Retail Data

What is retail data?

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.

Where does the data come from?

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. 

What types of attributes should I expect?

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:

• Revenue by time period, regions, and store locations
• Comparison of percent change from last year, previous quarters, or same time last year
• Inventory management and tracking information

Business insights covering wider information include:

• Store locations, contact details, photos, and video tours in some cases
• Pricing information
• Competitive landscape including competitor revenue and advertising spend
• Consumer search trends

Some vendors provide custom datasets to match your specific requirements.

How should I test the quality of the data?

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:

• Assess the data for accuracy, consistency, and completeness.
• Validate that the data is updated frequently or in real time.
• Ensure the web scraping tools are not collecting wrong pricing by overwhelming the competitor sites.
• Check if the data matches your requirements.
• Verify the privacy compliance of data in case of personal or personally identifiable information (PII) is part of the data.

Who uses retail 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.

What are the common challenges when buying retail data?

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.

• Data timeliness: The data is constantly updated as customers visit stores and make purchases. Internal data for retail stores is updated regularly, but external data about competitors and other industry briefings may take time to get reflected in the vendor data. For leveraging actionable insights driven by the most recent data, your vendor must assure data timeliness.
• Data accuracy: Large volumes of data make it harder to ensure data accuracy. Web scraping tools also have their own challenges. If the competitors detect frequent visits from such tools, they may show different prices, which again affects data accuracy. As the raw retail data needs cleaning and quality improvement, vendors may compromise on accuracy to deliver data in near-real-time.
• Data completeness and consistency: Data collected from diverse sources may have duplicate records, inconsistencies, or overlaps. However, only consistent data can deliver trusted analysis and actionable insights. Ensuring the data powering the analytics is complete is also a challenge, as incomplete data can produce skewed results.
• Privacy compliance: Data that includes personally identifiable information (PII) must comply with the required privacy regulations.

What are similar data types?

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 Catalog.

Sign up for Explorium’s 14-day free trial to access the data available on the platform.

What are the most common use cases?

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.

• Retail Analytics: It is the process of analyzing retail information to derive insights into customer preferences, sales outlook, inventory requirements, and other relevant intelligence that drives business decisions.  The insights help improve efficiency, accelerate growth, and deliver a better customer experience. Retail data presents information about the health of the business, market, or industry to fuel retail analytics.
• Competitor Analysis: Retail businesses use diverse data categories to monitor their competitors, assessing their sales, market share, and marketing strategies. The competitor analysis provides comparative strengths and weaknesses of the competitors, which helps strategize store locations, promotions, and distribution. Retail data provides industry and market-level information on competitors to power the analysis.
• Store Performance Prediction: It indicates the sales forecasting for brick-and-mortar stores to optimize the inventory and reduce the risks of under-stocking or over-stocking. A good model of store performance prediction considers historical sales data and retail data along with a wide variety of other categories such as weather data or events data.
• Promotion Planning: Optimizing promotional tools and resources helps meet business goals efficiently. Good promotional planning improves the conversion ratio and ROI on promotional spending. Businesses typically use retail data combined with other categories of data such as brand awareness and social media presence data to assess the success of promotions. Modern promotional planning leverages ML-driven analysis to plan and achieve promotional goals. 
• Shelf Planning: Refers to optimizing the placement of products on store shelves to maximize profit. A store shelf is a dynamic environment influenced by seasonal, promotional, and fast-moving offerings, and it needs careful planning to extract the maximum benefits. ML algorithms powered by diverse data categories ensure that the space and duration of shelf display provide highest returns. Retail data contributes to shelf planning with periodic sales information along with inventory intelligence.

Which industries commonly use this type of data?

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.    

How can you judge the quality of your vendors?

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.

• Customer reviews and testimonials: Most vendors provide customer reviews and ratings, where customers describe their experience indicating the vendor’s quality. Customer testimonials typically highlight the strengths of the vendor, and you can judge if those match your needs. The reviews and testimonials often list the industry, and you can leverage that information for your planned project.
• Case studies: Many vendor websites present case studies with details of projects, industry verticals, and the datasets used. You can leverage this information to assess the vendor’s ability to deliver high-quality datasets matching the required range of data attributes. You can also infer the level of vendor commitment and the capacity to provide custom datasets. 
• Demo: Demos present data in action, exhibiting the ease of integration and the types of available datasets. A recorded demo on the website or shared on request is a quick way to assess the vendor quality. In some cases, vendors arrange a live demo, where you can also discuss your specific requirements.

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.