Data Mining and Forecasting With Time
The recent years of tech development have shown remarkable improvements in computer processing and information gathering. The impact of artificial intelligence is seen in every industry around the globe with impressive speed and accuracy in data analysis that man can’t replicate at the same pace or in the same quantity. Business operators used to wade through reports and statistical analysis for the best insight on how to expand the company, adjust labor costs or alter the product to meet consumer trends. Now, big data presents this information in real-time with the push of a button. One area of exploration in data analysis deals with time.
Analyzing the Past
A company looking to be successful in the future can’t forge ahead without looking at the past. By going back to data points along with the operations at prior time intervals, a company can more successful attempt to predict what might occur in the future. This statistical process of understanding the past in order to predict the company’s future is called time series analysis. Essentially, the process defines a quantity that is then measured and evaluated sequentially throughout an interval of time. Companies all across the world use this predictive method of analysis, although those in utilities (such as power plants or boards) rely on this forecasting to recognize upcoming needs. Electricity use goes through seasonal patterns in keeping with seasonal cycles during the year. The temperature differences between summer and winter, peak usage times during days of the week, extended daylight hours and community or industrial growth need to be projected in order to provide consistent services. With a time analysis, the results can influence both short- or long-term forecasting needs.
Using Data Mining
Series analysis over time can combine data mining to extract more specific information and offing better predictive models. Seasons patterns like the ones needed in utilities are an outlet of data mining combined with forecasting. There also different methods of data mining, such as clustering or variable selection, which alters the construction of the data into fewer, more manageable dimensions. It is also possible to use data mining to conduct similarity analyses and look for detectable patterns in different areas of data. This information can then be regrouped to advise a company on new product forecasting or consumer response. There are several key areas of operating that can use data mining and forecasting analysis to improve efficiency and accuracy.
Marketing. The positive reaction of a customer over a marketing campaign or specifically offer is more likely to be repeated and influence customer loyalty. Using analysis on the recency of contact data, marketing departments are able to develop a more specific customer relationship management strategy. The advanced and complex predictive model relies on proxy variables to assess the potential relationship between the desired future outcome of consumer choices when viewed according to their historical behaviors. Mobile phone carriers rely on a breakdown of monthly air minutes used, time spent on the network, the type of activity on the network or total number or airtime through text or phone calls used in order to assess what package offers should be issued. Software tools have made the data prep in these areas more accurate and efficient.
Inventory Management. Each time a product is purchased, the SKU code is logged into the system with a date and time stamp. This information can go back for years, and companies may have thousands of products in their inventory list. This is an overwhelming amount of data to comb through in order to make accurate inventory purchases. With a data analysis that can break down items by peak times, seasonal effects, sale price or any other information associated with the purchase, stores are able to more accurately manage their inventory and plan for an influx in purchases or assess their listed prices for items that are performing unfavorably.
This technique of using data that is tracked through the past in over to forecast the future has changed the way businesses operate and adapt to the fluctuations in consumer behaviors. The risk that businesses have taken with investing in tech trends has paid off, to the benefit of both consumer and company.
