Machine Marketing and Human Marketing

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As we enter 2020, we are experiencing both the digital transformation and the automation of Marketing. In the era of AI, IoT, 5G, Big Data and quantum computing, it is worth asking not so much whether there is really a future for the marketing professional within this new paradigm but rather what position the consumer is in.

It is difficult to think of a marketing department that does not have an area dedicated to digital. In fact, it is beginning to seem strange that a traditional company is not flirting with some kind of prediction model. The figures that are being incorporated into Marketing departments such as Business Intelligence, Data Scientist or Business Analyst just 10 years ago would have been inconceivable.

These profiles come from the world of science and are made all india whatsapp number up of mathematicians, physicists or computer engineers who, in a certain way, are combining science and marketing hand in hand with digital transformation in the era of industry 4.0.

However, despite the automation of processes and the digestion of thousands of data, the more human side of marketing prevails.

The struggle to discover what our customers want
Within marketing departments we are using most of our efforts and resources to detect, decipher and model what our clients want. If we look back over the history of marketing, the purpose has evolved. Marketing has gone from creating needs to satisfying them. It has gone from waiting for the client to come to the brand going directly to meet them. It has gone from having a long period of action for the sale to having to find the right moment for it to happen.

This last premise has led to the current model being investigated in the intelligence labs of marketing departments. "What our client wants" is being studied in depth. The tremendous power that comes from knowing what a client wants is taken for granted. This allows us to offer a personalized service or product that can meet needs, save money on strategies that usually lead to poor results, and at the same time establish models that allow scaling.

All of this is given by models and predictions configured through thousands of consumption data or interactions with our products. However, we are neglecting a vital part that many departments have overlooked until now. We forget something as vital as trying to know what our customer feels .