Predictive Analytics Workflow
Many companies use predictive models in their activity to provide better customer service, sell more products and services to customers, manage risk from fraudulent activity, and better plan the use of their human resources (to list a few important examples). How does predictive analysis offer all of these benefits? In this article we will consider the process of predictive analytics, and its related advantages.
Defining the goal
The starting point in your analysis is determining the goal of your project. Having a clearly-set task is crucial to implementing the correct predictive analytics methodology and choosing necessary data sets.
Data Collecting
The next step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), geographical data, and other options. It is important to have accurate and up-to-date information. Most of the time, you will have information from multiple sources and it will often be in a raw state. Some of it will be structured in tables, while other parts will be semi-structured or even unstructured, like social media comments.
Data organizing
The third step seems obvious: analysts must organize the data. This is called data preprocessing. Data preprocessing includes cleaning, normalization, transformation, feature extraction and selection. Сleaning is used on raw data which may be incomplete, noisy (e.g., containing errors or outlier values), or inconsistent. If your data is from multiple sources, then you also need to combine data into a coherent store. Feature selection is needed to reduce the calculation time, since complex data analysis on huge amounts of data may take a very long time. Preprocessing usually takes up 80% of the time and effort dedicated to analysis overall.
Model Developing
Once you have the data in a nice, clean, and well-prepared format, you need to develop a model. In most cases, the best solution is to use already existing tools, like Decision Trees, different linear models, Logistic Regression, or Neural Networks (to mention a few of the many options available). You can find these tools in libraries built on open-source programming languages (for example, R and Python). The data scientist’s task is to know the available model types and choose the best one for the job. In general, model types can be grouped into three main types:
- Classification is used when we need to predict the category of a new piece of dataset based on its characteristics. Classification algorithms are useful for customer segmentation, spam detection, text analysis, etc. Classification includes the following methods: Decision Trees, Random Forests, Naïve Bayes, k-Nearest Neighbors, and other techniques.
- Regression is used for predicting outputs that are continuous. Price optimization and stock price prediction are typical case studies for the use of regression algorithms. The regression methods used include Logistic regression, Polynomial regression, Linear regression, etc.
Several techniques can be applied together to produce better predictions in combining models.
Model Training and Optimization
Then, the training data set is used to train a model and optimization parameters. When the training is complete, you can try the model with new data to evaluate how well it performs (model validation).
Model Validation
Next, you must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can only be valid for a certain period of time, since reality is not static and environments can change significantly. For example, customer preferences may change so fast that previous expectations become outdated. So, it is important to monitor models periodically.
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Predictive Analysis in Business
Decision-making in business is often based on assumptions about the future. Many companies aspire to develop and deploy an effective process for understanding trends and relationships in their activity in order to gain forward-looking insight to drive business decisions and actions. This is called predictive analytics. We can define predictive analytics as a process that uses data and a set of sophisticated analytic tools to develop models and estimations of an environment's behavior in the future. In predictive analysis, the first step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), and geographical data, among other options. It is important to have accurate and up to date information. Most of the time, you will have information from multiple sources and, quite often, it will be in a raw state. Some of it will be structured in tables, while the rest will be semi-structured or even unstructured, like social media comments. The next important step is to clean and organize the data - this is called data preprocessing. Preprocessing usually takes up 80% of the time and effort involved in all analysis. After this stage, we produce a model using already existing tools for predictive analytics. It is important to note that we use collected data to validate the model. Such an approach is based on the main assumption of predictive analytics, which claims that patterns in the future will be similar to the ones in the past. You must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can only be valid for a certain time period, since reality is not static and an environment can change significantly. For example, the preferences of customers may change so fast that previous expectations become outdated. So, it is important to monitor a model periodically. There are plenty of applications for business based on predictive analytics. To conclude this article, we will briefly consider some of them.
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