Predictive analytics is a powerful tool that has changed the way businesses operate. Have you ever wondered how certain companies anticipate customer needs and provide them with tailored solutions? This is where predictive analytics comes in.
By analyzing past data, predictive models help companies identify patterns and trends. This allows decision-makers to make more accurate decisions about the company’s future.
But what exactly is predictive analytics? Simply put, it is a methodology that uses statistical algorithms, machine learning techniques, and data mining. Its main purpose is to predict future outcomes based on historical data.
Predictive models takes data from various sources, such as sales, marketing, and customer behavior. It uses it to identify trends, relationships, and patterns that can help organizations make data-driven decisions.
How are predictive models different from other analytic models?
There are four different types of advanced analytics models: Descriptive, Diagnostic, Predictive, and Prescriptive.
- Descriptive models summarize the data and give insights into what has happened (The Past).
- Diagnostic models explain the reason things happened (Causes)
- Predictive models predict the future (What is likey to happen?).
- Prescriptive models suggest the best course of action. (What to do?).
In this guide we will cover the following topics:
- Why is predictive analytics so important?
- Benefits of using predictive analytics for business strategies
- Predictive Analytics Examples: How companies are using it?
- How does predictive analytics work? (Step by step)
- Understanding the different types of Predictive Analytics Models
- Understanding the Predictive Analytics Techniques
- The relationship between Big Data and Predictive Analytics
- The importance of Machine Learning in Predictive Analytics
- Deep Learning: One of the most sophisticated ways of Predictive Analytics
- Conclusions about Predictive Analytics
Why is predictive analytics so important?
Why is predictive analytics so important? Companies can make better decisions, improve customer satisfaction, and increase profits using predictive analytics.
How can a methodology accomplish all that? How does predictive analytics works? How is it possible to predict business outcomes? Two words: Mathematical Models.
Predictive analytics uses mathematical models to identify relationships between variables and predict future results. The models take into account the following:
- Historical data.
- External factors.
- Economic variables.
- Consumer behaviors.
- (Even weather patterns).
The predictions generated by these models can, for example, define products to stock or develop effective marketing campaigns.
It’s important to note that predictive analytics (like any other data analytics) is not a crystal ball. Remember: Its results are based on historical data and can only predict future outcomes based on past trends.
However, predictive analytics can still be incredibly valuable for organizations, as it can help identify potential risks and new business opportunities.
Benefits of using predictive analytics for business strategies
What are the causes of high employee turnover? Why are sales declining? Predictive analytics can help companies identify the root cause of such problems and suggest solutions.
By using predictive analytics, decision-makers can take action based on facts and not just gut instincts.
But what makes predictive analytics so effective and valuable for companies?
Identifying patterns and trends. For one, predictive analytics helps identify patterns and trends that are not easily visible in “standard” analysis. This favors better-informed decisions, resulting in improved operational efficiency, higher sales, and increased customer satisfaction.
You may also like to read: Customer Segmentation: How to Do With Predictive Location Intelligence
Reduce risks. Predictive analytics is often used to minimize risks within companies. By analyzing past data, businesses can identify potential risks and take proactive measures to mitigate them.
What is the benefit? A significant reduction of unexpected problems leads to substantial financial losses.
Saving costs. Wondering how does predictive analytics help in cost savings? Predictive analytics models can identify areas to reduce costs, streamline processes, and increase efficiency.
Competitive analysis. Predictive analytics also provides a competitive advantage. You can gain insights into customer behavior, preferences, and buying patterns using advanced statistical techniques. This lets you understand your customers better and create targeted marketing campaigns that drive sales.
Predictive Analytics Examples: How companies are using it?
Retail. Retail companies are using data to understand consumers’ purchasing and consuming habits. This brings a more personalized brand experience, increasing customer engagement.
You may also like to read: Predictive models for retail franchises
But predictive analytics isn’t just for online retailers. Banks, insurance companies, and healthcare organizations are all using it to improve their operations and better serve their customers.
Banks. For example, banks use predictive analytics to detect fraud on time. Predictive models can analyze data to identify indicators of fraudulent activity and then block transactions before they occur.
Healthcare. Predictive models also play a significant role in the healthcare industry. Hospitals use it to predict patient outcomes. Also, data help doctors improve treatments and care plans.
You may also like to read: Predictive Analytics In Healthcare: 4 Examples and Risks
Insurance. Insurance companies are using it to determine the risk of policyholders and set premiums accordingly.
You may also like to read: Big Data in Insurance Industry: How Data is Changing The Game
Transportation. Naturally, predictive analytics is about more than just looking into the future. It’s also about understanding what has happened in the past to improve the present.
You may also like to read: Supply chain solutions with predictive analytics
For example, transportation companies use predictive models to analyze their shipping and delivery operations. They can identify bottlenecks in the supply chain by analyzing past data, optimizing routes, and improving delivery times.
Real Estate companies are also using predictive modeling. Learn how: Predictive Modeling: The New Real Estate Journey
How does predictive analytics work? (Step by step)
Problem definition and project reach. The first step is to define the problem that needs to be solved and what the project will cover.
It is crucial to take this first step correctly to ensure we will work towards the right goal and get the desired value from this initiative.
