What is predictive analytics?
Predictive analytics is a type of statistical analysis that uses data that's already been collected to make predictions about the future. It uses some simple techniques, like data modeling, and some complex strategies, like data mining and machine learning, to make its educated guesses. The use of good predictive analytics techniques can benefit any organization or enterprise in any industry.
Predictive analytics is one of the four major types of data analysis. Other types of data analysis include diagnostic analytics, which uses historical data trends to figure out the causes of past events, and descriptive analytics, which uses the data that's currently being collected to isolate trends happening in real time.
Businesses use diagnostic, descriptive, and predictive analytics together for prescriptive analytics. Prescriptive analytics uses the other types of data analysis to figure out what actions, if any, a business should take when a problem arises. All types of data analysis are essential for a good business intelligence tool, but predictive analytics are especially useful.
A forecasting tool in Domo. Forecasting is one of the most common applications for predictive analytics.
Businesses use predictive analytics to answer business questions, drive insight, and solve problems before they even occur. Most business intelligence tools have some amount of predictive analytics tools, but not every BI software has every predictive analytics tool. If you're looking for a specific predictive analytics function, make sure the BI tool you're looking at has that function.
What are some predictive analysis techniques?
There's no limit to the potential use cases of predictive analytics, and so there's many different techniques for performing predictive analysis. Often, BI companies may use their own brand names or other jargon to refer to their predictive techniques. Broadly, though, predictive analytics techniques fall into four main categories. The collective term for these predictive analysis techniques is 'predictive modeling'.
Time-series models are some of the most common use cases for predictive analytics. These models predict how a certain variable will change in the future using historical and current data. Often, time-series models for a given variable are called forecasts.
Businesses use forecasts to plan ahead and make crucial business decisions. They may make seasonality forecasts, to see how data changes at certain times of the year, or do trend analysis, to see how trends go up or down over time.
A business might want to know what products to order month-to-month. They can build a forecast using historical data trends; based on the data from previous years, BI tools can make an educated guess at what the data for upcoming years will look like. Using predictive analytics, they can, for example, forecast that sales of sunscreen will peak in July, and that sales of pumpkins will peak in October. Through time-series models, businesses can ensure that they're selling the right products at the right times.
Regression models estimate the relationship between two sets of data. They aim to explain how different variables are connected to each other, and can inform a business on how potential changes to one variable could affect seemingly unrelated things.
In practice, the most common use for regression models is 'what if' analysis. If a business wants to make a change to some aspect of a product, they can run regression models to see how that change will affect other aspects of a business. They show a business what might happen if they made a given change.
A regression model in Tableau.
For instance, a clothing manufacturer might want to start making a new variation of a shirt they already sell. Using regression models, they can use historical data to determine how things like color, fit, size, and shelf placement affect how likely a customer is to buy a shirt. Through this analysis, they can design their shirts to have the highest possible chance of a sale.
Classification models use historical trends to fit data into certain categories. Based on an initial data set, this analysis can sort data into different categories based on what data in that category is supposed to look like.
One major use case for classification models is text analysis. Using this analysis, businesses can classify text to determine information about it algorithmically, without needing to put human eyes on it. A business may use models like these to separate valuable leads from ones that are unlikely to convert, based on what valuable leads have looked like in the past.
Recently, many BI tools have started to advertise the benefits of machine learning. Broadly speaking, machine learning is a term used to describe computer algorithms that can learn automatically, based on previous input. Machine learning algorithms use their knowledge of how they solved problems earlier to solve new problems.
Machine learning algorithms often use neural networks to analyze their data. Neural networks are algorithms that are intended to process information in the same way that a human brain does. They can often see trends that a simple machine algorithm might miss.
Machine learning is an important part of data mining. Data mining is the process of doing complex, computer-aided analysis on a data set to find insight that would be invisible to a human observer. Many BI tools pride themselves on the strength of their data mining tools.
Why is predictive analytics useful?
Predictive analytics is an essential tool for any business looking to stay agile and compete effectively in the market.
Predictive analysis improves decision-making. Using predictive analytics, business leaders can make informed decisions, supported by data, about the future. There's no need to rely on gut feelings or wild guessing. These tools help drive valuable insight that then turns into real profit.
Predictive analysis drives revenue. Predictive analysis is an essential part of today's supply chains. With predictive models, businesses can figure out exactly how much product to keep in stock, so there's no wastage. Many other systems and processes in a business can profit from better forecasting.
Predictive analysis reduces risk. With predictive models, businesses can see what the effects of a given change would be before they make that change. These models give businesses more information about what to expect in the future and allows them to prepare better.
Predictive analytics - a vision of the future
Businesses can leverage the power of predictive analytics to make educated, data-informed plans about their future. A good predictive analytics solution offers a range of tools that can help improve every facet of a business, from forecasting tools to help improve revenue to text analysis tools to help improve customer relations.
To stay competitive in today's markets, businesses will need to make full use of their entire predictive analytics suite. Any and every business can profit from improved predictive analytics, regardless of their size or situation. No business is too small or too complicated for a predictive analytics tool.
To find out which predictive analytics tool is a best fit for your use case, reach out to our team of experts today. They'll work with you to find software that meets all your needs in a no-cost, no-obligations, no-pressure consultation.