How to Use POS Analytics Data to Grow Your Bottom Line

  • By Matthew Davis
  • Jun 6, 2024
  • Upselling Tactics

Every time you sell a product in your store data gets recorded in your POS system. This POS analytics data is a business intelligence goldmine.

Your POS data can tell you how your business is doing, what products are driving profits, and what you can do to improve.

The problem? Many POS systems lock this data up (you must pay to access it) or make it too complex to navigate. As a result, the process of analyzing POS data becomes time-consuming, expensive, or both.

Getting ahold of POS analytics data starts with your point-of-sale system.

Top POS systems allow you to 1) access pre-built analytics reports, 2) create and customize reports, 3) automate analytics reporting, and 4) access your POS analytics from anywhere.

Need some help wrangling your point-of-sale analytics data?

Keep reading for tips on how to analyze POS data, KPIs to watch, and ideas for creating POS analytics workflows to save time.

Point of Sale Analytics: An Overview

POS analytics refers to all the data collected and stored in your POS system. POS data can include transaction data, customer data, inventory data, cashier analytics, or product data.

For retailers, POS data analysis can reveal strategies to improve operations and efficiency. You can use analysis to find ways to improve inventory efficiency, increase profit margins, enhance customer engagement, or prevent theft. In other words, POS data can improve nearly every aspect of your business.

POS Analytics Data: What to Monitor?

1. Customer Data

Your POS system collects a wealth of customer analytics insights. Your POS should gather data on A) your regular customers, and B) general customer buying behavior analytics.

For example, you could analyze these POS datapoints:

  • Customer preferences
  • % of loyalty sales
  • Buying behavior (e.g., 1.5 average visits per week)
  • Average basket value
  • Preferred payment types

How to Use POS Customer Data

Use this data to align your product mix, pricing strategy, and promotions to your customers.

For example, if you were considering selling phone chargers, you could compare the potential retail price to your average basket value. If you had a lower average order value ($7 is average for a convenience store), you should source a budget-friendly charger for your price-conscious shoppers.

Another easy win: Analyze customer purchase history to identify complementary products and suggest them at checkout. This is a sure-fire retail upselling strategy.

2. Sales Data

Sales or transaction data refers to all the details about individual sales. You can analyze POS sales data over time (like daily sales reporting ) or by segment, e.g. transactions from loyalty program members.

Sales data collected in your POS include:

  • Total # of sales (by location or POS terminal)
  • Daily or weekly peak times
  • Seasonality
  • Sales improvement (by store or across multiple locations)

How to Use POS Sales Data

Sales data is useful for forecasting product demand, gauging the general health of your business, or identifying if a promotion is working.

For example, let’s say you want to know if a promotion was successful. Analyze item sales data over time. During the promotional period, you want to see a lift in incremental sales (e.g., sales improvement that makes up for the loss of margin).

Another strategy: Identify peak sales hours and days based on POS data. If Mondays consistently show lower sales figures, for instance, consider scheduling fewer staff members.

3. Payment Data

Payment data reveals customer payment preferences. But it’s also a powerful loss prevention tool. Analyzing this data, you can identify payment anomalies or cancels/voids by cashier (which may be a sign of theft).

For example, you could monitor these payment analytics datapoints:

  • % of cash vs card
  • Payment types (by %)
  • Gift cards sales by cashier
  • Payment processing time

How to Use POS Payment Data

Identify changing customer payment preferences (e.g., card sales over time), estimate payment processing costs, or monitor cash handling metrics like reconciliation variance.

For example, you’re considering introducing dual pricing (cash discounting) to reduce credit card costs. Your point-of-sale analytics data would help you estimate cost savings and your customer’s cash preference.

You could also monitor POS data for unusual payment patterns. For example, if a store has a high number of transactions with the same credit card number within a short timeframe, this could be indicative of fraud.

4. Product Data

Maximizing profit margin starts with product analytics data in the POS. Here, you can find information related to best-selling products, profit margin, and frequently bundled items.

Additionally, you can segment product data by department, category, or vendor. Segmenting this data will help you understand valuable categories, top-selling brands, or which wholesale vendors offer the best margins.

For example, your POS data will help you identify:

  • Top 10 best-selling products
  • Avg. margin by product or category
  • Avg. sales price per item
  • Coupon/discount use by item
  • Items frequently purchased together
  • Product seasonality

How to Use Product POS Data

Use this to optimize your product mix based on performance (fast-moving, high-margin products) and to enhance profitability.

