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The customer experience represents the next competitive battleground and the main distinctive asset for companies.

The emergence of digital technologies has transferred the decision-making power from the company to the customer, who becomes the main actor of the purchasing experience.

Consumers are not satisfied by simply buying a product, but they expect customized experiences (offers, content, tailored products, etc.), transparent communication (prices, conditions, service changes, etc.), efficient delivery (fast delivery, real-time information, etc.) and immediate satisfaction.

It is not a coincidence when we feel that a message or content has been created for us, behind the scenes there is a marketer who has analyzed our needs and has designed a "buyer persona". Creating a buyer persona means accurately identifying who the customers are, where they are, and consequently the right tool to reach them.

In the new business model, the knowledge of customers' needs, and expectations must be at the heart of decision-making processes, but even organisations must be sufficiently agile and flexible to react quickly to the ongoing change, and to adapt to emerging quality standards.


The Customer experience management is the foundation of any omnichannel strategy. It is only by integrating both physical and digital channels that a company can communicate with its customers in order to optimize their experience.

The omnichannel strategy plays an increasingly important role in the strategic choices of companies.

The starting point in the management of the omnichannel strategy are data; the arrival point is the personalization of the contents conveyed starting from these data. In order to activate this process, it is essential to have technologies that are capable of supporting and processing in a rapid (if not real-time) and precise manner an increasingly large amount of data (Big Data).

The "data-driven" process consists of three main activities:

1. data collection, a phase in which all data in the company, both internal and external to the organization, are collected and integrated.

2. data analysis, the phase in which consumer data are analyzed and processed in order to gain new insights and create a unique view of the consumer.

3. data activation, the phase within which data are used by various business functions.

For each of these activities, there are specific supporting technologies:

As far as data collection is concerned, the creation of a single CRM is the first step in a data-driven approach. A more technologically advanced step consists in the adoption of a Data Hub, that must integrate and harmonize data from multiple sources, different touchpoints and of different nature, making them available to other platforms. This approach allows to break down the barriers between the different company silos, centralizing data and making them available to the different touchpoints.

Data analysis, on the other hand, makes use of advanced Analytics tools that can extract value from the data collected, working with complex data of different origins and formats. Some of the technologies also allow real time analysis, through a system capable of dynamically enriching and modifying user segments in real time.

Finally, for what concerns data activation, it is essential to offer effective and relevant communication to consumers, thus enhancing the value of data collection and analysis carried out previously.


Artificial intelligence represents a key enabler for companies to remain agile and keep up with the changing needs of both customers and the marketplace. Businesses must continue to prioritize their digital transformation processes to meet these needs and drive the customer experience.

Now more than ever before customers have been looking for a deeper connection with brands. With the right approach, AI has the potential to help companies interact with their customers by moving the relationship to a much more personal level.


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