Retail and commerce data is the collection of information about retail and commerce. This may be data about a single company, a market or industry, or a region.
This data comes from internal company data—financial reports, surveys, and feedback requests, mostly. Other sources include competitor data or market data analyzing social media trends, price comparisons, and product review data.
Governments and intergovernmental bodies also track reported revenue and international trade data by company, industry, and region.
Finally, there are physical and online sources of retail and commerce data. Points of interest, foot traffic data, and weather data can predict and inform sales in brick-and-mortar stores. Meanwhile, customer sentiment, price comparison, keyword trends, and product review data tell companies which products customers enjoy or look for.
What Types of Columns/Attributes Should I Expect When Working with This Data?
This data tends to be divided into physical vs e-commerce data. It is further divided into industries or regions or both.
You should expect to see revenue in one column and at least one time period in another. The revenue may be provided as a raw number followed by a percentage of the company’s or industry’s total. The dataset may also include a comparison of percent change from the last quarter or from the same time last year.
Managers, executives, and others use retail and commerce data to improve their business’s performance, products, service, marketing approach, or all of the above. They also use the data to conduct competitor and market analysis, or to weigh the wisdom of entering a new or foreign market.
Investors and lenders use the data to decide whether they will invest in a company.
Finally, governments and international agencies use the data to track the health of industries or larger economies. These institutions then use the data to argue for or against policy changes.
As in all data categories, you should be concerned about the accuracy, relevancy, consistency, and recency of the data collected. Certain sources, of course, are more reliable. Retail and commerce data from governments and international organizations is good quality, though the update frequency might not suit your needs.
Conversely, your own internal data is always up to date (barring server failure). However, data cleansing and analysis may need to be outsourced depending on your needs and capabilities.
Collecting data from third parties is generally reliable, especially if you are using a highly-rated vendor like one of the ones you will find on our site. If you are using web scraping tools or even manually checking competitor websites, however, be aware that the competitors that see your repeat visits with no purchases may show you higher prices than they offer to customers.
<3>Interesting Case Studies and Blogs to Look Into
The pandemic accelerated the growth of e-commerce by seven years within several months, according to John Koetsier of Forbes. In May, total online spending rose 77% to $82.5 billion, from the same month in 2019. … As a result of the intense competition, more orders and faster promises for shipping will put a burden on logistics networks, and smaller companies might face an increased risk of being without capacity, unable to control freight spending.
X-Byte’s dataset – ‘X-Byte | Retail food delivery data & Online food ordering App Scraping | Scrape Food App Data’ provides Company Data, Retail & Commerce Data and App Data that can be used in Targeted Marketing, and Pricing Optimization
X-Byte’s dataset – ‘Scrape Retail Data Worldwide Using E-Commerce APIs from X-Byte’ provides C2C E-Commerce Data, Retail & Commerce Data and that can be used in Price Segmentation Strategy and Pricing Optimization
X-Byte’s dataset – ‘X-Byte | Car Rental Data – Global Coverage – Datasets Spanning All Major Car Rental Sites & Aggregators’ provides Retail & Commerce Data, Automotive Industry Data and GPS Data that can be used in Price Segmentation Strategy, Pricing Optimization and