For example, let’s say you wanted to know which wholesale vendor offers the lowest margins. You could then identify that vendor’s top-selling products and estimate the impact of negotiating a better margin or find a vendor with better margins.

You could also: Track product seasonality using POS data. Stock up on popular summer items before the season begins and offer discounts on off-season products to clear inventory and make space for new arrivals.

5. Inventory Data

Inventory data differs from product data, and instead, includes datapoints that relate directly to your stock. These types of POS reports help you to know exactly how many products you have on hand, how fast they sell, and where they are in your operation.

In addition, inventory data can include information about vendors, purchase order history, or inventory trends. Some inventory KPIs you might track include:

How to Use POS Inventory Data

Inventory data helps you monitor the health of your stock, including identifying deadstock items, low stock items, or your inventory turnover ratio (how fast you sell items).

For example, let’s say you wanted to improve inventory efficiency. Your inventory analytics data would show counts for items, deadstock items (obsolete or slow-moving), and sell-through rates. You could then use this data to remove deadstock or items with low sell-through rates.

You could also use POS data to identify slow-moving inventory. Offer targeted promotions or discounts to clear out these items and free up cash flow for faster-selling products.

6. Employee Data

Employee analytics includes information related to payroll, as well as performance. The latter, cashier metrics, allows you to identify and investigate suspicious cashier activities like discount abuse or misuse of voids.

For example, you could track these employee metrics:

  • Labor costs
  • Voids by cashier
  • Cash variances by employee
  • Discount use by cashier
  • Birthdays entered / suspicious birthdays (useful for age-restricted retail)

How to Use Employee POS Data

Use this information to investigate possible employee theft, monitor compliance (ID checks), or assess performance (e.g., # of transactions per minute).

For instance, let’s say you notice a cashier has a disproportionate number of voids for a particular location. You can monitor the # of voids over time and compare that to cash variances in reconciliation reporting.

You could also use analytics to track cash drawer discrepancies during the POS reconciliation process. This can help identify potential cash handling errors or shrinkage (unexplained loss of inventory or cash).

How to Analyze POS Data: Tips & Strategies

Without a user-friendly POS system, analyzing data is time-consuming. Your POS should facilitate faster analysis, by offering:

  • Analytics dashboards for KPIs
  • Pre-built reports
  • Customizable reports
  • Quick access to your saved reports

If your POS offers that, you can get started. In general, the first step is to define your KPIs and set goals. But you might be wondering how do I choose KPIs? And then what?

Follow this process to get better results from your POS analytics data:

1. Choose Well-Defined KPIs

Set SMART goals for analyzing POS data.

SMART stands for Specific, Measurable, Attainable, Relevant, and Time-bound. For example, you could set a SMART goal to: “Increase average basket value by 10% within the next quarter.” Here’s why it’s a SMART goal:

  • It’s very specific (increase ABV by 10%)
  • It’s measurable (thanks POS data!)
  • It’s attainable (+10% is very possible)
  • It’s relevant to the business (more $$!)
  • It’s time-bound (within the next 3 months)

Commonly, you’ll set 3-5 SMART goals to tackle at a time.

2. Set Up (and Automate) Your Reports

Next, create analytics reports in your POS to measure each goal.

In FTx POS, you’ll find 100s of pre-configured reports. Plus, you can create custom reports and save your customizations.

However, you don’t want to have to pull the report manually every time. So, you’ll want to schedule your reports in the POS. This will automate reporting based on the timeframe you select.

3. Visualize the Data

In FTx POS, your store’s dashboard will help you visualize KPIs. A dashboard transforms tabular data into charts and graphs. With a dashboard, you can quickly analyze progress on your SMART goals.

Another option: Export your data to a data visualization tool to create custom dashboards.

4. Regularly Review and Refine

Regularly review your progress and make changes. For a quarterly goal, weekly or bi-weekly reporting will help you visualize progress.

Ideally, you’ll be on track. But let’s say, you have that quarterly goal to grow average basket value by 10%. At the end of Month 1, you notice you haven’t made progress. What now?

With these POS analytics insights, you could:

  • Improve your cross-sell or upselling techniques. Could you choose a better product bundle to promote?
  • Try new signage. Make design changes to digital signage displays to promote your offers.
  • Optimize your merchandizing. Test displaying high-margin impulse buys near the checkout.

