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Bugatti sport scenery

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Bugatti sport scenery
Bugatti sport scenery

Bugatti sport scenery

A quick review of Bugatti Sports Scene.

7. In 2012 we jumped into data analysis in terms of BFM and the number of days.

8. Realization that data analysis research can be done by analyzing large sets of data and having intelligent algorithms.

9. Producing visualizations of data could get visualizations with a click.

10. Collaborating with Germany’s first and only automatons test lab {aity}, and establishing research deal with Scikit-learn.

1. In 2012 we jumped into data analysis in terms of BFM

Several years ago Bugatti decided to conduct a research project on its automobiles. The company proposed several similar experiments and collected tons of data. We integrated trend analysis between old and new cars and released this info on how data analyses can improve the user experience. We were the first team to conduct a large-scale analysis with BFM and uncovered insight into the growing interest in different car models.

Partial collaboration with Bugatti to improve the user experience in retail workshops

2. Realization that data analysis research can be done by analyzing large sets of data and having intelligent algorithms

Advanced machine learning research. The result of the latest research is our product oeuvre performance graph. The graphic shows how consumers influenced the usage of the artist ergonomic. Consumers often take actions into consideration decision-making. We made an interactive addition of this graph and the dealer UI to gain more information about customers.

Graphic from oeuvre

Bugatti sport scenery
Bugatti sport scenery

To generate more metrics we compiled the data from 50,000 dealers, 38,000 consumers, and other non-metallic details and micro indicators.

We just changed the name of the product palette from “visualizations” to “Performance Fragments”. This product is now not just about a visual representation of insights. It now evolves into a link between consumer patterns and client experience. That’s the goal

Our product will be updated in the next 1-2 years!

#1️ growth increase for oeuvre

#2 investment in the product’s visualization

#3 social impact

How to build an industry-leading predictive analytics platform?

Introduction

Building a predictive analytics platform requires a careful approach to delivering useful insights for clients. The process requires constant updates. Data visualizations cannot just be observational and told. They must have a traceable business value attached to them. All this evolves from the continual need to deliver the rich and complete set of data in a manner consistent with precise algorithms. Various vendors work in close proximity to each other, it is not surprising that the best outcome is often achieved in the hands of one of the vendors.

Our project focused on including the main stages of strategic optimization. They included collecting internal data from all sides. Billing, sales, after-sales, and demand management are only part of it. This interaction will enable a visible flow of high-quality raw data.

In-depth stakeholders’ relations are greatly advanced. After-sales systems take into account representatives. Order to empower and assist customers. The relationship between Bugatti dealer and client. Which reflects crucial relations with dealerships and technology, is developing.

Investing in these experience-empowering analytical tools is a time-consuming and risky endeavor. To avoid betting inwards. Our main point of the strategy is to find solutions: make strong research and analyze data systematically and correctly.

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