Grand View Research employs comprehensive and iterative research methodology focused on minimizing deviance in order to provide the most accurate estimates and forecast possible. The company utilizes a combination of bottom-up and top-down approaches for segmenting and estimating quantitative aspects of the market. In Addition, a recurring theme prevalent across all our research reports is data triangulation that looks market from three different perspectives. Critical elements of methodology employed for all our studies include:
Preliminary data mining
Raw market data is obtained and collated on a broad front. Data is continuously filtered to ensure that only validated and authenticated sources are considered. In addition, data is also mined from a host of reports in our repository, as well as a number of reputed paid databases. For comprehensive understanding of the market, it is essential to understand the complete value chain and in order to facilitate this; we collect data from raw material suppliers, distributors as well as buyers.
Technical issues and trends are obtained from surveys, technical symposia and trade journals. Technical data is also gathered from intellectual property perspective, focusing on white space and freedom of movement. Industry dynamics with respect to drivers, restraints, pricing trends are also gathered. As a result, the material developed contains a wide range of original data that is then further cross-validated and authenticated with published sources.
Our market estimates and forecasts are derived through simulation models. A unique model is created customized for each study. Gathered information for market dynamics, technology landscape, application development and pricing trends is fed into the model and analyzed simultaneously. These factors are studied on a comparative basis, and their impact over the forecast period is quantified with the help of correlation, regression and time series analysis. Market forecasting is performed via a combination of economic tools, technological analysis, and industry experience and domain expertise.
Econometric models are generally used for short-term forecasting, while technological market models are used for long-term forecasting. These are based on an amalgamation of technology landscape, regulatory frameworks, economic outlook and business principles. A bottom-up approach to market estimation is preferred, with key regional markets analyzed as separate entities and integration of data to obtain global estimates. This is critical for a deep understanding of the industry as well as ensuring minimal errors. Some of the parameters considered for forecasting include:
• Market drivers and restraints, along with their current and expected impact
• Raw material scenario and supply v/s price trends
• Regulatory scenario and expected developments
• Current capacity and expected capacity additions up to 2025
We assign weights to these parameters and quantify their market impact using weighted average analysis, to derive an expected market growth rate.
This is the final step in estimating and forecasting for our reports. Exhaustive primary interviews are conducted, on face to face as well as over the phone to validate our findings and assumptions used to obtain them. Interviewees are approached from leading companies across the value chain including suppliers, technology providers, domain experts and buyers so as to ensure a holistic and unbiased picture of the market. These interviews are conducted across the globe, with language barriers overcome with the aid of local staff and interpreters. Primary interviews not only help in data validation, but also provide critical insights into the market, current business scenario, and future expectations and enhance the quality of our reports. All our estimates and forecast are verified through exhaustive primary research with Key Industry Participants (KIPs) which typically include:
• Market-leading companies
• Raw material suppliers
• Product distributors
The key objectives of primary research are as follows:
• To validate our data in terms of accuracy and acceptability
• To gain an insight into the current market and future expectations
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