Common Mistakes in POS Data Analysis

As you create your retail reporting system, be sure to steer clear of these common analytics mistakes:

1. Lack of Clear Goals

Without defined goals, it’s difficult to measure success or identify areas for improvement. Clearly define your objectives (e.g., increase sales by X%, improve customer retention) to guide your data exploration and reporting.

Pro Tip. If you’re new to retail analytics analysis, find your baselines, e.g., average margin, daily sales metrics, average customer visits per week. From here, you can find areas to improve.

2. Not Segmenting Your Data

Looking at overall averages can mask important trends. Segment your data by customer demographics, product categories, or sales channels to uncover hidden insights and opportunities.

Some segments to analyze include product-based segments (like brand or category), customer segments (loyalty subscribers vs non-members), payment segments (cash vs card), or sales-based segments (items by time).

3. Data Overload in Reports

Overcrowding reports with too many metrics makes them difficult to read and interpret. Prioritize the most relevant KPIs (key performance indicators) for each report.

For example, if you’re measuring our quarterly average basket goal, you might limit your report to:

  • Average transaction value
  • Units sold per transaction
  • Performance of upsells
  • Product bundles / combos sold

Ideally, your report would describe how you did for the week and compare that to the previous week.

Turn Data into Action

Finally, the biggest mistake you can make is not taking action. The true value of POS comes from the actions you take. Don’t just generate reports; use the data to create strategies to improve your business performance.

Getting Started with Analytics in FTx POS

FTx POS gives you the analytics data you need to improve your business. Some stand-out features of our reporting tools include:

  • Hundreds of detailed reports – Sales, customer, inventory, and more.
  • Easy report scheduling – Automate reporting based on the interval you choose.
  • Report sharing – Send a generated report to stakeholder emails automatically.
  • Customized POS data – Use filters to build custom reports and save them to your Favorite Reports to access.
  • Handy KPI dashboard – View sales KPIs for your store in your Admin Dashboard.

Too many POS systems limit access to your own data. In our platform, you can build and refine a reporting environment that takes your store to the next level.

FTx POS & Advanced Analytics. Learn how to grow your business with FTx POS and data analytics. Schedule a demo today to learn about this core feature.


POS analytics offers a wealth of insights to improve your business. You can use it to:

  • Increase sales and average basket value
  • Improve inventory management and reduce stockouts
  • Identify customer buying trends and preferences
  • Optimize staff scheduling and resource allocation
  • Track marketing campaign effectiveness
  • Gain insights into employee performance (e.g., cashier void rates)

POS systems collect a wide range of data, including:

  • Transaction data (sales details, items purchased, prices)
  • Customer data (purchase history, demographics, loyalty program information)
  • Payment data (payment methods, cash vs. card usage)
  • Inventory data (stock levels, product popularity, sales trends)
  • Employee data (sales performance, hours worked, discounts used)

Here are a few ways to use POS data to boost sales:

  • Analyze customer purchase history to recommend complementary products at checkout (upselling/cross-selling)
  • Track peak sales hours and adjust staff scheduling accordingly.
  • Analyze abandoned cart data to identify reasons why customers leave purchases incomplete and refine your checkout process to improve conversion rates.
  • Offer targeted promotions and discounts based on customer demographics or buying habits.

Here are a few examples of reports you can generate using POS analytics:

  • Average basket value report: Track average transaction value over time and compare it to your goals. Identify factors influencing average basket value.
  • Inventory performance report: Analyze stock levels, sales trends, and identify fast-moving vs. slow-moving inventory.
  • Cashier performance report: Track sales performance, voids, and discounts used by individual cashiers to identify potential areas of improvement

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Business Experts & Contributors

A New Solution Coming To FasTrax

Danielle is a content writer at FTx POS. She specializes in writing about all-in-one, cutting-edge POS and business solutions that can help companies stand out. In addition to her passions for reading and writing, she also enjoys crafts and watching documentaries.

Danielle Dixon

Content Writer
A New Solution Coming To FasTrax

Matthew Davis is a content marketing specialist for FTx POS. With experience in marketing, brick-and-mortar retail, and ecommerce, Matthew enjoys writing about strategies and technology retailers can use to grow. Previously, he managed retail operations for a sports/entertainment facility and worked in marketing consulting.

Matthew Davis

SEO Specialist/Content Writer

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