How to get insights from data with Machine Learning and Big Data Analytics

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Machine learning is a subset of artificial intelligence (AI) and allows computers to learn without explicit programming. It is based on the use of data that is processed by algorithms in search of recurring patterns, a greater amount of data corresponds to the possibility of generating more accurate insights. Machine learning, therefore, is positioned as an advanced technology within the Big Data and Analytics platforms, with the aim of extracting value from a large amount of information that is often not homogeneous.

Its potential applications today find effective adoption in various business areas such as marketing, sales, finance, operations, services, human resources, etc. From customer segmentation and clustering, to which to offer a unique customer experience, to real-time pricing, up to its ability to develop predictive models. Here are three ways through which machine learning allows you to obtain insight from the data available to the company.

1.Personalize the offer for the customer with machine learning

Machine learning fulfills every marketer's dream: to get to know their customers in depth to "surgically" customize the offer. In fact, machine learning can divide users according to various selection criteria (registry, area of ​​belonging, browsing behavior, percentage of purchases, etc.).

In this way, it can identify both which are the most valuable customers on which to focus specific marketing campaigns, and the reasons that cause the abandonment or disaffection of some (churn analysis). In the first case, the models that the algorithm creates are transformed into engagement actions related to the best practices of the customer base, in the second, they identify the main causes of the abandonment in such a way as to prepare consequent retention policies.

Overall, machine learning guarantees a customer experience that uses strategies such as cross selling, upselling and engine recommendations that derive from the set of insights obtained from the many channels that customers use in interacting with the brand.

2. Dynamic pricing possible with machine learning


If in the example cited above the machine learning algorithms fulfill the desire of the CMO (Chief Marketing Officer), the flexible determination of the price according to the demand is an advantage that is met by the CFO (Chief Financial Officer). A figure that today, thanks to the use of technology, is increasingly indicated with the expression Augmented CFO to highlight a role that is not limited to mere administrative and accounting tasks. The dynamic pricing systems, in fact, are a resource to support the financial area of ​​companies since they relieve the managers of the function from the burden of having to define price policies upstream of the offer. Airlines and transport companies are classic areas in which algorithms are now used to optimize the prices of their services. In this case, the management of data from the customer journey to acquire insights translates into the proposition of the best price at the right time.

3. Predictive models for plant decisions and maintenance

The ability to develop predictive models is perhaps one of the most revolutionary features of machine learning, with an impact on the accuracy and reliability of business decisions never observed before. If in the past the top management strategies were based on the historical and traditional Business Intelligence systems, the power of the algorithms gives predictive value by virtue of the intersection and aggregation of a vast amount of data. From customers' orientations to macroeconomic scenarios that may have repercussions on the choices made by their organization, predictive systems are precious allies against market unexpected events.

In addition, in addition to encouraging decisions that anticipate future trends, today they are adopted in predictive maintenance to optimize plant maintenance and decrease relative costs. The insights that come from IoT sensors (Internet of Things) thus become a heritage immediately available to minimize downtime and increase production efficiency. Yet another application that is decreeing the rapid spread of machine learning among businesses.

How Relatech can help you

Relatech is able to address all the issues previously indicated thanks to its digital platform RePlatform.

RePlatform is the Cloud based framework developed by Relatech, declinable in various business areas that allows you to develop customized solutions based on specific customer needs.

The platform consists of five modules that reflect the fundamental pillars of digital innovation:

 ReData, COGNITIVE ANALYTICS: it manages large quantities of data and allows it to be analyzed quickly and deeply through AI algorithms, chosen appropriately case by case, to make correct and timely decisions. Using Big Data and Machine Learning technologies, ReData extrapolates relevant information insight from the data, identifies recurrent and anomalous patterns, and produces predictive analyzes useful for the business.

ReYou, DIGITAL CUSTOMER EXPERIENCE: allows you to get to know your customers better through each channel to improve the company's marketing levers. To do this, ReYou uses the most modern web and mobile technologies, modern content management tools, digital devices such as AR/VR and wearable and in general and any other type of device to establish points of contact with users, improve their experience of use and purchase both digital and physical.

ReThing, PHYSICAL / DIGITAL INTERACTION: allows you to interact with the physical world by acquiring data from instruments and machinery to prevent possible outages and optimize business processes. ReThing collects data from smart devices such as hub servers, edge gateways, sensors and other related sources. The data collected through these devices thanks to the interaction with the ReYou and ReData modules can be analyzed and presented to users in innovative ways, for example as Digital Twin, for different applications in the Industry 4.0 context.

ReSec, PROTECTION OF VALUE: Ensures privacy, reliability, traceability and non-repudiation of information. For this reason, ReSec adopts advanced Cyber Security techniques for Intrusion Detection and Prevention, and the use of Blockchain infrastructures with Distributed Ledger for the certification of relevant information within distributed business processes.

ReHub, CLOUD READINESS: thanks to the use of best of breed technologies and the use of the DevOps methodology, it guarantees the platform's high availability and scalability, interoperability with other systems and compatibility with all types of Cloud, public, private or hybrid.

Thanks to RePlatform technology and to the skills and experience gained in the realization of projects in different market sectors ranging from Retail to Utilities, Telecommunications and Industry to Finance, Relatech is able to offer innovative Big Data Analytics solutions based on AI and Machine Learning to exploit the enormous potential represented by the data available but not yet fully used.