Data collection. Of course, data is the foundation of predictive analytics. Analysts need to collect the necessary volumes of data from different sources. This step may require data mining or data aggregation tools to gather the data.
For this step, it is important to define the best data sources for the project.
Data cleaning and analysis. Next, we ensure data is ready for analysis. This is where statistical regression methods and machine learning techniques come into play.
Predictive model development. Now it is time to build our predictive models. Analysts will generate multiple models and select the most accurate one.
Model execution. After selecting the best model, it is time to put it to work.
Modelo monitoring. It is important to keep constant monitoring. This step ensures our model’s effectiveness.
Understanding the different types of Predictive Analytics Models
Predictive models classify into different types according to their characteristics.
These models can understand the relationships between different variables to make predictions based on those relationships. They are widely used because they can handle a large amount of data.
As the name suggests, these models classify data into different categories. They can relate data points into categories. Are you using an email spam filter? Well, that is an excellent example of a classification model in action!
Outliers are data points significantly different from the data points in the same dataset. Outlier models identify and remove these data points from the dataset to improve the accuracy of predictions.
Time series models
As the name implies, they are used to model data with a temporal aspect. For example, stock prices, weather patterns, and sales data are all examples of time series data.
Clustering models groups similar data points together. These models help identify patterns and trends in the data that are not easily visible.
Understanding the Predictive Analytics Techniques
By using predictive techniques, companies can make better decisions, understand their customers, improve efficiency, manage risk, and detect fraud. This is how a retail company can use past sales data and market trends to forecast next quarter´s sale.
There are different types of tecniques, such as linear regression, logistic regression, decision trees, and neural networks.
(This is where we get a little bit technical)
Let’s start with decision trees. Decision trees are used to make decisions based on a set of rules. They are called decision trees because of their visual resemblance to a tree (With branches leading to different choices). The “branches” represent different outcomes, and the “leaves” represent the final decision.
This type of predictive analytics is often used in finance to make decisions about investments or marketing to predict customer behavior.
Random forests are similar to decision trees but are more complex and use many “trees” instead of just one. Random forests account for many different variables and decision points, leading to more accurate predictions.
Industries such as healthcare often use random forests in their decision-making processes.
Linear regression is a statistical method that models the relationship between two variables. It is helpful to make predictions about future outcomes based on past data.
Linear regression works very well in fields such as economics to predict future economic trends.
Finally, we have neural networks. Neural networks use algorithms to model complex relationships between variables. They are modeled after the structure and function of the human brain.
Neural networks make accurate predictions in fields such as finance and healthcare. Not for nothing are they considered one of the most advanced forms of predictive analytics. That is why they also require a lot of data and computing power to work correctly.
The relationship between Big Data and Predictive Analytics
Have you ever wondered how companies like Amazon and Netflix can personalize their recommendations for you? Or how can banks predict if a loan applicant is a reasonable risk? This question can be easily solved when combining Big Data with Predictive Analytics.
How do both concepts work together? Big Data refers to the vast amounts of data being generated daily. Meanwhile predictive analytics involves using statistical algorithms and machine learning techniques to use that data and make predictions.
What is the formula to take any company to another level? Using all the enormous volumes of data collected and employing the correct methods, tools, and technology .
Want to learn more about Big Data Analytics? Here is the most complete guide
The importance of Machine Learning in Predictive Analytics
To work correctly, Predictive Analytics needs the proper Machine Learning techniques. The reason is that data scientists need to extract valuable insights from the given data.
Machine Learning is a subfield of Artificial Intelligence. It enables computer systems to learn from data, identify patterns, and make decisions without being explicitly programmed.
Note: Machine Learning algorithms improve as they receive more and better data sets. Then, predictive analytic models use these algorithms to build their predictions.
Want to learn more about Machine Learning? Read our full guide
Deep Learning: One of the most sophisticated ways of Predictive Analytics
Deep learning-based predictive analytics is a subfield of machine learning. It involves training artificial neural networks with large amounts of data to perform specific tasks.
These neural networks have the peculiarity of having the structure and function of the human brain. That is how they can recognize complex patterns and relationships in data.
How is Deep Learning being used right now?
Deep Learning is being used to predict customer behavior, stock prices, sales trends, credit card fraud, customer churn prediction, and much more. It has the quality to identify hidden patterns and relationships that traditional predictive analytics models might miss.
What is the best part about Deep Learning?
Deep learning models are self-learning and can continually improve with new data. That is why it is becoming increasingly popular in businesses and organizations.
Of course, deep Learning predictive analytics accuracy depends on the quality and quantity of data used to train the models. The more data the models have access to, the more accurate their predictions will be.
Conclusions about Predictive Analytics
Predictive analytics can be your secret weapon to stay ahead of the competition, make informed decisions, and increase your market share. By analyzing historical data and identifying patterns, predictive models can help you anticipate future outcomes and develop data-driven strategies.
But you might be thinking, “Isn’t that a lot of work and too technical for me?” Well, the truth is that predictive analytics has become more accessible and user-friendly in recent years, and its benefits can make a big difference.
Besides, it’s not just about data analysis but also about understanding what the data is telling you.
So, you may be wondering, where do I start? At PREDIK Data-Driven, we have experience in developing highly effective models. Our unique methodology allows you to take your sales, customer, and even social media data and turn them into valuable insights for your business.