Fabrizio Nencini big data consultant

Get to know your customers!

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Big Data for large companies, banks and insurance companies

Big banks, insurance companies and large companies have to meet a multitude of regulatory requirements and store particularly sensitive customer data. In order to meet the ever-changing requirements, more and more software solutions are added. A data chaos is inevitable. That’s why our customers decide to use a modern big data solution. The big banks and insurance companies not only work more efficiently, but can also improve customer communication at the same time.

Improve customer service with big data analytics

When an employee talks to a customer, he gets much more information than what the customer says. Is the customer relaxed or already frustrated? He can then ask questions and find an individual solution for this customer.
Large companies, banks and insurance companies with a large number of customers who contact the company in various ways can no longer afford this very personal customer service. The solution: big data analysis of voice recordings, customer comments and more. The analysis of this data shows where and with which products customers have problems. This can improve your own product portfolio and strengthen customer loyalty.
For example, if the Big Data system detects bad customer sentiment shortly before the end of the contract, customer service can intervene directly and suggest a solution. In this way, customer service activities can be prioritized to create an individualized customer experience more efficiently.
Large companies, banks and insurance companies can use big data to combine their customer relationship management (CRM) with the data from customer communications management (CCM) in one system.
This will allow the right content to be put into the hands of people who can then use it to create relevant messages for your customers.

Personalize marketing with big data analytics

Marketing campaigns are typically targeted to a specific audience, customer segment, and so on. It does not just sound abstract, it is. The marketing has a self-defined idea of the ideal clientele. Is this idea correct? What does customers really want and think?
Big data analytics can identify the real needs and desires of customers: marketing campaigns can be targeted so that they can specifically pick up customers.

How does big data work?

The Big Data solution of the big banks and insurance companies is based on a so-called Data Lake. This takes the data from the various data sources, z. Excel files, text files, audio files, data from Outlook CRM and much more.
The advantage of a Data Lakes: The data stored there can be analyzed quickly and easily. Highly specialized and complex questions can be ideally answered.

Manage hundreds of millions of data with big data

The big data solution relies on a distributed file system, eg. With Hadoop. This allows data to be stored on different systems in a computer network. The big data system is able to manage several 100 million data.

Real-time big data analysis

In the Big Data solution for large banks and insurance companies, we rely on real-time data streaming with Spark and Informatica BDM. With Spark, important information can be temporarily stored so that employees have immediate access to the data.

Big Data and Privacy

With a big data solution, data protection must be a top priority. Especially when it comes to sensitive data as is the case with big banks and insurance companies. Data protection includes protection against unauthorised access and to ensure legal storage periods.

In the planning stage Companies should create a “DATA Road Map” concept for big data projects. For data protection, Apache Ranger is an important component for controlling the access concept : usually there are users or groups or departments that are only allowed to access specific data under certain paths from the HDFS (Hadoop Distributive File System).

In the Marketing area, personalised offers increase the probability of a conversion. On the other hand, a false “personalisation” can lead the customer to distrust the company, so companies should only use the data they want to use for “personalised customer experience”.

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