Types of predictive analytics models and how they work
What are predictive analytics models?
If you have been working or reading about analytics, then predictive analytics is a term you have heard before. Currently, the most sought-after model in the industry, predictive analytics models are designed to assess historical data, discover patterns, observe trends and use that information to draw up predictions about future trends. While the economic value of predictive analytics is often talked about, there is little attention given to how they are developed. So, the topic of this blog post will focus on the type and development of predictive models.
Types of predictive models
Predictive analytics models are not a monolith. There are different models developed for design-specific functions.
A forecast model is one of the most common predictive analytics models. It handles metric value prediction by estimating the values of new data based on learnings from historical data. It is often used to generate numerical values in historical data when there is none to be found. One of the greatest strengths of predictive analytics is its ability to input multiple parameters. For this reason, they are one of the most widely used predictive analytics models in use. They are used in different industries and business purposes. For example, a call centre can predict how many support calls they will get in a day or a shoe store can calculate inventory they need for the upcoming sales period using forecast analytics. Forecast models are popular because they are incredibly versatile.
One of the most common predictive analytics models are classification models. These models work by categorising information based on historical data. Classification models are used in different industries because they can be easily retrained with new data and can provide a broad analysis for answering questions. Classification models can be used in different industries like finance and retail, which explains why they are so common compared to other models.
While classification and forecast models work with historical data, the outliers model works with anomalous data entries within a dataset. As the name implies, anomalous data refers to data that deviates from the norm. It works by identifying unusual data, either in isolation or in relation with different categories and numbers. Outlier models are useful in industries where identifying anomalies can save organisations millions of dollars, namely in retail and finance. One reason why predictive analytics models are so effective in detecting fraud is because outlier models can be used to find anomalies. Since an incidence of fraud is a deviation from the norm, an outlier model is more likely to predict it before it occurs. For example, when identifying a fraudulent transaction, the outlier model can assess the amount of money lost, location, purchase history, time and the nature of the purchase. Outlier models are incredibly valued because of their close connection to anomaly data.
Time series model
While classification and forecast models focus on historical data, outliers focus on anomaly data. The time series model focuses on data where time is the input parameter. The time series model works by using different data points (taken from the previous year’s data) to develop a numerical metric that will predict trends within a specified period.
If organisations want to see how a particular variable changes over time, then they need a Time Series predictive analytics model. For example, if a small business owner wants to measure sales for the past four quarters, then a Time Series model is needed. A Time Series model is superior to conventional methods of calculating the progress of a variable because it can forecast for multiple regions or projects simultaneously or focus on a single region or project, depending on the organisation’s needs. Furthermore, it can take into account extraneous factors that could affect the variables, like seasons.
The clustering model takes data and sorts it into different groups based on common attributes. The ability to divide data into different datasets based on specific attributes is particularly useful in certain applications, like marketing. For example, marketers can divide a potential customer base based on common attributes. It works using two types of clustering – hard and soft clustering. Hard clustering categorises each data point as belonging to a data cluster or not. While soft clustering assigns data probability when joining a cluster.
How do predictive analytics models work?
Predictive analytics models have their strengths and weaknesses and are best used for specific uses. One of the biggest benefits applicable to all models is that they are reusable and can be adjusted to have common business rules. A model can be reusable and trained using algorithms. But how do these predictive analytics models actually work?
The analytical models run one or more algorithms on the data set on which the prediction is going to be carried out. It is a repetitive process because it involves training the model. Sometimes, multiple models are used on the same data set before one that suits business objectives is found. It is important to note that predictive analytics models work through an iterative process. It starts with pre-processing, then data is mined to understand business objectives, followed by data preparation. Once preparation is complete, data is modelled, evaluated and finally deployed. Once the process is completed, it is iterated on again.
Data algorithms play a huge role in this analysis because they are used in data mining and statistical analysis to help determine trends and patterns in data. There are several types of algorithms built into the analytics model incorporated to perform specific functions. Examples of these algorithms include time-series algorithms, association algorithms, regression algorithms, clustering algorithms, decision trees, outlier detection algorithms and neural network algorithms. Each algorithm performs a specific function. For example, outlier detection algorithms detect the anomalies in a dataset, while regression algorithms predict continuous variables based on other variables present in the dataset.
Creating predictive algorithm models
While developing a predictive analytics model is no simple task, we managed to break down the process to six essential steps.
Defining scope and scale – Determine the process that will use the predictive analytics models and what the desired business outcomes will be.
Profile data – Predictive analytics is data-intensive. So the next step is to explore the data needed for analysis. Organisations have to decide where it is stored, its current state, and how accessible will it be.
Gather, cleanse and integrate data – Once data is found, it needs to be cleaned and gathered. It is an important step because predictive analytics models need a strong foundation to work effectively.
Incorporate analytics into the business process – The model can only be used to integrate it into the business process to get the best outcomes.
Monitor models and measure the business results – The model needs to be measured to see if it makes genuine contributions to the overall business processes.
Limitations of predictive analytics models
Despite the immense economic benefits predictive analytics models, it is not a fool-proof, fail-safe model. There are some disadvantages to predictive analytics. Predictive models need are specific set of conditions to work, if these conditions are not met, then it is of little value to the organisation.
The need for massive training datasets
For predictive analytics models to be successful at predicting outcomes, there needs to be a huge sample size representative of the population. Ideally, the sample size should be in the high thousands to a few million. If datasets are smaller than the predictive analytics models will be unduly influenced by anomalies in the data, which will distort findings. The need for massive datasets inevitably locks out a lot of small to medium-sized organisations who may not have this much data to work with.
Properly categorising data
Predictive analytics models rely on machine learning algorithms, and these algorithms can properly assess data if it is labelled properly. Data labelling is a particularly demanding and meticulous process because it needs to be accurate. Incorrect classification and labelling cause several problems, like poor performance and accuracy in findings.
Applying learnings to different cases
Data models have a problem with generalisability, which is the ability to transfer findings from one case to another. While predictive models are effective in their findings for one case, they often struggle to transfer their findings to a different situation. Hence, there are some applicability issues when it comes to the findings derived from a predictive analytics model. However, there is a solution in certain methods, like transfer learning that could help mitigate some of these shortcomings.
Predictive models in the future
The future will see predictive analytics models play an integral role in business processes because of the immense economic value they generate. While not perfect, the value they offer organisations, both public and private, is immense. With predictive analytics, organisations have the opportunity to take action proactively in a variety of functions. Fraud prevention in banks, disaster prevention for governments and sublime marketing campaigns are just some of the possibilities tangible with predictive analytics models, which is why they will be an intangible asset for the future.