Data is not the thing of this century: people and institutions have always shared and stored personal data. But it was only in the digital age that this data could be analyzed and used for various new purposes, from providing health care to the entire population to deciding where to open a new branch of a modern coffee shop. That’s what predictive analytics is mostly for.
In this article, we’ll talk about how this type of analysis can benefit your business and how you can use it. Let’s begin.
What is predictive data analysis?
Predictive analytics is the study of a set of data (via statistics and algorithms) in order to interpret it, identify patterns, and make predictions about a process. For example, you can predict user behavior, company revenue, or market sector trends.
The purpose of predictive analysis is to make better decisions for the future, that is, to suggest preventive actions before a complex situation or to make better use of circumstances where appropriate.
One of the most common uses is when a bank or financial institution performs a credit diagnosis on an individual: the credit bureau has a scoring system that evaluates the financial history of someone applying for credit or making a purchase. Based on your recorded behavior, you get a score that shows how likely you are to pay on time.
Predictive analysis of big data using artificial intelligence
By combining the power of predictive analytics and big data, organizations can gain deeper insight into trends and patterns in human behavior. Especially when artificial intelligence is used, whose complex algorithms simulate a human neural network that enables detailed predictions of behavior and events.
This information is essential for implementing decisions or strategies that maximize results and minimize errors; However, this depends on the quality of the available data. Artificial intelligence also helps in automatically collecting and storing user data online.
For companies, predictive analytics is of course a very useful tool. Then you will know why.
What is the purpose of predictive analytics in the company?
It reduces the impact of risk
Thanks to predictive analytics, the company can foresee the risks that threaten the industry in which it operates. In this way, it is easier to invest in measures that will prevent or at least minimize the damage in order to repair it in a shorter time.
It optimizes company processes
By predicting the amount of inventory needed or by identifying the needs that customers want to satisfy with the company’s offering, the company invests its resources wisely. Inditex uses it method in their funds.
Thanks to predictive analytics, Amazon has been able to consolidate its online shopping recommendation system. Its platform records searches and lists products that belong to the same category or that other customers usually buy together.
It helps create more effective marketing strategies
In addition to trend forecasting, you can use predictive analytics to influence customer decisions by showing the right content at the right time. Thanks to the available data on the behavior of the sector interested in the product or service, the company recognizes the information needed for persuasion and sends it to the channels it usually visits.
An example of this is what happens in some online stores like Mercado Libre, if you searched their store but did not complete the purchase, in your mailbox, browser or application you use, ads related to your search in the e-store.
On the other hand, when we talk about efficiency, we also mean it the importance of complete visibility of the entire customer journey in one place to optimize ROI and marketing budget. Because, Marketing Hub brings together all your 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 attracts and converts customers in droves.
It marks new areas for research
We are talking about potential markets or segments that may not have been viable in the beginning, but which, thanks to the company’s growth and historical results, offer new opportunities to diversify and face greater challenges.
Showcase cross-sell and up-sell opportunities
To engage customers more successfully, a brand uses this type of analytics to discover who would like to hear about certain items or services that complement their experience or offer something completely innovative.
6 models of predictive analysis
- classification model
- regression model
- association model
- prediction model
- extraordinary model
- time series model
It is worth noting that models represent and describe how predictive analysis is performed, while techniques are a set of actions to perform this process.
1. Classification model
This model predicts class membership. For example, if you want to know which customers will leave you because of the competition. In this way, a classification can be created that helps to effectively target the messages that certain people need to know in order to remain loyal to the brand.
It is the simplest and is achieved by answering questions with «yes» or «no» or binary (0 and 1). It can be applied to different companies and is ideal for making decisions such as: B. Approving a loan, giving a special benefit to a customer to convince him to continue doing business, etc.
2. Regression model
It is most often used in predictive analytics. It predicts a value based on the relationship between data variables. This is a way to understand the importance of this segment and therefore when to invest in order to achieve the goals appropriate for the company.
3. Clustering model
This model assigns a variable to separate groups based on similar attributes. Clustering models are very useful in creating personalized marketing strategies, as they identify traits and behaviors shared by specific groups of customers or prospects, and then identify them as ideal for specific campaigns, messages and content.
4. Forecast model
Use historical data to predict value metrics and estimate the numerical value of new information based on what you already knew. This allows the call center to estimate the number of calls it will receive on a Friday afternoon or the inventory a toy store should have for the upcoming holiday season.
5. Outlier model
It targets abnormal data entries, either because they are atypical in themselves or compared to others in the same group and other categories. They are useful models for retail and financial companies because they detect fraud or product defects when related information is analyzed.
For example, when a mobile phone is broken, a fluctuating number of calls to customer service or support indicates that more users than usual are looking for solutions. Again, this type of analysis could determine which models are experiencing the error, which version of the software is registering it, and what types of actions are experiencing the error.
6. time series model
This model is useful for understanding how metrics evolve over time beyond percentages. It works by taking data over a period of time to develop metrics that it uses to predict what will happen over the next 3-6 weeks in the future. Generally, it takes a year of data to implement properly.
For example, it is used to determine the number of guests in a hotel at a certain time of the year.
