How Small Businesses Can Use Predictive Modeling and Analytics to Increase Revenue

How much do you know about predictive modeling and analytics when it comes to increasing business revenue?

In the past, business owners relied on consumer buying patterns to gain insight into business strategies. It was a hit-or-miss process because it relied solely on previous sales analysis rather than changing trends.

With the technological advancements of computer learning, data mining and statistical analysis in real time, it is now possible to implement policies and procedures that meet company and consumer needs with a high probability.

While data mining collects the information, it would be impossible to know what to do with it unless you had the software in place to extract, house and analyze large amounts of data. Predictive modeling analyzes and finds the variables that increase the probability of revenue increases.

How Does Predictive Analytics Work?

Predictive analytics collects data from many different sources and determines what will likely occur next. Unlike human analysis, predictive analytic processing narrow down the data into subsets that marketers will be able to apply to company operations like productivity, geospatial sales, marketing, digital advertising or economic conditions.

When a worker uses predictive modeling andanalytics to focus on business efficiency, it will save money, increase profitsand increase output. It also allows you to become an asset because you canpredict the likelihood of critical business functions that directly increasecompany revenue and productivity.

While computer learning is a new application because of technological advancements, increase company is not so new a theory since it has been around for decades. 

What has changed is the amount of data that is analyzed as well as the number of possibilities for businesses to consider. Before the age of technology, businesses would hire an employee who was good at mathematics to scour over statistics and create formulas that led to probabilities. 

It was a lot of work that often was not so reliable as computer application software is today.

Data Mining Also a Useful Tool

Data mining was also a hidden secret used by corporations who could afford in-house servers and data scientists who could analyze large datasets.

There are several ways that companies go about data extraction and analysis to predict business models including using in-house data or third-party systems. After the data is extracted from sources like social media, websites or company databases to help businesses deliver products or services. Today, predictive analytics software allows any business, small or large, to gain insight into business functions.

It also allows companies to increase worker productivity since computer algorithms can breakdown customer behaviors to increase revenue and sales.

Analytical software isalso able to define demographics like age, buying history or gender of likelybuyers. Knowing what customer activities are occurring in real time will alsoreduce customer turnovers because of the ability to counteract unpopularbusiness practices or negative online engagement. 

It is also important to use accurate predictive modeling and analytics. If you are not extracting data from the right sources or use an algorithm you do not fully understand, there is a high probability that you will not get the results you anticipate.

There is more data online now than ever before. Because moreretailers have access to predictive modeling, there is more competition toconvert data to give businesses more awareness into future sales and revenue.

It is critical to find actionable information rather than focus on undecided data mining, which is where predictive modeling and analytic software becomes an asset.

You will be able to narrow down the data to fit your criteria, so you focus specifically on your industry, customer base or products or services. You also have the right predictive modeling strategies to pinpoint what products or services are selling now as well as forecasting the likelihood of future revenue.

Areas That Predictive Analytics Will Help Businesses

Customer Segmentation and Acquisition

Marketsegmentation is vital to revenue increase as predictive analysis reduces datainto subsets that break down demographics, customer behaviors, geographic andpsychographic segmentation. It allows businesses to market to existing and likelycustomers because of similar and dissimilar characteristics.

Productivity

The ability to extract data and quickly analyze it allows businesses to be more proactive rather than taking a traditional approach to analysis, which takes time to implement. It’s a costly measure that eats up company resources better spent in other areas.

Cost Reduction

Cost reduction is a vital factor in revenue. Predictive modeling provides a fast answer to problematic issues and reduces the hidden cost of ineffective policies and procedures.

Resource Management

Predictiveanalysis allows businesses to focus on resources that deliver revenue resultsand cuts practices that contribute to lost employee time and costmisallocation.

Quality Control

Quality control is vital in a competitive industry, which is why predictive modeling will allow you to focus on business practices like pricing, marketing or sales models.

Risk Management

Risk managementhas long been an issue because of issues like criminal activities,cybersecurity, fraud or loss prevention. Analyzing data and detecting ways toovercome the challenges will reduce company vulnerabilities.

Enhance Marketing and Advertising

As predictivemodeling identifies customer behaviors, you can enhance marketing and advertising that retainscustomers as well as attracts new ones.

Overstocking inventory is wasteful. Businesses either make the mistake of housing too many products or lack the products that do sell. Predictive analytics allows you to analyze data to identify products in high demand as well as reduce stock that eats profit.

Consumer Engagement and Personalization

Do you know yourbusiness’s customer acquisition rate? It is anever-increasing issue that is causing businesses to take a closer look at howbest to capitalize on transitioning one-time shoppers into return customers.Using predictive modeling allows you to take in shopping behaviors, searchinquiries and website favorites to determine future sales.

There is no longer the need to anticipate your customer needs as you now have predictive modeling to optimize data innovatively and offer the best strategies based on real-time analysis.

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