Commerce Data: Best Commerce Datasets & Databases
What is Commerce Data?
Retail commerce data is information about the retail market, individual businesses, or their performance. It's mostly used by marketers and investors e.g. in optimising store layouts or identifying market trends. Datarade helps you find the right data providers and datasets.Learn more
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The Ultimate Guide to Commerce Data 2023
What is Retail Data?
Retail data is data you collect about your retail customers or the industry in the larger scale that can be used to improve your business. Retail data takes guesswork out of the equation and enables you to make smarter decisions. It might include point of sales data, loyalty card data and market data.
Other forms of data you’ll want to consider include customer- centric data, supply chain and merchandising data. Foot traffic data allows you to measure which departments of your business in different locations are popular and which are getting less attention. Such insights can help to make decisions on how better to attract customers’ attention. Historical data looks at previous sales and inventory data to help Retailers make decisions. Retail Data enables your business to make smarter decisions, obtain higher profits, gain greater customer satisfaction and have a more competitive store. All in all, when used correctly, data will help you will increase your business efficiency, save money and drive sales.
Who uses Retail Data and for what use cases?
In today’s market, where customer centric approach is everything, it’s important to stay one step ahead of your customers. Retail data gives you the information retailers need to stay ahead and competitive. It allows retailers to better understand their customers and their customers’ needs, wants, spending and eating habits. It allows retailers to go that step further in creating a great retail experience for their customers. Retail data can analyze who were your best customers and what they spent, and which products are most popular among your customers. This can help with ordering decisions and with deciding which products to promote.
Retail data can also help sales teams and marketers to target the right customer at the right time. Analyzing the timing of purchases helps predict what customers may buy next and when. For example, consider a customer who just bought clothes size 0-3 months for her newborn baby from your baby store. Understanding general buying patterns, you can predict when she will need to buy 3-6 months’ baby clothes and 6-12 months’ baby clothes, plus when she may be thinking of shopping for a high-chair, baby walker and so on. Using these predictions, you can recommend products to your customer at relevant times.
Retail data can also be used for streaming. For instance, if retail data company analytics show not many customers shop in the evening midweek, you can consider not opening on midweek evenings, or at least reducing staff on those evenings. You can also use retail data to plan for seasonal fluctuations. Retailers can learn a lot about their market and customers from retail data, and, indeed, more and more businesses have recognized the benefits and use retail data attributes to help make important decisions and find retail business solutions.
What are typical Retail Data attributes?
Retail reporting these days is about more than ringing up sales. Retail data attributes provide businesses with information on how best to lay out departments, aisles and shelves. Retail data sales reports can tell you which products or supplies are driving revenue so you can plan your stock orders accordingly. Retail data allows retailers to predict trends and forward plan. This saves time and money. Having the right data also increases customer satisfaction. Retail data analytics helps retailers to be keenly aware of their customers’ tastes and preferences. This is information retailers can use to increase the value of customers’ experiences. Retailers can see what customers like and enables them to make recommendations based on the customer’s retail history.
Common Retail Data attributes might include:
- customer data, such as account number, gender, DOB, postcode, time period
- store space data, such as floor product, sales turnover, space format
- transactions made with loyalty card, such as account number, store number, value (£), product category, timestamp, time period.
- Transactions without loyalty card, such as store number, value (£), product category, timestamp, time period.
- POS data, such as profit margins, basket sizes, customer counts, sales trends, staff streamlining.
- Foot traffic, such as people counters, beacons and dwell times.
How is Retail Data typically collected?
Retail Data analytics is collected in raw form. It can then be reviewed and cleaned, analyzed and standardized so unit can be integrated into a database. Upon delivery, data may be stored in spreadsheets such as Excel. Another option are CSV files, if you need to share between systems that are incompatible. Data can be stored in data warehouses owned by data providers where it is transformed into a data format ideal for querying and analysis.
The collection can happen in a number of ways, such as by surveys, by having conversations with your customers, and by collecting background data via modern technologies. These different kinds of customer information can then be combined using data analytics to give a more complete picture of your customers’ shopping decisions.
While survey and 1-1 discussions with your customer may yield deep, descriptive insights, the data collection in such a way is not scalable. In often cases we see companies looking for third party data providers who focus on raw data collection for retail analytics. They have extensive expertise on the matter and can use innovative technologies to bring you insights about the market at scale.
Analyzing both offline and online Retail Data is crucial as today customers purchase from multiple channels. Combining data is necessary to gain key insights and increase customer satisfaction. For instance, some retailers have found that customers browse their products online but prefer to purchase their products offline in store. Data enrichment is an essential step to understanding your customers’ path to purchase.
How to assess the quality of Retail Data?
Retail data that is of poor quality will have a negative effect on your business. Raw retail data needs to be reviewed regularly to ensure it is of good quality and any errors removed. For the purpose of many use cases, it is necessary to ensure data is offered in real time, as outdated data is inefficient, ineffective and can have an adverse effect on your business.
Data needs to be tracked regularly for the number of errors, empty values and dark data. The most important thing is to have a data quality assessment plan in action to regularly check your data quality.
Before buying data from third party sources, you should look for previous customer refernsers and ask your data provider for a sample set to test the data in its intended environment for the ultimate pre qualification of quality.
How is Retail Data typically priced?
The price you pay for retail data depends on the size of your company and the sophistication of the system you want to purchase. Retail data is typically purchased by monthly subscription that gives you access to a real time API. Alternative pricing models could consist of pay per usage (CPC), one time payments, or even customized quotes based on your needs.
What are the common challenges when buying Retail Data?
Although retail data is essential for today’s business market, it doesn’t come without challenges. Some challenges when buying retail data include:
- Siloed data that is not combined. Combined data and looking at customers as a whole is essential to obtain accurate customer information so you can target more effective marketing campaigns
- Ensuring real time analysis for supply chain management. Real time analytics are essential for efficient inventory management.
- Ensuring Retail Data is compliant with the new customer compliance rules that are coming into force.
- Security, it is important to ensure any Retail Data purchased is secure and doesn’t put anyone’s private information in jeopardy.
What to ask Retail Data providers?
Setting up the right systems and learning to use them may take a bit of work. But Retail Data analysis is becoming more and more necessary to stay competitive. Thinking of purchasing a Retail Data system? These are some questions you might consider asking your data providers:
- How is the data qualified?
- Will it integrate with my existing business technologies?
- How often is the Retail Data updated?
- What security and compliance policies are in place?
- What dataset delivery method do they offer?
Where can I buy Commerce Data?
Data providers and vendors listed on Datarade sell Commerce Data products and samples. Popular Commerce Data products and datasets available on our platform are Buy eCommerce Leads | eCommerce Leads Database 2023 | eCommerce Lead List with Store Owner Contact Details | 100% Real-Time Verified Data by Lead for Business, WooCommerce stores list - 1.6M+ stores worldwide by ScrapeLabs, and Web Scraping Services| Extract & Analyze Data | Scrape Ecommerce websites by TagX.
How can I get Commerce Data?
You can get Commerce Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Commerce Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Commerce Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Commerce Data?
Commerce Data is similar to Environmental Data, B2B Data, Energy Data, Real Estate Data, and Geospatial Data. These data categories are commonly used for Retail and Predictive Analytics.
What are the most common use cases for Commerce Data?
The top use cases for Commerce Data are Retail, Predictive Analytics, and Online-To-Offline (O2O) Analytics.