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.