With rapid market changes to the retail industry and a sudden need to serve customers through a variety of different channels, staying ahead of the curve with predictive analytics software can help increase sales, reduce loss, and boost loyalty. Now, you can receive smart recommendations for your business based on predicted future outcomes. By allowing you to preemptively act, rather than react, you can stay ahead of the trends around you. While standard analytics and reporting have been a mainstay of businesses for some time, AI (artificial intelligence) and machine learning algorithms are taking prescriptive analytics to the next level.

 Advantages of predictive analysis and machine learning

Before you get started on adapting your data analysis model, first you must understand the difference between the two types of predictive modeling.

Predictive Analytics: The use of data analytics to predict the future by understanding the past. This model is made possible with AI by using data science in analyzing historical data from neural networks to create future models.

Machine Learning: The computer has ability to use data mining to predict patterns without being programmed that way. It can create future models as well as implement those models.

The difference between using the two is the difference between where you are in the process. The Predictive analytics model is the process you use the data analytics to create a business model, and after that, you will use the Machine Learning automate your business trends and drive towards your business goals.

 How predictive analytics is changing the future of the retail industry

As your customers need to shop online increases every year, and rates skyrocketed in the wake of Covid-19, the need for AI to predict the market grows. Only retailers that use all neural networks available and have the digital capabilities to predict demand forecasting will survive the coming years. If you can use the data you have to master the business analytics model, you can predict what your customers will buy next. Or you can predict what they are not buying next and make adjustments as needed.

AI will use data mining along with predictive modeling to fight growing problems such as fraud prevention by being able to indicate when something is not in the usual behavior of the user. As well as loss prevention by keeping the shelves stocked with the appropriate amount of inventory and keep supply chain from expiring or going out of trend before they are sold. Right now, most customers want a multichannel business model, so they can shop their favorite brands from in-person shopping to online shopping without any disruption. By keeping up with these trends in the retail industry, you can use prescriptive analytics to better understand your customers, and enhancing their customer experience by anticipating their needs.

While we are still in the early stages of retail AI, according to Kevin Sterneckert, CMO of Symphony Retail AI, within the next 5 years we will see a complete transition over to AI. The data analytics market is expected to grow by $6.8 million in the years between 2020 and 2025. Like the great gold rush, there is tons of historical data out there, all you have to do is scoop it up. Then, you just have to figure out what to do with it once you've got it.

 What you need to get started using predictive analytics

The idea behind using AI programs to predict the market means using prescriptive analytics to free up you and your team to focus on the roles they were hired for. If you have a POS system that can automatically do the work for you, then you are already a step ahead. Do your research, are you ready for a business analytics model? Or do you have some groundwork you will need to lay first?

NCR just announced its first predictive services platform in a groundbreaking move to stay ahead of retail AI needs. The NCR Predictive Services platform works by using neural networks from all NCR devices, gathering big data analytics, and creating a proactive approach to the business model instead of a reactionary one. It can predict failures on these terminals before they happen and dispatch technicians before consumers are impacted. The NCR Predictive Services platform can also share secure, audit-controlled access with engineers to run faster diagnostic issues.

If your customer has less time with the system down, they are more likely to walk away with a positive customer experience. Or predictive modeling action recommendations like preemptive alerts when the receipt printer is about to run out of paper so there is no time wasted changing it out with a line of people waiting. Or cashier analysis, which uses employee trust scores to predict human error on the machines and strengthen loss prevention. Whatever your concerns, you can personalize your systems with all of the new updates that NCR has added.

Here are some features you might want to look into:

• Advanced Reporting
• "Less than Whole" Items
• Check and Guest Counts
• Retail Labor Management
• Schedule vs. Actual Detailed Reports
• Work Schedule Enhancements
• Inventory Control
• Copy Functionality

Whichever POS system you choose, it is important to look at the traditional retail services with a new point of view. If they are more focused on how they fix problems "after-the-fact" as opposed to being proactive, then they probably aren't the right choice for you. In this new age of AI, remember the 6-million-dollar man, "we have the technology." There are now plenty of predictive analytics tools out there to predict the technical problems and market trends that plague most of the traditional POS systems.

 Implementing a predictive analytics program

Now that you have done all of the leg work, it's time to finally find out what all of this big data actually means for you. Here is your step-by-step guide:

1. Clean the Data: Before you start creating your program, it is important to go through the big data you've collected and eliminate the outliers. These can throw off your algorithms and corrupt your analysis from the start.

2. Set your Intentions: What is it you hope to accomplish from this research? Are you trying to boost sales? Are you trying to figure out why a particular location is failing? Or maybe you just want to get ahead of the curve before the great AI boom. Whatever your intentions, it's good to focus on what you're actually trying to quantify and set your baseline from there.

3. Define the Predictive Model: Now that you have squeaky clean data, you need to define the predictive algorithms that will be used to predict future behaviors. You want to avoid "over-fitting or under-fitting" your business analytics model -don't become so laser-focused on the future, that you forget to take into consideration historical data trends. At the same time, don't assume that past retail analytics trends are always predictors of future ones. Remember that correlation doesn't always equal causality.

4. Test your Predictive Model: Once you have set your model, you need to make sure it works. Use data from sales that have already occurred to test your new machine learning algorithm, did it work? This is such a crucial step in the process, as crucial as a chef tasting the food before sending it out. You need to feel confident in knowing that all of the future data you predict will be correct.

5. Roll out your Predictive Analytics Techniques: Now you're an expert! Get ready to roll out your new predictive model and let it do the work for you. Your predictive analysis POS program can take care of it from here!

With increased competition in the retail market and customer expectations for a seamless, multichannel experience, predictive analytics software combines AI and machine learning algorithms to provide information such as employee trust scores, future sales forecasts and preemptive action recommendations. Advanced analytics and data mining are no longer nice-to-haves, but must-haves: investing in predictive analytics software today gets your retail business ready for a successful tomorrow.