POS data

What is POS data?

POS (point of sales) data is the sales information captured at retail stores with items purchased, bill amount, customer ID, and more. It’s a digital or physical record of everything sold at a retail or an eCommerce store. People call this the frontline source of truth, which connects closely with inventory, analytics, and revenue systems.

Examples of POS data in retail include: StoreID, Qty, SKUID, Price, Total, payment.

Why does POS data matter?

POS data isn’t just sales records. With proper data integration and analytics set up, you can uncover a lot of insights about retail sales. This can aid with timely decision-making – from what to stock up to how much. Here are the types of insights you can get from POS sales data.

1 - Sales trends and patterns 2 - Store and zonal comparisons 3 - Promotion and campaign performance 4 - Customer ratings and satisfaction 5 - Shrinkage and losses 6 - Staff requirements and more

Here’s how the above POS insights help retail managers and heads run better operations across stores, drive revenue, and offer smooth customer experience.

1 - With custom-built ML models and predictive features, one can generate future demand forecasts. That’s how POS analysis helps with preventing over-ordering and unsold inventory. 2 - Project vivid charts into high-selling SKUs, profit-driving products, and low-margin SKUs at zonal, store, and regional levels.
3 - Track performances of newly launched products in different regions, its acceptance, availability, and other performance factors. 4 - Retail operations team and zonal heads often need data that support zonal or regional level comparisons with trench level insights. By integrating POS data with other sources, they can compare data in real time—sales, footfall conversion, and SKU-level trends, and more. 5 - POS insights can uncover hidden losses and shrinkage by highlighting differences between sold items and storage.
6 - POS data can also help with omni-channel planning, which combines performance across store, eCommerce and application interfaces, so that the demand, sales, and inventory data lies in one place.

How POS data helps with inventory forecasting?

If you want to set up retail analytics, POS data is the most crucial input. It not only shows what happened in the past but explores connection between multiple data points and predicts future trends. Here’s how POS data can help you forecast demand and inventory.

1 - Identifies sales trends: POS data shows which products or categories sell fastest by day, week, or season. This means you can plan reorders and replenishment based on POS insights. For example: POS highlighting higher cold drinks sales on weekends in summer.

2 - Run offers and promotions based on demand: To improve profitability and demand, you can use and analyze POS data. It helps you trace the connection between discounts, campaigns, and sales, allowing room for micro changes, which results in high sales volume as well as revenue.

3 - Improves SKU-level forecasting: No more guess work as POS data can be relied on to make decisions on what SKUs perform better and how many exact units a store needs at any point of time. Modern POS systems with predictive capabilities can tell this accurately based on past sales patterns.

4 - Supports seasonality & event planning: POS history comes handy with seasonal products selling too. Example: season-based products, holidays and festival items.

5 - Reduces waste & shrinkage: Grocery and pharma retail chains often struggle with perishable goods handling. There is a risk of wastage as well as storage restraints and cost related to that. With better forecasting, perishable items (food, pharma) are stocked in line with demand. Example: If yogurt sells 200 units on a specific day, then suggestions on unnecessary re-orders or excessive storage.

What tools make POS data easier to use?

Almost every business track POS data. But are they finding it resourceful? Sadly, not all of them. The major reason why POS data remains unused is because the channels and applications are siloed. And so, as their data. Here are some tools and interfaces that help with handling POS data, automating use cases that rely on this source.

1 - BI dashboards and charts: Tools integrate two or multiple retail data sources including POS and turn raw POS transactions into visual dashboards (sales trends, heatmaps, margin analysis). Some examples include Power BI, Tableau, etc.

2 - Retail analytics platforms: If you don’t want to build custom dashboards and need pre-built templates, then there are platforms like Zoho, SiSense, etc. These tools provide pre-built templates to measure retail KPIs in real time (basket size, shrinkage %, promotion ROI).

3 - Use built-in reports from POS systems: Many POS systems like Shopify POS, Square, Lightspeed, Oracle Micros, etc., come with built-in reports. If you want daily/weekly sales summaries and performance insights straight from POS data, this will work.

4 - Integrate ERP with POS: You can connect POS data with supply chain, finance, and HR data.This expands the scope of POS analysis, making space for multi-dimensional analytics. The zonal/regional heads can see end-to-end performance (sales + stock + cost impact).

5 - AI-powered capabilities for POS systems: Once you have the basic analytics workflow, you can bring in AI into the picture. This is about using custom-built AI/ML models on POS data to predict demand, detect anomalies, and personalize promotions.

6 - Single source of truth using cloud data warehouses: SSOT works best for large retail companies with too many store, SKU, and customer databases. A cloud data warehouse set up using Snowflake, Fabric, or Databricks can help with storing and processing large volumes of POS data from multiple stores/regions.