Using POS Data to Understand and Improve Profitability

According to an IBM survey, 62% of retailers say that big data and analytics give them a competitive advantage in their field. In order for companies to succeed, they need to leverage the analytics that today’s best point-of-sale (POS) systems can provide.

Information about customer shopping behavior and product purchases can be collected and analyzed, allowing companies to tailor their strategies for increased profitability and customer satisfaction.

With POS data, it’s important to know what various metrics mean and why they matter. Here’s a look at three major indicators, and how businesses can use them to boost their profits.

Retail conversion rate

Your retail conversion rate tells you how many customers visit your store and buy something. Not all store patrons will make a purchase — some may be browsing ahead of pay day. You’ll want to turn as many visitors into customers as possible, so keeping an eye on the conversion rate helps you measure your success. If you notice consistently low rates, you can start identifying what changes would improve that number. That said, not enough stores make use of this metric: Forrester reports that only one-third of retailers measure their retail conversion rate in-store.

To calculate the retail conversion rate, divide the total number of daily transactions by the total number of daily visitors, and multiply the result by 100 for the percentage.

Total number of daily transactions / total number of daily visitors * 100 = X%

Tip: Find ways to count by customer rather than by channel. An offline customer and an online customer — commonly thought of as two distinct channels — might be the same individual. While physical entry into a store can be clearly measured with infrared or thermal sensors, how can businesses know whether that shopper has already visited the website or not?

What if the same customer visits the store and the website three or four times before making a purchase? That might look like a bad conversion rate, but that’s not necessarily true. Every store is different. For example, big purchases may require more visits over several days. If that’s the case, use a smaller window of measurement, like daily conversion rate, instead of a weekly conversion rate. Then, use the number as a baseline that you try to improve, and avoid trying to compare it to other stores.

Days of supply

This metric tells you the length of time before the current stock sells out, assuming a given rate of sales. The greater the visibility into days of supply — considering the type of day (weekend? holiday?) or product (luxury good? perishable?) — the better retailers can forecast demand, delivery dates, and inventory. This information helps with planning discounts, anticipating restocking orders, relocating supply, and more.

Tip: Incorporate an endless aisle solution into your brick-and-mortar store. No matter how many days of supply are left in a particular store, products are always available for purchase. Supply chain disruptions are a fact of life — whether they happen because of bankruptcies, natural disasters, or other phenomena — so there will come times when patrons can’t find what they want. With digital merchandising kiosks, tablets, or other solutions, the aisle really is endless.

Sell-through percentage

The sell-through percentage analyzes the sales performance of a particular product. Basically, it states how much of the item has been sold during a certain period (like one month). To calculate this figure, divide the average number of units sold by average number of units stocked, and multiply the result by 100 to turn it into a percentage.

Average number of units sold / average number of units stocked * 100 = X%

Building off POS data, products can be combined into different categories for analysis: for instance, are cosmetics outselling apparel? It’s not just individual products that can be tracked, but product categories as well.

Tip: Pay attention to particular categories at particular times. Sales of decorations, for instance, will be far better in the months leading up to Christmas than afterward. Your POS data should register popular categories to measure so that you can track which ones do especially well.

The value of POS data

The more POS data retailers have, the better equipped they are to understand their customers and find ways to improve sales. By implementing the right retail technology to collect and analyze this data, companies put themselves in a better position to succeed.

For a powerful cell phone store POS system that will provide better visibility into your inventory and help you manage your wireless retail business more efficiently check out RQ, our cell phone store POS.

Feature Photo: LDprod /