Point of Sale Data Analysis: 3 Best Practices for Retail Businesses to Succeed
In this digital era, the retail industry has been evolving rapidly. The in-store experience has gone far beyond conducting a simple transaction at the point of sale (POS). The functionality of point of sale has enhanced recently and more developments are on the horizon. Today’s digitally empowered customers are expecting better service and experience in [...]READ MORE >>
In this digital era, the retail industry has been evolving rapidly. The in-store experience has gone far beyond conducting a simple transaction at the point of sale (POS). The functionality of point of sale has enhanced recently and more developments are on the horizon. Today’s digitally empowered customers are expecting better service and experience in real-time. Real-time engagement technologies, such as the Internet, kiosks, digital signage, even mobile apps, are revamping retail and putting consumers in control of the shopping experience. In response, companies that want to conduct business in this next generation of retail are learning to position their point of sale systems at the center of this Internet-oriented shopping experience. This is the reason why point of sale data analysis has become very important for retail companies. With the help of point of sale data analysis, retailers can deliver good customer experience at store levels. In this article, we have curated a list of a few best practices that can help retailers to establish a clear foundation for their point of sale data analysis strategy and remain flexible enough to adopt new solutions as they emerge.
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Point of Sale Data Analysis Best Practices
Best Practice #1: Analyze the trend data first
To improve their point of sale data analysis strategy, retailers need to monitor the market trend data timely through effective corporate reports and scorecards. They need to look at both shorter- and longer-term market trends to obtain desired business outcomes. Shorter-term trends can proactively help in fixing issues before they extend into longer-term trends.
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Best Practice #3: Conduct distribution analysis
Tracking new items can be done easily and accurately by creating a report that tracks the percentage of stores selling an item. Here, the distribution would only be recognized once the first sale is recorded. Distribution analysis helps in measuring the percentage of stores that carry the particular item. Also, this analysis can help in understanding how quickly the product is getting through the supply chain – from the warehouse to the store’s receiving, then out from the back room and onto the shelf. As a result, retail companies can make changes to their point of sale data analysis strategy and maximize their profit.
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Best Practice #2: Determine stocking schedules at store level
For retail companies measuring out of stock (OOS) lost sales are probably one of the most challenging tasks. But to improve point of sale data analysis, they need to monitor hourly sales data with significant gaps as this could be a strong indicator of OOS issues. If you are only looking at average units per day, you would not see a difference between weekday and weekend sales. Therefore, it is important to look at sales per hour as it helps to identify an OOS issue during what should be a peak time for sales.
By leveraging our point of sale data analysis solutions, you can gain a holistic view of POS transactions and ROS while continuously monitoring end-to-end transactions from payment source to payment completion. Request a FREE demo to know more.