Remember the last time? We spoke about introducing a PACE-powered framework for your Supply chain process optimization.
The first letter of the acronym PACE depicts predictive, and that is the thing we are going to discuss in this blog. With the power of predictive modeling, you can make informed decisions that will benefit your organization no matter what your niche is.
So, without any further ado, let’s get you started with it.
Addressing Predictive Modeling in Layman’s Language
In general, predictive modeling refers to a process that leverages data and stats to predict all sorts of outcomes with the help of data models. These data models are capable of predicting outcomes from TV ratings to business expenditures.
The interesting fact is when people address predictive modeling, they often refer to these three terms,
- Predictive Analysis
- Predictive Analytics
- Machine Learning
These are like synonyms of predictive modeling; you can use them interchangeably. However, it is worth mentioning that predictive analytics often referred to as the commercial application of the term predictive modeling. In addition to that, predictive modeling is more of an academic term. If we talk about machine learning, the concept is quite distinct from predictive modeling. Machine learning is an application of statistical data to empower machines to construct predictive models. However, in practice, people do use machine learning and predictive modeling interchangeably even though machine learning is a part of artificial intelligence.
Here in this post, we’ll stick to predictive modeling.
An Overview of Predictive Modeling
Predictive modeling is beneficial for an organization as it gives futuristic insights that will help you in maintaining a competitive advantage. How will you feed the predictive model? The following are the sources that your analyst can use for the predictive model.
- Survey Data/Polling
- Advertising and Digital marketing data
- Economic data
- Web Traffic Stats
- Data from sensors and beacons
- Transaction data
- CRM software data
To be the leader in the business, you must align the predictive model with your enterprise’s strategic goals. Data organization is another aspect that an analyst must focus on. The model must align with the data so that machines (computers) can create outputs and forecast for hypothesis testing. Then the business intelligence tools will give you insights in visuals or graphs or report format. So, when you are integrating predictive models in your business process, the following are the things you need to consider:
- Benchmark analysis
- Goals & KPIs evaluation
- Plan execution
- Process streamlining
- Action plan `as per the report
How Predictive Modeling & Data Analytics Resonates Each Other?
As we all know, data analytics is further segmented into four types. Out of these four, predictive modeling is closely related to the predictive analytics category. Let’s have a look at these four types of data analytics to understand the deeper meaning.
The primary role of descriptive analysis is describing the data available. For example, a software company served to 2,000 clients in Q2 and 1,500 in Q1. Descriptive analysis will answer the question of how many clients were served in Q2 vs Q1.
If you want to know the “Why” part behind the descriptive analytics, then diagnostic analytics is all that you need. Let’s stick with the previous example. With diagnostic analytics, the above data will be drilled deeper. The data analyst will seek more data by determining marketing and sales efforts to take reference against sales growth.
It further adds value to the data because the analyst can also find what the reason for the sales increase was, was it the highly proficient salesperson or the hype in the industry!!!
In a nutshell, predictive analytics is the application of machine learning and data mining practices to depict what might happen in the future. The outcome is never accurate, but with the help of relevant data, it can determine the most likely outcome. It is different from data mining. Data mining is all about finding out hidden relation between variables while predictive analytics is about finding most likely results. With predictive analytics, the software company in the above example can create a predictive model to determine future revenue generation based on the marketing spends.
Once your predictive model is ready, prescriptive analysis comes to play and provide you with the recommendations based on the predicted outcome. It leverages the external data sources, stats history, and ML-based algorithms scripted in languages like python.
Applications of Predictive Modeling
Here are some real-world application of predictive modeling you might love,
Predictive Modeling in HR Analytics
From hiring to retention, HR analytics can leverage the potential of predictive modeling. The professionals can harness the power of the predictive model in making important strategic decisions like performance
management and workforce planning.
Here are some applications of predictive modeling in HR Analytics,
- Identifying employees with high retention risks.
- Identifying potential candidate best for the job
- Evaluation of technical skills based on screening tests and other forms of candidate evaluation.
- Predicting employee requirements in the next six months.
- Calculating incentives to encourage desired behavior.
- Determining behaviors to ensure high performance.
- Evaluating the effect of wellness on the performance of the employee.
- Predicting numbers of resources that will go for different insurance packages offered.
- Determining the efficiency of the managers in the enterprise.
Controlling Customer Churn Rate
Controlling the customer churn rate is crucial for any business. Prevention of customer churn is one of the most common use cases of
business analytics for all B2B & B2C companies.
