DATA MINING: Predicting Tipping Points


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Users will continue to appreciate technological innovations, driving dynamics, and comfort in the future, but seamless mobility as an overall experience enriched with many networked services will become more important.

A Vicious Circle

Digital companies, such as Amazon, Apple, and Google, are a step ahead of automobile manufacturers, unconditionally placing customers as their focus. Customers should be offered personalized address and appreciation instead of generic models with equipment features. Availability and flexibility in vehicle provision, simple handling of the usage process from booking to payment, value for money, and price transparency are all factors for success for digital mobility services.

As more and more young people choose not to drive, the industry will only be able to attract new consumers by harnessing this customer-related, digital added value.

Tipping Points — The Next 10 Years

Data is the fuel that drives new services and business models. Even though a modern vehicle generates more than 25 gigabytes of data per hour of use, established automotive manufacturers have not used the comprehensive data from vehicle sensors to create digital added value for their users. OEMs must learn from successful digital companies. By analyzing massive amounts of data, these companies gain deeper insights into customer behavior to create targeted offers.

If automobile manufacturers proceed correctly, they can not only optimize their products through data analysis but also stay in constant contact with customers via new services to learn more about their wants and needs. In addition, OEMs can access new sources of income through platforms by disclosing vehicle and transaction data to third-party service providers, such as suppliers, insurers, workshops, or dealers. Boundaries between the automotive industry and IT, telecommunications, utilities, and public administration are merging.

As networking increases, the car becomes part of a larger, cross-industry ecosystem.

DATA MINING: Predicting Tipping Points: By Dr. Philip Gordon, PhD

Companies that open their proprietary innovation structures and use external innovations can gain decisive competitive advantages in the future. A more outward-looking automotive ecosystem with strong innovation partners and external developers can relieve OEMs of their need for innovation. Vehicle-integrated services from third-party service providers could emerge from a variety of industries.

Data Governance.

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A Tipping Point Prediction: More Predictive Analytics | Transforming Data with Intelligence

Data Management. January 18, , a. Here are five predictions for what we can expect in Kostas Tzoumas. Chief executive officer, data Artisans. For reprint and licensing requests for this article, click here. Tags Data management Data visualization Data mining Data discovery.

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12222 Predictions: The Tipping Point for Automotive

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DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points
DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points
DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points
DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points
DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points
DATA MINING: Predicting Tipping Points DATA MINING: Predicting Tipping Points

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