The concept of 'forecasting' is not new. Business analysts have been making forecasts for decades, but with the rise of predictive data analytics, forecasting is becoming much more sophisticated and powerful.
In simple terms, ‘forecasting’ is a forward-looking analysis based on a single independent variable: time. In business terms, predictive analytics implies some sort of regression analysis—the relationship between one dependent variable and a series of other changing variables. Predictive analytics promises to tell us something that we don’t know about the future in much more detail than ever before.
Forecasting then, can be defined as a process of estimating the future based on past and present data. Leveraging past and current data, statistical modelling and other mathematical processes can reliably detect future trends and behaviours, find and exploit patterns contained within that data, and detect risks and opportunities as well.
“We can forecast sales for the upcoming week by using past sales data. Forecasting can also take into account other variable factors, such as the number of active sales staff, promotional campaigns, availability and pricing of competitor or substitute products.” Says Friedemann Ang, one of DataVLT’s resident data scientists. “Advanced predictive analytics is crucial in such cases, as human efforts alone are insufficient and impossible to quantify these factors over an extended time period.”
Predictive data analytics allows retailers to look at historical data to make things more relevant to their customers. Retailers often use predictive analytics to forecast inventory and predicting logistics requirements so that they can provide the best product at the most optimal time, based on customer needs.
Data-enabled heat maps can help retailers to identify foot traffic in a store environment. This can help to determine store layouts that eventually maximize sales. This is a combined use of historical data as well as psychological datasets that help optimise customer browsing routes to best elicit a purchasing decision.
In recent years, the technology around product recommendation engines has improved significantly. Recommendation engines are sophisticated algorithms that take into account past data that include such things as demographics, preferences, needs, and previous shopping patterns, to predict products that may interest a customer.
Online travel agents, such as Agoda and booking.com, rely almost solely on predictive analytics to optimise guestroom prices. This technique uses technology to forecast the number of guests on any given future date, in an effort to maximize occupancy and revenues throughout the year. Commonly, they will look at past reservation trends and broader country-inbound statistics.
Forecasting and predictive analytics has real world business applications. Increasingly, businesses of all sizes, from almost every industry, have taken to predictive analytics to optimise marketing campaigns.
Implementing predictive analytics and forecasting into your business isn't exactly a walk in the park. For those committed to a data driven culture, however, it is a good idea to select a specific part of your business to trial an implementation. Sophisticated, customised predictive analytics platforms are now becoming increasingly available to businesses of any size, allowing for forecasts with a significant degree of precision. To create a forecasting or predictive analytics platform, only two core components are required: Data from within a vertical, and data analytics tools.
In an ever-changing world where trends influence the masses in waves and disappear within days, companies are starting to realise the importance of being flexible to remain relevant. Advanced predictive tools help proactive firms save time and plan for changes without undergoing upheavals in operations, campaigns and goals.
Firms that choose to invest in themselves will eventually take the forefront of their industries in the long-term.
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