A happy customer is all a business want. If a customer suddenly stops continuing their buying behavior, then the company need to work extra on either on new customer acquisition or selling more to other existing customers. In addition to that, new customer acquisition is always tricky and expensive; this makes customer churn even more critical for a business.
With the power of predictive modeling, you can control or prevent customer churning. If you have enough data, you can create models to identify the reasons why customers are not buying anymore. Is it the effectiveness of the product, or does the market is offering better products? With the right data, it becomes easy to identify and mitigate customer churning.
Predictive Modeling in Healthcare Sector
The best use case for predictive modeling in the healthcare sector is a medical diagnosis. With the silos of data every year, the data quantity available for creating accurate models is more than enough. There are many use cases where predictive modeling can be leveraged but the way medical diagnosis has paved its mark is making significant breakthroughs that are newsworthy.
There are devices and systems that are using predictive modeling with such brilliance that they are outperforming, even medical professionals.
However, it is highly unlikely that medical professionals can get replaced by machines. On the positive side of that, the way doctors are working now is has changed, and all the tasks of a medical professional will become more easy with natural language technologies.
Use Case in Finance & Banking Sector
The finance and banking sector is one of the most sophisticated lines in the industry that requires concrete measures to stop fraudulent transactions. Powered by predictive modeling, anomaly detection is one of the best applications of machine learning leveraged by the banking sector to detect any fraudulent transactions.
With the power of predictive modeling, these organizations
can now observe past data based on factors like geographic locations, transaction amount, and time to predefine a baseline for a typical spend behavior of the consumer. All this data helps the organization to detect any anomaly and send a warning to its customer to verify the purchase for safer transactions.
Optimization of Logistics Operations
Another industry that requires comprehensive predictive
modeling solutions is logistics. With intensive logistics operations such as delivery, predictive modeling can be extremely helpful in logistics planning, making cost-effective adjustments, and mitigating problems that can hamper the entire supply chain process.
With predictive modeling, you can optimize the driver’s route which will shorten the distance and delivery time. RFID-based sensors will capture data that resonate with the driver’s actions and vehicle performance. With such data, you can create a model that will help you in predicting right actions
to optimize the entire scenario.
Types of Predictive Models Discovered So Far
In terms of broad segmentation, predictive modeling is of two types: Parametric and Non-Parametric.
These terms are not jargon. The simplest difference between these two types is parametric make specific assumptions about the characteristics of the sample population used to create the model. Here are some of the types of predictive models for you:
- Ordinary Least Squares
- Logistics Regression
- Decision Trees
- Multivariate Adaptive Regression Splines
- Generalized Linear Models
- Random Forests
- Neural Networks
The crux of all these types of predictive modeling is to predict future outcomes with the help of data collected in the past.
Some Benefits of Predictive Modeling
The benefits of predictive modeling include cost-cutting in forecasting business outcomes, all environmental factors, market conditions, and competitive intelligence. Below are some of the ways through which you can
harness the real power of predictive modeling,
- Very useful in contemplating demand forecasts.
- Planning workforce and customer churn analysis.
- In-depth analysis of the competitors.
- Forecasting external factors that can affect your workflow.
- Fleet maintenance.
- Identifying financial risks and modeling credit.
What is the Future of Predictive Modeling?
With the advancement in the artificial intelligence domain, the future of predictive modeling seems very bright. The computing prowess will always be on the verge of a breakthrough, and with ever-increasing data collection and technology upsurge, computing devices will always bear the brunt for creating models. Here are some pointers that will clear out the future of
With predictive modeling technologies, there is a significant growth in data quantity and computing power, which, in return, improved the newsworthy technical breakthrough. Algorithms of predictive modeling are becoming sophisticated exponentially in countless domains, complex games, and computer vision.
Mitigation of Risks
With such advancement in predictive technologies, data silos (with the introduction of Big Data) are increasing, and with that increase, the risk of data security also increases.
Security concerns circles around the privacy of that data and how to protect that as well. In addition to that, researchers are also working on feeding in societal biases into their predictive models, which can be great for organizations and policymakers.
What We Learnt So Far!!!
All businesses can benefit from predictive modeling. With the power of those data models, you can gather all the relevant data about your customers, employees, and other factors and process it in the data model to
predict possible outcomes. This information may not be that accurate, but it allows you to be ready for all the possible outcomes.
How Seasia Will Help You With Predictive Modeling?
It’s simple!!! We harness the power of artificial intelligence and machine learning, and our data scientists will create innovative yet productive data models and systems that will help you process and access the data.