RSPJ.co.id – Predictive Analysis – Data is not a thing of this century: people and institutions have always shared and stored personal information.
But it was not until the digital age that this data could be analyzed and used for a wide variety of new objectives, ranging from health care for an entire population to deciding where to open a new branch of the trendy coffee shop. That is what, broadly speaking, predictive analysis works for.
In this article we will talk about how this type of analysis can benefit your company and how to use it. Let us begin.
What Is Predictive Data Analytics?
Predictive analysis is the examination of a set of data (through statistics and algorithms) to interpret them, detect patterns and obtain predictions about a process. For example, you can forecast the behavior of a customer, the sales of a business, or the trends of a market sector.
The intention of predictive analysis is to make better decisions regarding the future, that is, to propose preventive measures before a complex situation or to take better advantage of the circumstances, as the case may be.
One of its most common uses is when a bank or financial institution does a credit diagnosis of a person: the credit bureau has a rating system that gives a score to the financial history of someone who wishes to obtain a loan, or make a purchase in installments. Based on the recorded behavior, you receive a score indicating how likely it is that you will be able to pay on time.
Predictive Analysis Of Big Data Through Artificial Intelligence
By combining the power of predictive analytics and big data, organizations can gain deeper insight into trends and patterns of people’s behavior. Especially when using artificial intelligence, whose complex algorithms simulate a human neural network that allows detailed predictions about behaviors and events.
This information is substantial for the implementation of decisions or strategies that maximize results and minimize errors; although this depends on the quality of the data available. Likewise, artificial intelligence helps to automatically collect and store user data on the Internet.
Of course, for companies, predictive analysis is a very useful tool. Next, you will know why.
What Is Predictive Analytics Used For In An Organization?
Reduces the impact of risks
Thanks to predictive analysis, an organization can identify in advance the risks that threaten the industry in which it operates. In this way, it is easier to invest in actions that will help to avoid, or at least minimize, the damage in order to recover in less time.
Optimizes the operations of a company
By predicting the amount of inventory that will be needed, or identifying the needs that customers will want to meet with what the business offers, the company invests its resources more wisely. Inditex uses this method in its inventories.
Thanks to the practice of predictive analysis, Amazon managed to consolidate its recommendation system in online purchases. Its platform records searches and lists products that belong to the same category or that other customers usually buy together.
Helps create more efficient marketing strategies
In addition to anticipating a trend, predictive analysis allows you to influence customer decisions, displaying the right content at the right time. Thanks to the data available on the behavior of a sector interested in a product or service, the company realizes the information that is needed to convince it and sends it to them through the channels that it usually visits.
An example of this is what happens in some online stores such as Mercado Libre, if you did a search in their store, but did not complete the purchase, in your mailbox, in the browser or in any app that you use, ads related to your search in ecommerce.
On the other hand, when we talk about efficiency, we also refer to the importance of having full visibility of the entire customer journey in one place to optimize ROI and the marketing budget. That’s why Marketing Hub puts all the tools and data into one powerful, easy-to-use platform. You’ll save time and have the context you need to deliver a personalized experience that engages and converts customers at scale.
Indicates new territories to explore
We are referring to potential markets or segments that perhaps were not initially viable, but which, thanks to the growth of the company and its historical performance, are new opportunities to diversify and conquer bigger challenges.
Show cross and up selling opportunities
To more successfully delight customers, a brand uses this type of analytics to discover who will appreciate hearing about certain items or services that complement their experience, or offer something totally innovative.
The 6 Predictive Analytics Models
It is worth mentioning that the models represent and describe how predictive analysis is carried out, while the techniques are the set of actions to carry out this process.
1. Classification model
This model predicts class membership. Like when you want to know which of your customers are likely to abandon you for the competition. In this way, a classification can be created that helps to efficiently direct the messages that certain people need to know in order to stay loyal to the brand.
It is the simplest, and is achieved by answering questions with “yes” or “no”, or in a binary way (0 and 1). It can be applied to different businesses and is ideal for making decisions, such as granting a loan, giving a special benefit to a client to convince them to continue with the business, etc.
2. Regression model
It is the most used in predictive analysis. Predicts a value based on the relationship data variables have to each other. It is a way of understanding the importance of that segment and, therefore, when it should be invested to achieve the objectives that are convenient for the company.
