Big Data Analytics in Retail Market Size, Trends & Demand to 2030

Research Methodology – Big Data Analytics in Retail Market

At Straits Research, we adopt a rigorous 360° research approach that integrates both primary and secondary research methodologies. This ensures accuracy, reliability, and actionable insights for stakeholders. Our methodology for the Big Data Analytics in Retail Market comprises the following key stages:

Market Indicator & Macro-Factor Analysis

Our baseline thesis for the Big Data Analytics in Retail Market is developed by integrating key market indicators and macroeconomic variables. These include:

Factors considered while calculating market size and share

Volume of data generated by the retail sector.
Number of retail businesses adopting big data analytics.
The incremental growth of data usage over time.
The existing market of retailers investing in data analytics infrastructure.
Existing technological infrastructure and investment in IT and analytics services in the retail sector.
Demand for predictive analytics in the retail industry.
Revenue generated from the application of Big Data in the retail sector.

Key Market Indicators

The scale of digital transformation in the retail industry.
Number of retailers using consumer data for personalized marketing.
Penetration of e-commerce platforms utilizing big data analytics.
Investment in AI and Machine Learning to understand consumer behavior in the retail sector.
Growth of cloud-based analytics services in retail.
Artificial intelligence (AI) and Machine Learning (ML) adoption in retail industry.
Increased use of IoT technologies in retail.

Growth Trends

Increasing trend of using big data analytics for personalized recommendation and customer engagement in retail.
The rise in implementation of AI-Driven advanced analytics in the retail industry.
Growth in usage of predictive analytics for inventory management in retail.
Trend towards real-time analytics for improving customer experience.
Increasing usage of big data analytics for price optimization.
Adoption of omnichannel strategies facilitated by big data analytics.

Secondary Research

Our secondary research forms the foundation of market understanding and scope definition. We collect and analyze information from multiple reliable sources to map the overall ecosystem of the Big Data Analytics in Retail Market. Key inputs include:

Company-Level Information

Annual reports, investor presentations, SEC filings
Company press releases and product launch announcements
Public executive interviews and earnings calls
Strategy briefings and M&A updates

Industry and Government Sources

Country-level industry associations and trade bodies
Government dossiers, policy frameworks, and official releases
Whitepapers, working papers, and public R&D initiatives
Relevant Associations for the Big Data Analytics in Retail Market

Market Intelligence Sources

Broker reports and financial analyst coverage
Paid databases (Hoovers, Factiva, Refinitiv, Reuters, Statista, etc.)
Import/export trade data and tariff databases
Sector-specific journals, magazines, and news portals

Macro & Consumer Insights

Global macroeconomic indicators and their cascading effect on the industry
Demand–supply outlook and value chain analysis
Consumer behaviour, adoption rates, and commercialization trends

Primary Research

To validate and enrich our secondary findings, we conduct extensive primary research with industry stakeholders across the value chain. This ensures we capture both qualitative insights and quantitative validation. Our primary research includes:

Expert Insights & KOL Engagements

Key Opinion Leader (KOL) Engagements
Structured interviews with executives, product managers, and domain experts
Paid and barter-based interviews across manufacturers, distributors, and end-users

Focused Discussions & Panels

Discussions with stakeholders to validate demand-supply gaps
Group discussions on emerging technologies, regulatory shifts, and adoption barriers

Data Validation & Business POV

Cross-verification of market sizing and forecasts with industry insiders
Capturing business perspectives on growth opportunities and restraints

Data Triangulation & Forecasting

The final step of our research involves data triangulation ensuring accuracy through cross-verification of:

Demand-side analysis (consumption patterns, adoption trends, customer spending)
Supply-side analysis (production, capacity, distribution, and market availability)
Macroeconomic & microeconomic impact factors

Forecasting is carried out using proprietary models that combine: