Coniecto

Coniecto-360

To offer a customized value proposition in e-commerce environments.
Challenge

Challenge

The challenge of this project, co-financed by the Ministry of Energy, Tourism and Digital Agenda. (Num. TSI-100105-2017-9), has been to create a student-machine interaction that had into account the variety of educational resoruces, created by the teacher and the student´s interests and his/her intellectual abilities?

This R & D project aims to develop an intelligent platform that, through BigData, Machine Learning and Natural Language Processing techniques, can adapt to the specific needs of each student.

Omni-channel Marketing

Omni-channel Marketing

"The omni-channel marketing tries to offer a continuous and fully integrated user experience regardless of the channel or device the user uses to connect/access.

In fact, omnichannel marketing aims to stop talking about user-clients to start talking about people-clients, a person who connec with our company at many different times through different channels and through different devices."

Our solution

We take on technological challenges and are committed to improving the user experience.
Data and source integration
Data and source integration
Having a holistic vision of our customer means to get a vision of the whole life cycle. So we need to obtain the information that comes from every step of the customer´s journey, such as purchase, website analytics, customer relationship management ( CRM ) and later fidelity programs campaigns.
Processing data using ML techniques.
Processing data using ML techniques.
Taking into account navigation data of miles demands to use a platform consistent with massive data management, such as GCP. Thanks to it, we will be able to store key data to our study in a structured and easily recoverable way.
Insights commercial exploitation.
Insights commercial exploitation.
Analytic data true value appears when we get knowledge we can apply effectively. In Coniecto-360 case, we are developing a recommedation system that makes possible to offer a customized value proposition for every user. In this manner we optimise conversion and return rates.

State of the art enhancements

Obtain characteristic behaviour patterns existing in the data, for use in the field of e-Commerce.
Results

Results

A recommendation system based on Big Data problems and applying Automatic Learning techniques to offer a personalized service to users, based on their browsing history. It consists of different stages:

  • Data collection and storage
  • Preprocessing of the data
  • Learning and obtaining the model
  • Implementation of the learned model

Technologies

Machine learning
Big Data
Google Cloud Platform
Google Analytics
Google Big Query
Datalab
Dataprep
TensorFlow

The project has been co-financed by the Ministry of Energy, Tourism and Digital Agenda.

Project Number: TSI-100105-2017-2
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