3. Clustering model
This model assigns a variable into separate groups, based on similar attributes. When creating personalized marketing strategies, grouping models are very useful because they identify characteristics and behaviors that certain groups of customers or prospects share, and then recognize them as ideal for certain campaigns, messages and content.
4. Forecast model
Deploy historical data to predict value metrics, estimating the numerical value of new information based on what you already knew before. It’s what allows a call center to estimate the number of calls it will receive on a Friday afternoon, or the inventory a toy store must have for the upcoming holiday season.
5. Outlier model
It is targeted at anomalous data entries, either because they are atypical by themselves or when compared to others in the same group and different categories. They are useful models for retail and finance stores, because they detect fraud or product failures when the related information is analyzed.
For example, in the case of mobile phones with defects, an irregular increase in calls to the customer service or support area indicates that there are more users than usual who are looking for solutions. Even this type of analysis could discover in which models the fault occurs, the version of the software that registers it and in what type of actions the error occurs.
6. Time series model
This model is useful for understanding how a metric develops over time, beyond percentages. It works by taking data from one period to develop a metric that it uses to predict what will happen in the future, in the next 3-6 weeks. Generally, a year of data is needed to implement it correctly.
For example, it is used to determine the influx of guests in a hotel during certain seasons.
Predictive Analytics Techniques
- Decision Trees. Sort the data into different groups. It has the shape of a tree: each branch is a possibility of choice and the result is shown on the sheet.
- Random forest. It is a set of decision trees in which different models are applied.
- Neural networks. This artificial technology aims to imitate the reactions of a human brain to make predictions in relationships of complex variables.
- Data mining. Also known as data mining, it refers to the exploration of large databases to find patterns.
The 7 Stages Of The Predictive Analytics Process
1. Definition of the project
Here your organization must determine the specific objectives it wants to achieve and what will be the data sources that will help it achieve it. For example, if you want to improve the performance of the sales area, you must ask yourself different key questions: since when did the sales of a product decrease? Who are your main buyers? What elements presented variations in this period?
2. Data collection
It is important that you consider different sources of information to feed the process with valuable data. We refer to what can be obtained thanks to data mining, from information from the media and official industry bodies to data collected by sensors, transaction and sales systems, records on websites or customer service centers, among others. others.
3. Data management
It will be necessary to have a system that helps to “clean” the information, to get rid of what is not relevant to the particular project and that only contaminates the conclusions. Therefore, make sure you have a team that knows how the company works and what you are looking for, and that has access to tools that make this process more efficient.
4. Statistical analysis
It is the first part of the data analysis, which will yield information based on descriptive statistics and will give an estimate of certain behavior probabilities. The specialized software will be in charge of applying mainly regression techniques to find patterns and behaviors in the data.
5. Predictive modeling
This is where you decide which predictive models will come into play for your goals. Data analysis tools have specialized options such as automatic learning or machine learning, regression techniques, Bayesian analysis, decision trees, among others. Depending on the objective that you have proposed to solve, from the beginning you must choose the predictive process that best suits it.
6. Implementation of the model
Based on the results of the predictive analysis, you will be able to enumerate the series of actions that must be carried out to achieve the business objectives. By launching one or more predictive models, you will obtain new analytical results that you can apply in the areas that need it. In this way you will automate day-to-day decisions, based on what the previous step revealed.
7. Supervision of the model
Of course, you need to keep track of the model(s) to ensure there are no errors. You could even optimize it, if you consider it so; it’s a good idea to do good supervision.
Predictive Analytics Tools
1. IBM
IBM knows exactly how to do predictive analytics and has a set of tools that cover three key stages. First, it includes the understanding and analysis of data with IBM SPSS Statistics, whose function is that the user understands the information, predicts, raises hypotheses and reaches valuable conclusions.
Then, IBM SPSS Modeler puts algorithms and data models, highly appreciated by data lovers, at your fingertips. Finally, Watson Studio Desktop simplifies the process of implementing and experimenting with data, so that the business can take better advantage of artificial intelligence.
2. Alteryx
This tool helps to gather information by connecting with various data sources and also does a clean to prepare or combine what it gets. It’s a bit friendlier because it’s aimed at admins, not developers, plus it gives you the option to customize reports and cares about finding easy solutions for its customers.
On its same website, it explains that you will be able to “automate processes, insert intelligent decision-making and motivate your employees to provide better and faster business results.”
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