation are powerful tools which are used with big data mining for large databases and give researchers opportunities to gain new insights into consumer behavior. There are many practitioners in this domain both from the academia, the business world, and increasing number of initiatives and many research companies trying to solve more problems, which are becoming epidemic, in giving meaning to all forms of unstructured data flooding from e-commerce pipelines, social media, mobile applications, gamification platforms and overloaded content generation.
This huge volume of data, new generations of customers and their evolved capabilities, competition aware firms and service based focus in business are expanding the canvas for the future. Management, marketing and technology are in intersection and merging with more advancements coming from all axes to reach customer, to act with partners, to stand tall in the market, to manage resources more effectively.
But, the human side of the story is still up there to be understood better and more “humanistic”, don’t you think?