How predictive analytics software can help forecast sales

One of the buzziest words in business today is “prediction”. Technological advancement has driven the corporate sector so far ahead that a once-futuristic theory has now become a tangible reality. Predictive analytics software solutions developed to solve a variety of different challenges are strategically popping up on the market. This innovative and cutting-edge technology has even made its way to the holy grail of business operations that are commonly referred to as “sales”. Because, in this highly competitive business context, relying on intuition is no longer enough to stay ahead. Professionals developing expansion plans or product mix strategies having been pining for a technological solution that enables them to forecast sales. And as a result of significant leaps in Artificial Intelligence techniques, predicting potential revenue is now possible. Nevertheless, financial forecasting models must be grounded in historical data to provide relevant predictions that are tailored to a specific business.

So, how can predictive analytics software help with sales forecasting for expansion and product mixing? We will go over the steps behind the methodology that has turned this trending topic into usable technology.

1. Understand the market conditions that affect individual points of sale

In order for any predictive analytics software to perform sales forecasting, it is important to take into account the market dynamics that surround a point of sale. Is there one particular area that generates more revenue than another? Maybe there are some unique circumstances responsible for a store’s success in that particular location? The same can be said for the opposite scenario. Imagine a certain store or point of sale is performing poorly and you would like to know why. You will use this information to either improve operations or decide to close and relocate. Location impacts each point of sale differently making it important to determine the driving factors that influence their outcomes. All of this knowledge must then be incorporated into the sales forecasting statistical model.

2. Find new locations that mimic the market contexts of your best-performers

Once you know which market contexts generate profitable outcomes for your top points of sale, the next step involves identifying new territories that maintain those same characteristics. This is what is commonly called “replicating success criteria”. If you know which levers are contributing to your store or product’s expediency, why not expand or place products in those areas where the market conditions are conducive to the best possible outcomes?

The blue dot represents a new potential location that shares common success criteria with the first location

3. Employ predictive models that are based on realistic data

The final step to building any sales forecasting module involves the gathering of historical sales data that is particular to a single business. Once collected, the company’s internal sales data is combined with other data sources and enriched with Artificial Intelligence techniques to provide contextually relevant results. This gives the concept of “crunching the numbers” an entirely new meaning. Predictive analytics software that performs sales forecasting is not about making magical prophecies, it’s rather about building on previous data to draw logical conclusions and make accurate predictions about the future. Solid and objective data should always be the basis for every business decision as it is the foundation for every predictive analytics model. This same idea was succinctly expressed back in the 6th Century BC by the Chinese poet, Lao Tzu by saying:

“Those who have knowledge, don’t predict. Those who predict, don’t have knowledge”.

Sales forecasting model based on the specific market conditions of a potential location and built from historical data from a top-performing POS

Predictive analytics software made simple with Location Intelligence

While predictive analytics solutions are continuously evolving, there are still some key challenges many players in this market must meet. Most of these are in regards to the software’s overall usability which oftentimes inhibits value extraction. As these advanced statistical models are highly complex in nature, the design and navigation of the final product can easily become muddled.

This is not the case with Location Intelligence when it comes to forecasting sales. Apart from its ability to provide contextual revenue projections for a particular location, the differentiating factor behind this technology is the user experience. Easy navigation is what makes Location Intelligence transversal for many different industries and teams—sales, trade marketing, operations, brand marketing, etc.—and provides these professionals with immediate insights. Using the right data and technology to forecast sales and fuel an expansion plan or product mix strategy is already complicated enough. Which is why the complexity behind the scenes should always remain invisible, ensuring the experience with sales forecasting is as revolutionary as the technology itself.