Data lakes & machine learning: tools for decision making in digital marketing

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Data lakes & machine learning: tools for decision making in digital marketing

Two-thirds of digital marketers consider data-driven decision-making to be much wiser than instinct- based decision making . This is highlighted by a survey carried out by Econsultancy in collaboration with Google , which also highlights that seven out of ten how to get a chinese phone number experts already live with a data-driven culture at all levels within their organizations.Companies are increasingly aware of the potential that the collection and exploitation of data has to correctly guide their business . But one thing is clear: to achieve the growth objectives set, it is necessary to break down silos and bet on transparency in access to information within the companies themselves.Data lakes and machine learning techniques are tools that help achieve this goal. They help to gather huge and varied amounts of data, to classify it to allow its use by all departments (the c-suite , Marketing, Sales, Customer Service, etc.) and predict future trends with a high percentage of success. . Let’s see how business decision making is made easier and more reliable thanks to data lakes and machine learning.Data lakes: what are they and what advantages do they have
Data lakes are repositories that store raw data of all kinds , both structured and unstructured, from various sources, for its conservation and analysis.

The use of data lakes has advantages over the use of data warehouses and, above all, allows to overcome the limitations of the isolated exploitation of data source by source, guilty of the creation of silos of information disconnected from each other
The aggregation of new records in the data lakes is simple and flexible: it is only required to apply a tagging scheme based on metadata that simplifies the extraction. In data warehouses, on the other hand, the data must be previously transformed and normalized to conform to a relatively rigid field structure, usually oriented to specific reporting needs.
Data lakes admit data from very diverse sources : from structured files processed by third parties to raw documents, records collected by sensors and IoT devices, geolocation signals, activity on social networks and even content in audio, video and high resolution images.

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Data lakes open the door to self-service data access protocols within the company . Each department or each international division can extract the set of insights gulf email list  that interests them and create their own reports and dashboards with minimal collaboration from IT specialists. With the help of data lakes it is easier to draw the buyer’s journey of users. By accepting a mix of sources in the data flow, it is possible to gather information about online and offline touchpoints in one place , and reliably reconstruct customers’ purchase itinerarieALEX MASIP , HEAD OF DATA AT LABELIUM
Data lakes support data from a wide variety of sources
Machine learning applied to marketing data lakes
Being prepared to host a huge volume of data, data lakes are the perfect terrain for the application of machine learning algorithms .

Data lakes are the favorite sandbox for data scientists when it comes to testing the potential of machine learning. Without machine learning, you need to carry out each analysis with a specific objective to draw conclusions. However, with machine learning it is possible to develop highly valuable segmentations and intelligence for the business, without having to previously define what is being soughtALEX MASIP , HEAD OF DATA AT LABELIUMIn the realm of digital marketing, some of the most common uses of machine learning in marketing data lakes are:
dentification of the different consumer segments and their purchasing patterns .
Anticipation of possible cancellations ( churn rate ) . Through machine learning, the signals that denote that a customer is going to end their subscription contract to a service are detected. In this way, there is scope to offer you more advantageous conditions before your departure takes effect.
Hyper-personalization of the brand’s messages in the interaction with the user, providing a true omnichannel shopping experienceData lakes and machine learning: how do they help the business?
The implementation of a data lake and the analysis of data using machine learning techniques is the cornerstone of a data-driven marketing strategy and, in short, guarantees that decision-making is supported by the possession of useful, valuable and updated.The unification of all the data collected by the organization and the detailed exploitation of these, even in real time, leads to a better management of the relationship with customers . Predictive models based on machine learning give clues about future behaviors, both global and individualAt a general level, they serve to identify variations in demand and anticipate external conditions that may impact the future of the business.
Person-to-person, they favor omnichannel, facilitate upsellings and cross-sales and anticipate possible incidents.In large companies and multinationals, using cutting-edge technology in data analysis is essential to avoid losing ground in highly competitive markets. According to the forecasts of the International Data Corporation (IDC) , in 2023 the investment of companies in machine learning software will amount to 97.9 billion dollars , a figure that multiplies by 2.5 that registered in 2019 (37.5 billion dollars ). Turning away from data lakes and machine learning is therefore not an option.

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