The supreme command over competition leads to the long-term sustainable survival of the companies throughout the world. As a result, every institution is heading towards finding a way for mitigating competition. Some find the solution in their improvised sale and marketing strategies while others seek answers in their budget planning. These varying outlooks have led to a fragmented approach in the industry and made it difficult to compare the different growth strategies. Thus, predictive modeling emerged as the full-fledged AI-driven solution to adhere to companies’ expectations and channelize energies towards a growth perspective.
What is Predictive Modeling?
Generally, the term predictive modeling is used in an academic context whereas predictive analytics is used in commercial settings. It is AI and ML-enabled statistical technique used for predicting future outcomes and possibilities. Over a short span of time, the method has become the choice of several industries out there owing to its high accuracy rate. The prediction made on the basis of historical data and current trends provides a precisely accurate link to the planning and development process of the company. As a result, an extended horizon of the predictive model makes the competitive world gain calculative precision. The model algorithms are not fixed they are revised and validated periodically to incorporate suitable changes in the underlying data.
Though the algorithm of predictive modeling is complex in nature and requires experts’ hands to perform the job, the results so derived are highly accurate and speedy. The computational strength of the predictive models makes them an apt choice for quantum computing and computational biology too.
How do Predictive Analytics Models Work?
Predictive models are reusable, repetitive, and training businesses that work through an iterative process. The process begins with pre-processing and data mining to understand the business objectives. It is followed by data preparation, data modeling, evaluation, and finally the deployment. With the completion of one cycle, the process is iterated all over again.
The multiple models are used for similar data sets depending on the business requirements and objectives. Each algorithm is built to support specific data functions. For example, a regression algorithm is used for predicting continuous variables whereas an outlier detection algorithm identifies anomalies in the dataset.
The creation of the predictive models is nowhere a simpler task. As a result, the process is broken down into six important steps:
- Defining the scope and scale of the business outcomes.
- Exploring the data required for the analysis process, deciding its storage norms, and ensuring quick accessibility.
- Cleaning and integrating the data into a strong foundation for supporting predictive analytics and modeling process.
- Incorporating the analytics into business plans and strategies to achieve desired objectives.
- Monitoring and measuring the information derived from the process and controlling the gaps if any.
Types and Use Cases of Predictive Models
The predictive analytics models are design-specific functions that are divided into five broad categories to support the organizational purpose.
|Forecast Models||It is the most common analytics model which considers the metric valuation method for determining the value of new data. The model is based on historical learning and data that helps to generate numerical value and process multiple input parameters. As a result, they are used across different types of industries for specific business purposes. The use cases of the forecast model can be seen in the restaurant for determining the inventory of raw materials. Moreover, the model stands suitable for predicting call quantity and quality at the call centers. Additionally, the manufacturing companies can predict the customer taste and preferences while allowing a boost to the sales period.|
|Classification Models||The model work on the categorizing algorithm where historical data are categorized into distinct data sets. They are highly suitable data models for providing broad analysis of the subject. These model types have their extended use in eh finance and the retail sector where they can be used to identify fraudulent transactions, shortage of stocks, facilitate comprehensive budget allocation, etc.|
|Outlier Models||The forecast and classification model work based on the historical data whereas the outliers rely on anomalous data entries of the data set. An anomalous data are the data elements deviating from the basic norm and identify unusual patterns. The role specification of the outliers can be witnessed in the retail and finance sector to find anomalies and save organizations millions of dollars. Especially in the fraud identification process outlier models can assess the amount of money lost, last transaction history, location, nature of loss, and time. Furthermore, it plays a crucial role in determining risks a financial company might have to bear due to the irregular pattern structure of the client/ customer.|
|Time Series Model||The organizational activities are the blend of fixed and variable elements, of which various factors are responsible for the dynamic nature of the market scenarios. As a result, the time-series analytics model helps to predict the specific extraneous factors affecting business performance. The model is considered suitable for small business owners who aim to measure sales trends for the past four quarters by focusing on variable factors too. Additionally, the project management process also gains immense strength from the time series model of predictive analytics.|