Predictive analytics techniques
- decision trees. Sort the data into different groups. It is in the form of a tree: each branch is a choice, and the result is shown on a sheet.
- random forest. It is a set of decision trees in which different models are applied.
- Neural networks. This artificial technology aims to mimic the reactions of the human brain in order to make predictions in the relationships of complex variables.
- data mining. Also known as data mining, it refers to examining large databases to find patterns.
7 stages of the predictive analytics process
1. Project definition
This is where your organization needs to determine the specific goals it wants to achieve and which ones data sources will help it achieve these goals. For example, if you want to improve the performance of a sales area, you need to ask yourself a few key questions: How long have product sales been falling? Who are your main customers? What elements showed variations in this period?
2. Data collection
It is important to consider different sources of information in order to supply the process with valuable data. We refer to what can be obtained thanks to data mining, from information obtained from the media and official industry organizations to data collected from sensors, transaction and sales systems, records on websites or customer service centers, among others. the rest.
3. Data management
A system will be needed to help «clean» the information to remove what is not relevant to the project at hand and only pollutes the conclusions. So make sure you have a team that knows how the business works and what you’re looking for, and has access to the tools to make this process more efficient.
4. Statistical analysis
It is the first part of data analysis that provides information based on descriptive statistics and provides an estimate of certain probabilities of behavior. Specialized software will mainly use regression techniques to find patterns and behavior in the data.
5. Predictive modeling
This is where you decide which forecasting models 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, and others. Depending on the goal you propose to solve, you should choose the most suitable forecasting procedure from the very beginning.
6. Implementation of the model
Based on the results of predictive analysis, you can enumerate a set of actions that need to be performed to achieve business goals. By introducing one or more predictive models, you get new analysis results that you can apply to the areas where they are needed. That way, you automate day-to-day decisions based on what the previous step discovered.
7. Supervision of the model
Of course, you should keep an eye on the model(s) to make sure there are no errors. You can even customize it if you see fit; It’s a good idea to keep track.
3 examples of predictive analytics
The possibilities of data are endless: you can locate and segment customers in marketing; check whether the person has good life insurance prospects; monitoring of aircraft condition and maintenance; Inventory delivery management, among other things. Then we’ll show you simple examples of how big brands are using it.
1. Google Maps
A very common case is when you are trying to find an address on maps that will show you the way there. The platform has several filters and options: by car, public transport, walking, cycling, departure time, day of departure, fewer transfers, etc. With these settings and predictive clustering analysis that relies on data such as traffic and weather, it attracts the best route for you .
2. BBVA Bankomer
It is also very effective to perform predictive data analysis in banking systems. For example, Bancomer uses an outlier value model to detect unusual user actions that may be a sign of fraud, theft or card cloning. When it detects a change in a user’s device or location, it immediately sends them an alert to take action.
3. youtube
With the euphoria of short videos, YouTube has created its short section, driven by an algorithm that works according to predictive regression analysis. This provides users with content related to their viewing and search history in their navigation bar.
As we mentioned earlier, it’s important to have a tool that does the heavy lifting with predictive analytics. Fortunately, there are several, so we’ll mention a few so you can get to know them and decide which one is the best fit for your organization.
6 tools for predictive analytics
one. IBM
IBM understands exactly how predictive analytics is done and has a set of tools that cover the three key stages. First, it involves understanding and analyzing data using IBM SPSS Statistics, the function of which is to enable the user to understand information, make predictions, formulate hypotheses and draw valuable conclusions.
Then IBM SPSS Modeler provides you with the algorithms and data models that data enthusiasts really appreciate. Ultimately, Watson Studio Desktop simplifies the process of deploying and experimenting with data, enabling an organization to better leverage artificial intelligence.
2. alteryx
This tool helps gather information by connecting to different data sources and also performs cleaning to prepare or combine what it receives. It’s a bit friendlier as it’s aimed at administrators rather than developers, plus it gives you the ability to customize reports and takes care of finding easy solutions for your users.
The same website explains that you’ll be able to «automate processes, embed smart decision-making and motivate your people to deliver better, faster business results.»
3. DataBricks
This option is ideal for companies that manage a large amount of data. DataBricks has a number of open source tools, as well as others like collaborative laptops and data channels to ensure seamless work between multiple teams.
four. DataRobot
For those managing their data and predictive models on-premise, in the cloud or both, DataRobot offers solutions targeting verticals such as marketing, insurance, retail and communications. It has tools for different predictive analytics models for better management.
5. MathWorks
Since its inception, MathWorks has developed solutions for data analysis and statistics. MATLAB is an information management platform with the ability to create your own algorithms and run models. Thanks to its applications, you can check how your algorithms work and then view the resulting information in charts or share it directly in the cloud.
6. RapidMiner
RapidMiner has modeling tools that want to be automated so that you can work with them without much help. In addition, it offers a certification program to improve the skills of people who are not experts in the field. The data you get through this platform is intended for information professionals, but is simplified for all profiles.
Who needs a crystal ball when you have opportunities like this in your field. Remember that information is one of the most valuable assets in the industry, so use it responsibly and grow your business.
