What is Point of Sale (POS) Data? Uses, Types & Dataset Examples

How is Point of Sale (POS) Data Collected?

With POS software attached to a payment system, POS data collection can go beyond capturing transactions. POS software tracks a range of commercial information. It links customer contact details to payment methods, as well as to product data.

For example, when a consumer purchase a product, the POS software can update the retailer’s inventory details. This product, consumer and transaction data is valuable information for item and store level analytics. Often the POS tracking system is integrated with the retailer’s other business software. This enables the company to perform more complex analytics into consumer spending. In many cases, this data can be enriched with consumer transaction data for a more comprehensive analysis.

POS data is usually collected in large quantities in its raw form. The raw data undergoes cleaning and enrichment to form more actionable, commercial datasets.

What is an Example of POS Data Collection?

The best source of point of sale data is the POS machine that used for billing a customer in a store, usually a card reader. The device facilitating the transaction tracks inventory levels, units sold, and revenue earned. POS data providers form datasets with data collected from card readers. External datasets including these metrics are available to buy via a data marketplace.

Point of sale data collection is also evolving. As more supermarkets allow customers to scan as they shop, merchants count products in the customer’s basket as they go, not just at the checkout. The scanning devices can also register when a customer scans an item only to return it to the shelf. This enables supermarket managers to understand buyer behavior and hesitancy towards products.

Retailers buy retail POS data online that is relevant to the growth of their business. For example, a POS dataset can tell a merchant the place of purchase (which could be online or offline). In this instance, they can see which store locations are driving the most sales.

Automated POS data collection tools systems include Shopify, Lightspeed, Shopkeep, Magestore. These ecommerce platforms enable retailers to sell products via online sales channels. Whenever a consumer makes a purchase, these ecommerce platforms capture and collect POS data in the process.

What is a Point of Sale (POS) Data Model?

A point of sale data model is a simple visual representation of a POS database. At a basic level, the model will include the consumer, the merchant, and the goods or service purchased. They’re used by retailers of all kinds, including online stores, to visualize POS interactions.

Where is Point of Sale (POS) Data Stored?

POS data is stored in the databank and transaction history of the seller. External POS data vendors then store these datasets in secure data warehouses. Once you buy POS data, you can access it via an API, or it’s delivered to you as a data dump. To check that you’re getting high-quality data, it’s good to ask for a POS database example. Using this example dataset, you can sample and test the information to check it works for your project.

What is Point of Sale (POS) Data Analysis?

POS data analysis involves monitoring transaction data and deriving insights from it. POS data analytics vary according to your use case. In general, analyzing POS data helps retailers and suppliers understand both product and store level sales. Additionally, utilizing SKU-level transaction data can provide deeper insights into individual product performance.

How Do You Analyze Point of Sale (POS) Data?

One way of analyzing POS data is through inventory analysis. This helps in showing how well a product sells, thus helping in the calculation of the forecasting product number. Sales trend analysis is also a form of POS data analysis. Sales trend analysis involves discovering products whose sales are decreasing or increasing.

You can also analyze POS data to build customer segments and contact lists. At the point of sales, merchants get valuable information about customers. Customer data points can include names, addresses, phone numbers, email, and previous purchases. You can then analyze POS data to identify correlation between consumer and sales performance. For example, whether a consumer from a particular location or demographic is more likely to buy specific products. From here, you can map purchase intent patterns.

Point of sale data goes beyond the moment when the consumer buys the product. In many instances, it extends to when the product is returned. It’s important to get insights on the reasons for refunds and exchanges. In this sense, analysing POS data often means analyzing consumer sentiment.

How is a POS Database Structured?

The POS system is supposed to provide more than an easy way to process payments for retailers. Structured in the right way, it can reveal insights about consumer purchase behavior.

A POS database is structured to give you sufficient details of each transaction in order form proper data that can be analyzed. It’ll typically include the SKUs of the products bought, time, and payment system. With a POS database, businesses can also monitor how each product is selling and when it is time to re-stock based on real-time updates.

How to Create a POS Flow Diagram?

The first step to take is to enter receipts that show when a transaction started. The next step is ensuring these receipts show the names of the products and their prices. Then, the flow diagram should show return receipts in cases where a customer returns a product.

Once created, you should update the flow diagram with transactions done during a given period. The final step of the flow diagram should show when the transaction is successfully closed. As always, backing up the data is essential.

Can I Analyze Customer Data with a POS?

You can capture a range of data points relating to a customer at the point-of-sale. For example, if they pay electronically, you’re able to collect details shared by the payment service e.g. PayPal.

You’re also able to build customer profiles of recurrent buyers. If your POS signals that someone buys from you often, you can track their purchase behavior. For example, how often they visit, what products they buy, and how they pay. This provides a richer customer understanding for your targeting and strategies.

How Do Supermarkets and Retailers Use POS Data?

The use of POS has become very important in most supermarkets and retail stores. On a proprietary level, retailers use POS data to make purchasing processes faster. Based on POS data, store managers can instal the most popular payment methods.

A more advanced application of POS data is to capture customer contact details. For example, name, email and phone. This B2C contact data is helpful for refining marketing and advertising campaigns.

There are also marketing-specific applications of POS data. For B2C businesses, POS data helps decide on the most effective way to go about marketing campaigns. If there’s a particular price point which drives sales, marketers can adjust product prices to ensure more customers.

Shop owners also use retail POS data to manage inventory. A point of sale dataset will tell them how many units of each product has been sold. This way, they can monitor when stock is low and know when to restock.

In general POS data enables supermarkets and retailers to streamline their business operations. It makes their processes and strategies more data-drive and accurate. Most importantly, it allows them to increase profit via stronger sales. POS data improves the shopping and payment experience for their customers as well. For instance, restaurant food delivery transaction data is crucial for optimizing food delivery services.

What is POS Data Management?

POS data management requires you to update and refresh you data regularly. It also means you have to store your POS dataset securely because it’ll often contain sensitive customer and financial data. In many cases, this will include bank transaction data that must be managed with extra caution.

How Reliable is the Quality of POS Data?

The quality of POS data can vary depending on the provider’s processes and methodologies. For example, data quality is often measured by coverage, real-time accuracy, and historical consistency. Some datasets offer 100% real-time accuracy, while others ensure up to 75% correlation with reported KPIs. Our suggestion: Look for providers with transparent quality metrics and proven methodologies to ensure data reliability for your use case.

How is POS Data Secured?

POS data providers prioritize security and compliance, using secure delivery methods like S3 buckets, encrypted APIs, and rigorous data handling standards. Additionally, datasets only include anonymized and aggregated data to protect consumer privacy and ensure compliance with global data regulations. Our suggestion: Confirm that your chosen provider adheres to GDPR or other relevant compliance standards for secure and ethical data usage.

How is POS Data Delivered?

POS data is delivered in multiple formats such as CSV, Excel, or via APIs for seamless integration into your systems. Delivery frequencies vary from real-time, daily, weekly, to monthly, depending on the provider and your preferences. Data can also be customized to suit specific needs, such as aggregated or SKU-level details. Our suggestion: Opt for flexible delivery options to easily integrate data into your existing workflows.

What Factors Influence the Pricing of POS Data?

POS data pricing depends on factors like the volume of data, historical depth, geographic coverage, and usage frequency. Providers offer various pricing models, including yearly subscriptions, monthly licenses, or one-off purchases. Some providers also offer free samples to help you evaluate data quality. Our suggestion: Request a free sample and inquire about discounts for long-term subscriptions or higher volumes to find a pricing model that aligns with your budget.