Retail data provides information about retail stores, companies, markets, industries, and regions. This information powers market research, industry analysis, and retail data analytics for increasing sales, driving growth, and better decision-making.
All retail businesses use this data to get a clear picture of their company and its market segment, perform competitor analysis, plan sales and marketing strategy, and improve customer experience.
Governments and intergovernmental agencies track reported revenue and international trade data. This data is sorted by company, industry, market, and region.
Retail industry data points for specific companies comes from internal as well as external sources. The internal company data consists of surveys, customer feedback, sales reports, financial reports, and similar data. This data is collected using in-store surveys and point of sales (POS)technological tools.
External sources provide competitor data, specific market data, retail trade data, price comparisons, online trends, social media data, and product review data.
A variety of consumer behavior, demographic, and other data categories augment retail data for analytics. They include point of interest (POI) data, foot traffic, customer sentiment, keyword search trends, website reviews and rating data, social media presence, and brand sentiment data among others. For example, companies leverage POI, weather, and events data to predict retail sales.
The data can be segmented by industries, or regions, or both. The attributes are typically divided by customer insights and business insights.
Customer insights provide customer behavior data along with customer feedback and reviews.
Business insights for the company include:
Business insights covering wider information include:
Some vendors provide custom datasets to match your specific requirements.
Data coming from governmental and intergovernmental agencies is often of good quality. However, it is not updated frequently, therefore its important to check if it matches your requirements.
Data from internal sources is updated regularly, meaning you can get more recent data. For internal data, you can either get quality assurance from your internal team or outsource data cleansing and quality improvement to a trusted vendor.
Data from external sources needs thorough testing for accuracy, consistency, and timeliness. If data collectors use web scraping tools, it is essential to test their reliability, especially for competitor pricing data. While a large variety of external data is available, it is prudent to assess if the categories are relevant to your use cases.
To test the quality of the data:
Marketers and company executives use retail data for market and industry analysis to improve the company performance, marketing strategy, products, and services. The data can be used to optimize their supply chains and inventories. They also leverage customer insights, trends, and preferences to enhance customer experience.
Companies use retail data to perform competitor and market analysis, including analysis for entering into new markets. Investors and lenders rely on this data to make investment decisions. Governmental and intergovernmental agencies utilize retail data to track the health of industries. Based on the analysis, they modify the policies as required to strengthen the retail markets.
The most critical challenge when buying retail data is its timeliness. Data from governmental sources is typically updated every quarter or every year, presenting the general health of a specific industry or market. It may not deliver the best results if used in trend forecasting.
Data from vendors is updated fairly regularly, though getting near-real-time data is a challenge. Accuracy of data is also a concern, especially due to the large volumes of data available.
Other common challenges include completeness, consistency, and compliance with privacy regulations.
Retail data is similar to eCommerce data, shopper data, brand data, consumer review data, product data, and other related data categories used in retail analytics and marketing.
You can find a variety of examples of consumer and company data in the Explorium Data Gallery.
Sign up for Explorium’s 14-day free trial to access the data available on the platform.
The most common use cases for retail data include retail analytics, competitor analysis, store performance prediction, promotion planning, and shelf planning. This data also drives retail intelligence, market share analysis, and operational intelligence.
Industries with a retail presence commonly use this data for powering sales and marketing strategies. They include tourism, sports, entertainment, travel, hospitality, food services, leisure, retail, CPG, healthcare, financial service providers, insurance providers, and banking.
Vendors use third-party data sources, web scraping tools, and other methods to deliver external data. Due to the large data volume and diverse nature of sources, the data quality depends on vendor quality. Judging the quality of vendors requires leveraging the information available on their websites as well as discussing directly with their reps.
Interacting with vendor reps: The quickest and the best way of judging the vendor quality is directly discussing with vendor reps. You can explain your requirements, check the available datasets for your projects, resolve your queries, and decide the next steps. Interacting with vendor reps helps to immediately assess vendor capabilities, knowledgeability, and interest in your project.
