For companies looking to gain competitive advantage, using Predictive Analysis can be a great option. This will allow them to expand analytical capabilities, always looking to the future, taking advantage of data from the past.
Your company can use this type of analysis to identify the opportunities of the company that have not yet been exploited and thus discover the hidden risks that lie within large amounts of data.
Predictive analytics provides valuable information for decision making. This enables you to take advantage of future trends and, at the same time, respond to challenges before they occur. Predictive analytics has drawn a lot of attention in recent years, thanks to the technological advances that support it, mainly in the areas of Big Data and Machine Learning.
What is Predictive Data Analytics?
When we talk about predictive data analytics, we are referring to the set of strategies that are applied in a business-related problem. Today, many companies use predictive analytics, and others are thinking of using it. This is due to the great advantages it offers in strengthening business decision making.
In a nutshell, predictive analysis allows knowing the behavior of consumers regarding the object of studyin advance. However, it not only seeks to predict, but also to publicize how it can be influenced through actions and what probabilities are decisive to solve a pre-established event.
To build data predictions in a company requires a very high level of experience and the incorporation of statistical methods. It is important that this analysis is carried out by experts and data analysis engineers, who help collect and organize the most important data.
When the data collection process has been completed, the statistical model is determined or formulated, which is constantly modified to generate the most accurate results. That process is sometimes not linear, and correlations can occur where scientists don't seek it.
It is important that scientists specialized in physics, computer science, mathematics or statistics are hired. These professionals have a more open mind regarding data exploration, which favors predictive analysis, making it more effective.
Advantages of predictive analytics
Among the main advantages of predictive data analytics, the following stand out:
• Prevents rotation and detects signs of dissatisfaction
Through the creation of customer segments regarding loss risks, both major and minor, you will be able to apply corrective actions in a timely manner, which could generate a considerable increase in your earnings.
• Expand the quality of your customer's life cycle
Using predictive analytics, you have the possibility to identify those customers of great value to your brand. This will help you plan the most appropriate and timely marketing strategies.
• Identify the high potential customer
If you know your clients, you will be able to identify those with the capacity to increase their purchases. This will allow you to contact them and offer them timely actions that will help you increase your income considerably.
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• Allows campaign planning
If you analyze all the data you have, such as behavior, purchasing patterns, web browsing, interactions through social networks, among others, you can define the best times and channels to communicate with your customers.
• Analyze campaign performance
Through this procedure you will be able to identify the behavior of the clients and their purchasing habits. This way, you will know if your marketing strategies are giving the correct results.
• Identify the probability of purchase
You can create customer segments based on purchase probabilities, so that you can then communicate with them in a different and completely efficient way.Predictive analytics suggests that we act quickly, through predictions and their implications. This is why this science is so important to marketing.
It is useless to know what can happen or what interests the company if the necessary actions are not carried out constantly so that this behavior changes and better results are generated at the business level.
Predictive analysis in R Language
Today there is a wide range of tools to predict analysis and models. There are also a host of techniques that enable predictive analytics. In addition, other technologies that support deep learning based applications can be included.
The R language, like Python and Scala, is frequently used for predictive analysis. It is widely used by statisticians and data miners to develop statistical software and analyze data. It can be run through platforms like UNIX, Windows and MacOS.
This language offers a range of statistical functionalities such as linear and non-linear modeling, classification and grouping. Also the possibility of manipulating data, performing calculations and representing them through graphs.
Predictive analytics Big Data
Big Data is synonymous with action and opportunity. However, to take advantage of that potential you must use the right tool and predictive analytics is ideal.While companies have the possibility of storing large amounts of data, the greater the opportunity to exploit them to obtain predictive information.
This type of analysis has also been of great help in the commercialization of learning tools created by information technology providers, financial services, insurance companies, among others.
When it comes to predictive analytics, in the context of Big Data, for example, you might be developing data from a company's systems that could include transaction data, customer complaints, sales results, and marketing information.Every day, more and more, businesses make decisions based on the data that comes from this valuable mine of information.