Product Analytics Part II: Predictive analysis

To get great insights from your data, you need to familiarize yourself with the four types of data analytics to understand where business current position is and where there is potential to grow.

Briefly they are:

Descriptive analytics: It allows you to pull trends from raw data and succinctly describe the past or present events. Descriptive analytics answers the question, “What happened?” Data visualization is a natural fit for communicating this type of analysis
Diagnostic analytics: addresses the next logical question, “Why did this happen?” In this step, analysist will investigate comparing coexisting trends, unknown correlations between variables and determining causal relationships if possible.
Predictive analytics: as literally the term suggests, we want to predict future trends or events that answer to the question “What might happen?”
Prescriptive analytics: we want to answer to the question of “what should we do next?” with this type of analytic analysis. Considering all possible scenarios and factors, this analysis suggests possible and more efficient courses of action.

The power of predictive analytics is its ability to predict outcomes and trends before they happen. Using historical data and information of the current situation, it can predict future outcomes. Identifying the best predictive analytics model for your business is a crucial part of business strategy. The use of these models has become so popular and gives so much benefit to businesses that for example nowadays analytical tools, used to build dashboards, have already incorporated them just at one click distance to be applied. In the following post we will introduce you to the fundamental concept of predictive analytics and you will discover all its potential.

This post is the closing topic of our Analytics Beyond Dashboards series. Please take a moment to explore our previous posts in the series if you have not yet:

How do predictive analytics models work?

All predictive models are trained using one or more predictive algorithms. It is a cyclic process that begins with the understanding of the business objective and the data, followed by data preparation. This step is essential as predictive models will only be able to predict based on the information that we provide to them.

Now with a solid data foundation the model is trained, and results are analyzed. The analysis of the results does not only involve looking into the final number that a forecast can give to us or the accuracy of the predictive algorithm. As analysts we also investigate the details, we identify the variables that contribute to the result as might be the key for maintenance.
The last step is to deploy the model ensuring it seamlessly integrates into the intended application and begins making impact.

A predictive model is not a one-time task, the model needs to be validated every certain time, might need to be retrained for example because of new data inputs or adjustments in the predictive algorithm for a better result.

The top 5 predictive analysis models

Classification models

These models arrange the data in categories based on what they learn from the historical data. Classification models can provide a binary solution to facilitate a comprehensive analysis.
Some examples of classifications algorithms are: Logistic regression, Decision trees, Random Forest, Neural networks, Naïve Bayes.

Clustering models

Clustering models help sort data into distinct groups based on multiple attributes. This analytic model class is the best choice, for example, for dividing the data into smaller data sets with common characteristics for effective marketing strategies.

You can further divide the predictive clustering modelling into two categories: hard clustering and soft clustering. Hard clustering helps to analyze whether the data point belongs to the data cluster or not. However, soft clustering helps to assign the data probability of the data point when joining the group of data.

Some popular algorithms are K-Means, Mean-Shift, Density-Based Spatial Clustering with Noise (DBSCAN).

Forecasting models

This class of predictive analytics models helps businesses with estimating the numeric value of new data based on historical data.

The most important advantage of the forecasting is that it also considers multiple input parameters simultaneously. It is why the forecast model is one of the most used predictive analytics models in businesses.

Outlier models

Unlike the classification and forecast models , which works on the normal historical data, the outlier model of predictive analytics considers the anomalous data entries from the given dataset for predicting future outcomes.

The model can analyze the unusual data either by itself or by combining it with other categories and numbers present. Examples of applications can be found in the financial or retail industries were identifying outliers (ex. suspicions money movements in bank transactions) can save a lot of money identifying fraud.

We can say that Isolation Forest, Minimum Covariance Determinant (MCD) and Local Outlier Factor (LOF) are the most popular.

Time series models

As the name says, the best applicability is when time is the input parameter for our prediction. This predictive model works with data points drawn from the historical data to develop the numerical metric and predict future trends.

This model involves the conventional method of finding the process and dependency of various business variables. Also, it considers the extraneous factors and risks that can affect the business on a large scale with passing time.

A few of the common time series models are: ARIMA and Prophet.

Predictive analytics tools

The range of possibilities is wide from no-code tools to machine learning algorithms. It is on each business to decide what best fits their needs, expertise, time schedule for application, etc. Some tools are complete workspaces while others can be integrated with existing tools. There are solutions for cloud deployments and on-prem.

The major shift of modern predictive analytics tools is that they have become easier to implement compared to existing models or building new ones from scratch. The new capabilities of automated machine learning reduce the need to deeply understand how the variables affect each other, automatically choosing the best combination of algorithms for a given tasks, reducing the time that an analyst needs to spend in writing code.

Here we will list some considerations as best practices to select the more appropriate predictive analytic tool for your case:

Think first about the company’s application needs and then search for the tool, keeping in mind that an individual tool or a group of services could be chosen. Not all businesses need the full package, and it could be the case that existing tools for business intelligence, analytics or CRM already support your needs.
Consider the use of automated machine learning services or prebuilt models, templates, or toolkits in conjunction with standard language, like R or Python, and visualization services, like Tableau or Qlik Sense, to add unique attributes to your solutions.
Think on who will be using these tools, some users might look for tools to augment data discovery, data preparation and model development while others might look for services that provide guardrails for common business requirements and support collaborative development across teams.
Plan on regular enhancements of applications based on changes on the data, improving accuracy and performance, to make applications more user friendly and intuitive, overcome security threats and reduce costs with better efficiency.

The Data Analytics role

Predictive Analytics is also known as predictive modelling, with the first term being the most preferred one for commercial applications and the second one in academic environment. Successful use of predictive analytics depends heavily on unfettered access to sufficient volumes of accurate, clean, and relevant data.

Predictive models for everyday business cases can run and give accurate results in real time. To align with this trend, who is better than a data analyst as the most indicated and efficient person to be able to make a quick and accurate choice of a model and the evaluation of its results. The reason is based on the facts that this person is already familiar with the data due to the data profiling and data preparation made for populating the model and of course knows the business case since it needs to be given as input for the previous task.

Evaluating the performance of predictive models is a critical task that data analysts can easily assess using a variety of metrics. Also, their skills in data visualization allow them to present the interpretation of the models results in the most user-friendly way possible.

Predictive analytics is a wide and important part of data analytics. Exploring its endless possibilities can empower your business with data-driven insights for a better decision-making strategy.