The automobile industry of India will become the third largest in the world by 2016. There has been growing demand for automobiles due to rising income and young population propelling India amongst top five auto manufacturers by the year 2015. Naturally, the automobile manufacturers would want to understand the Indian automobile market at a granular level. So, web crawling is one of the latest techniques that would serve their analytics’ needs.
The government of India aims to develop the country as a global manufacturing as well as a research and development (R&D) hub. That’s how we hear of many global automobile giants setting up manufacturing units in India. As the companies step into new geographies, they need insights into local market trends, buyer preferences, working of distribution channels and many more things about how a business runs in their new territory. In this context, Web crawling is helping a great deal in gearing up the automobile industry. Collecting relevant industry trends and data has become a simple affair with web crawling. It offers good information about the local perception on product, price and promotion schemes. Modern web crawling tools help in predicting future trends and behavior enabling businesses to make knowledge based decisions. On the other hand, Web crawling updates customer’s regarding the latest trends in the automobile industry and best available options as per their buying power.
The possibility to Scrape Data for automobile or automotive industry analysis is crucial for companies in this field. It helps them in understanding buying patterns of customers. Different Web Crawling and Scraping techniques and tools help in extracting robust data from different channels. These tools search every minute details of the web and create a comprehensive volume of customer–centric data. This data is further processed as per the customers’ requirements. These processes transform voluminous amount of unstructured data into structured data which can be stored and analyzed by uniquely tailored web crawling tools.
Companies are extracting automobile information from various sources to shape up future vehicle designs. They usually try to scrape automobile company reviews, user reviews and auto parts reviews to understand the customers’ requirement in an in-depth manner and design vehicles accordingly. In the big data age, every part of the vehicle can be tuned and modified as per customers’ requirements. Authentic, Real-world data is being collected from different sources including customer sentiment data, vehicle sensor data and others as well. This data, which is based on user-feedback and behavior, is helping companies to improve safety, performance and other features.
Companies are leveraging big data in the automotive industry to solve major business issues. There has been a major transformation in the way automotive makers interact with customers now. Automotive industry makers are interacting with customers like never before. The industry is looking at implementing innovative approaches to maximize revenue and profitability. Big data is offering an effective opportunity for companies to address the demands of their more informed customers.
Big Data helps automobile industry by generating powerful insights influencing different aspects of the manufacturing process. Right from generating conceptual design to after-market strategies, Big Data is helping automotive manufacturers by enhancing operational efficiency in designing, building, and servicing vehicles. Consumer sentiment analysis along with data collected from drivers is offering inputs for innovations in car design. Data collected during the building process is used in predictive analytics to improve manufacturing simulation making the next assembly line more flexible and efficient. Big Data is already playing a major role in marketing of automobiles. Social sentiment analysis along with customer feedback helps marketing professionals in designing innovative vehicles and identifying important themes and messages for marketing campaigns.
Companies are analyzing customers’ preferences, habits and buying power to develop specific financing programs for their clientele. Innovative insights from Big Data analysis of sales and other data will help captive financing companies develop innovative services and revenue streams. Based on available sensor data in modern vehicles, automobile manufacturers are in a way transforming into data repositories. They are witnessing a growing volume of data which when coupled with manufacturing and development data can create tremendous value for players in the automotive industry. However, this data is not used to its optimum and might turn into useless pile of information in many cases.
Automobile makers are discovering faults faster than before. This is the result of big data analytics tools that helps in identifying issues as soon as possible. Innovative technology is enhancing quality and cutting costs. In design and manufacturing sectors, errors can be more costly with each successive production stage. Latest software helps in finding errors while the part is still in the blueprint stage thereby cutting costs involved. Businesses are leveraging innovative technology to track performance, enhance vehicle maintenance to boost mileage and lower bills. Big data analytics is helping vehicle owners to improve vehicle efficiency and cut down maintenance costs. Companies are scraping data from warranty repairs to conduct cost-performance analysis. For example, a company may remove a cheap automotive part with a costlier one, which enhances the bill of parts but it lowers warranty cost saving funds in the long run. Predictive analysis helps in identifying errors before systems malfunction optimizing the service process and helping drivers from fatal accidents.
Web crawling is helping automotive industry in diverse ways. It is helping businesses to dig up data from auto dealer database and gathering insights about customers’ feedbacks and preferences strategize marketing campaigns and many others. The players in this field are using the innovative technology to shorten design cycle, reduce development cost, and design innovative models which are more innovative, sustainable, and consistent.