Almost every business today is going digital somehow, and it has become normal that collected data and documents are saved in the clouds.
For private persons, owning a smartphone is normal, and most people cannot imagine a day without going online, like photos or facetime with friends. Because of this, the amount of data created and collected each day is exploded.
With this expansion in available data, new tools have emerged, with the purpose to use the collected data to gain insight and knowledge – that will help grow the business.
Data Science covers all these tools, models, and algorithms that make it possible to handle and use the data for each company’s own business.
This article will explain how a company can generate insight and value by analyzing internal historically collected Point-of-sales (POS) data.
What is data science?
In short terms, data science is a blend of data inference, algorithm development, and technology – put together to solve and answer complex business questions. A data scientist seeks to make discoveries by using advanced algorithms to analyze and generate predictions from a large amount of data. The analysis outcome can be precious for the future of the company.
The results from a data science analysis makes it possible to take data-based business decisions and get an actionable understanding of customer buying behavior, cash flow, stock management, etc. The only purpose data science has is to find areas to save or generate money for the company.
If you want to read more about data science in business, I recommend you the article: Why data science is important for your business.
What is POS-data?
POS-data is collected by a company when a transaction happens. It can be online purchases, checkouts at a retail store, handheld POS-hardware, QR scanners for apps, etc.
When shopping online POS-data is all the information you as a customer have to plugin before you can hit the “buy now” button.
In physical stores is the machine at the counter, there is registering each product you are buying.
The data collected through a POS-system is known as passive data collection, where the customer is not required to take any specific action on the website. The POS-system will typically gather information about: product brought, amount of product brought, time of sale, geographic location, and sometimes demographic data.
Ways a POS-analysis can create value
A POS-system can be much more than a way for your customers to make a payment. Only a few business owners and CEOs are leveraging the available data from the POS-system.
Based on the data collected from the POS-system is it possible to predict user preferences based on a historical profile of interactions with the company or webshop 
There are many examples of how POS- analysis can have a significant impact on your business. One of the most notable is inventory management. You cannot run a successful business without knowing what you are selling. The best way to effectively manage your inventory is to know and understand what your customers are buying.
By using data science, you can predict what your customers want and when they want it. Knowing what and when a previous customer wants a new specific product, you can optimize your inventory and always make sure that you have the right amount of products in stock.
It is not only enough to know what product your customers want. You need to inform them that they can buy that product in your shop – which leads us to the next area where POS-data mixed with data science can increase revenue:
When you know when and what product a customer wants, and with the correct tracking on your webshop, you can target your previous customers with campaigns promoting the exact product they most likely would buy, at the time they are most likely to buy it.
Based on the POS-analysis results, it is possible to improve and maximize your revenue from the ads and minimize your marketing spending.
Knowing what your customers want, when they want it doesn’t only improve inventory management and marketing, it also optimizes the way your sales department operates:
Because you now know, when your customer wants a specific product, your sales team can divide the leads of the day into categories based on how likely each customer is to make a purchase.
POS-analysis makes it possible to identify the customers there doesn’t follow the defined buying patterns. Classifying which customer there isn’t following the predicted and average ‘buying road’ makes it possible for the sales team to contact these specific customers directly.
The direct contact can lead to a sale, but most importantly, it gives the company insight about why some customers chose to buy the product from the competitors. It could be insight as; wrong price setting, perhaps the competing product contains an extra function, or the delivery time is faster.
Because the sales team now knows why some customers churn and seek other suppliers, the sales team can change the factors that make them lose the sale. They can lower the sales price, offer faster delivery, or get the production to add the extra feature on the product – to avoiding customer churn.
This moves us to the next area there can be optimized by a POS-analysis, and don’t worry – it will be the last area in this article even tho we could add many more.
Customer lifetime value
It is up to 70% cheaper to keep and maintain exciting customers than acquire new ones, and loyal customers are not only the best word-of-mouth branding, but they also tend to care less about the product price.
By understanding POS-data one of Borbaki’s data scientists increased the yearly revenue pr. Customer by +65% for a production company, just by analyzing their POS-data, you can read the case here.
- If you have collected POS-data, a POS-analysis can give you insight about how and where to increase your revenue or save your spendings.
- The results from the POS-analysis, can be used in more than one of your companies department
- If you aren’t analyzing your POS-data the data science way yet – it is time to start.