|Clustering Model||The renowned model of analytics sorts data into distinct groups based on the common attributes. The segregation ability of the model makes it a useful tool for marketing. Using the concept, marketers divide their potential customers on similar ground and define hard and soft clustering. Hard clustering refers to a concept which indicates whether or not the data point belongs to a data cluster. On the other hand, soft clustering is used to assign a probability to the data set while joining a cluster. The major application of the clustering model extends to the determination of credit risk for loan applicants by examining the applicant’s past behavior in a similar situation.|
Metrics Used for Predictive Analytics Models
Since the predictive model considers both the historical and present data, therefore, it becomes important to make use of essential metrics to evaluate model performance. The common types of Predictive metrics which serve as the base of evaluation are:
|Confusion Matrix||It is also known as error matrix which plays an essential role in statistical categorization. It is a table that defines the actual and predicted classes of activities which establishes a clear distinction between the two performance roles.|
|Gain and Lift Chart||Such charts are used to access the effectiveness of the prediction so derived. Furthermore, it helps to check the ranking order of all the probabilities. As a result, the concepts calculate the probability of each observation and rank them in reduced order.|
|Concordant and Discordant Ratio||The ratio describes the relationship between pair of observations which is generally considered ordinary. The classification of items as concordant and discordant helps to establish a healthy comparative study between the related variables.|
|Kolmogorov Smirnov Chart||It measures the level of separation between negative and positive aspects where the value varies between 0 and 100. Here, models with the highest value are considered best.|
Algorithms Used for Predictive Analytics Models
The algorithms of the predictive models are either based on machine learning or deep learning. Both are important subsets of artificial intelligence and works on different data sets. Machine Learning involves structured data processing also known as machine data whereas the latter deals with unstructured data such as social media posts, text, videos, images, etc. to facilitate metric reading. Some of the most common types of predictive algorithms are as follows:
|Decision Tree||It refers to the non-parametric supervised machine learning algorithm used for creating value by targeting the desired variable in the data set. The algorithm plays a crucial role in statistics and data mining processes in business scenarios. As a result, it helps to establish a well-researched ground for decision–making.|
|Random Forest||It is considered as an advanced version of a decision tree algorithm where both classification and regression models are used to deal with a vast amount of data. Being a supervised machine learning algorithm it is essentially used in banking and e-commerce platforms to predict organizational behavior and related affairs.|
|Neural Network||These are the series of machine and deep learning algorithms which are used for reflecting on the behavior of the human brain. Furthermore, it allows computer programs to recognize and analyze patterns to derive suitable conclusions or predictions.|
|Regression Analysis||A regressive form of predictive modeling technique investigates the relationship between dependent and independent variables to draw suitable predictions. In general, the technique is used for finding the cause-effect relationship, time-series modeling, etc. of the involved variables.|
|Gradient Boosted Model||The technique also uses several decision trees in a related and connected format. The connectivity of the tree allows, next tree to correct the flaws created by the previous tree rather than carrying it forward. Such models facilitate ranking on search engines and online media platforms.|
|K-means||The technique is used in alignment with the clustering model and works extremely fast. The algorithm groups data points based on similarities and helps to render personalized results. Like, creating customized retail offers for each customer differently by figuring out the customer preference.|
|Generalized Linear Model (GLM) for two values||The GLM algorithm reduces the irrelevant element in the list of options to help find “best fit”. It works by tipping points and changing the data capturing process. As a result, it has lead to healthy categorical predictions and overcomes drawbacks in the other related models like linear regression.|
|Prophet||Being based on time-series and forecasting model, Prophet is used to identify the inventory needs, resource allocations, capacity planning, and sales quote. It has emerged as the most flexible predictive algorithm which also carries an array of useful assumptions altogether.|
The predictive Analytics model has supported a wide range of opportunities for the global market. On average, the predictive analytics market is expected to increase to $10.95 million by 2022. Alongside, the profound tool has witnessed a CAGR (compound annual growth rate) of 21% in less than a decade. Evidently predictive model has already established its significant mark in various industries leading towards the growth phase.
Topics in Predictive Analytics
- AI for Predictive Analytics
- Companies Using Predictive Analytics
- IoT and Predictive Analytics
- Predictive Analytics for Business Forecasting and Planning
- Predictive Analytics for Call Center
- Predictive Analytics for Sales Forecasting
- Predictive Analytics for Workforce Planning and People Management
- Predictive Analytics in Banking – Use Cases, Metrics, Examples and More
- Predictive Analytics in Health Insurance
- Predictive Analytics in Procurement
- Predictive Analytics Models
- Predictive Maintenance Analytics