How Can POS Data Analytics Help in Optimizing Operations?

Nowadays, the majority of businesses that operate in retail and hospitality industries rely on point-of-sale (POS) systems to streamline their back-office operations and allow a better understanding of their customers. Unfortunately, many of these businesses don’t realize the full potential of these systems due to the lack of expertise in data analytics.

However, POS software development today also means integration with APIs, which significantly simplifies data analysis and unveils a range of opportunities to improve customer experience, streamline inventory management, and optimize staffing.

Inventory Management

Inventory management is a rarely exciting yet extremely important task that requires precision and needs to be done routinely. POS software can help businesses automate and optimize it. For example, a POS system can automatically hide out-of-stock items from online stores, provide information on item quantities, returns, etc. Furthermore, combined with careful analysis, a well-thought-out POS system can automatically establish certain thresholds for every item and send alerts as soon as item quantity isn't sufficient, ensuring that demand is met at all times.

When it comes to multilocation retailers, POS software becomes invaluable. If one particular item is sold out in one of the stores, sales staff don’t need to guess or call other stores to know if the same product is available in other locations as the POS system can provide accurate information on every SKU in every store.

Sales Analytics

POS-enabled inventory optimization and automation should be seen as a bonus to elaborate sales reports, which can help businesses better understand purchasing trends in a given region and over a certain time period. For retailers, it’s critical to know how particular items sell during low and high seasons, holidays, promotional activities, etc. This helps with adjusting prices, determining discount values and time, identifying consumer demands, and assessing the effectiveness of loyalty programs.

However, it’s often extremely hard to identify if that sudden bump in sales is caused by the unavailability of the same item among competitors or if it's tied to a holiday season, or both.

For decades, expert retail consultants would spend hours on end trying to figure out the combination of factors that influenced sales patterns. Not only was it a rather complex task by nature, but each retail niche often had a different set of factors that impacted sales fluctuations. This is why many POS software developers now turn to custom artificial intelligence (AI) models to facilitate advanced data analytics.

This isn't to diminish the power of non-AI data analysis, though. For the majority of retailers, getting elaborate reports about store performance is usually enough to make much more informed business decisions than they used to. Successful retailers are well aware that consumer purchase patterns and trends can rapidly change, making proactive, data-based decision making a massive prerequisite for achieving profit targets.

Employee Reports

In brick-and-mortar stores, product layout and employee performance are among the most important success factors. With POS software in place, retailers can view sales per employee in a given timeframe and track their progress, making it clear who deserves rewards and promotion, who needs training and upskilling, and who needs supervision.

Quite often, certain employees are great at selling one type of item over another. POS systems allow tracking these sales "preferences," which can give valuable insight. Why are they more prone to recommend particular products? Is your products’ sales performance influenced by sales staff’s preferences? More often than not, businesses fail to ask these important questions unless they peek into POS analytics.

POS analytics can also help with staffing optimization. For example, by identifying a store’s busy hours and analyzing footfall, you can more accurately predict the optimal number of cashiers and sales assistants. This is especially relevant for businesses with multiple locations, where staff scheduling effectiveness directly correlates with customer service and can lead to significant economic savings.

Lastly, a POS system can be used to detect dishonest cashiers. Depending on the store, numerous red flags can indicate fraudulent activities of some employees. Advanced POS systems have embedded rule-based algorithms, which can automatically notify the relevant department about excessive amounts of quantity overrides, manual item ring-ups, etc. Similarly, if one checkout lane has significantly more or less activity than others, an ongoing fraudulent scheme might be present.

Customer Insights

Let’s recall a sales 101 rule: it’s cheaper to retain existing customers than acquire new ones. To better understand what needs, wants and desires your customers have at different times, you need to gather data about them. Coupled with customers’ personal information collected via an online store and other channels, POS software can provide numerous opportunities for customer segmentation and personalization.

For example, repeat customers who haven’t come back in a while might be incentivized with a limited-time offer or a special discount. Your most loyal customers can be targeted with referral programs or given early access to a new product to make them more involved or even turn them into brand ambassadors.

Conclusion

Regardless of the industry, data analytics is the name of the game. The aforementioned examples of using POS to gain various business insights just scratch the surface of what these systems are capable of. With current advances in machine learning, computer vision, and Internet of Things, it’s not long until POS systems will automatically analyze in-store data in real time and make some decisions without human intervention.

Ivan Kot is a customer acquisition director and IT solution manager at Itransition, a software development company.