What is predictive analytics?

Predictive data analytics is moving from hindsight to foresight. While looking at past sales reports tells you what you sold (descriptive), predictive analytics analyses patterns within that sales data—perhaps combined with customer demographics, marketing spend, or economic indicators—to forecast future sales figures or identify which customers are most likely to make a purchase soon.

Some examples of what you might generate with predictive analytics include:

• A score indicating the likelihood a customer will churn (stop using a service)
• A forecast of demand for a product in the next quarter
• An alert signifying a high probability of equipment malfunction within a specific timeframe
• A segmentation of prospects based on their predicted likelihood to respond to a marketing campaign

Ultimately, the goal of explaining and utilizing predictive analytics is to empower organizations to make proactive, data-driven decisions.

By anticipating future possibilities, businesses can optimize operations, mitigate risks, personalize customer experiences, and uncover new opportunities more effectively than relying solely on past performance or intuition.

How does predictive analytics work?

Predictive analytics achieves its forecasting power through various mathematical predictive models, which could even precede deep learning. These aren't interchangeable; the best approach depends entirely on the question being asked and the nature of the data available.

Understanding the main types of models helps clarify how predictive analytics works in practice.

Classification Models

Classification models are fundamental when the goal is to assign an item to a predefined category or class. They essentially answer "yes/no" questions or determine "which group" something belongs to.

These models learn from historical data where the categories are already known (labelled data), identifying patterns associated with each class. This learning is then applied to new data points to predict their most likely category.

Common applications are widespread, including filtering emails into spam or not spam, predicting whether a customer will churn or remain loyal, identifying financial transactions as potentially fraudulent or legitimate.

Regression Models

When the prediction required is a specific number rather than a category, linear regression models are employed, these models focus on forecasting continuous numerical values, answering questions like "How much?" or "How many?"

They work by analysing and modelling the relationship between various input factors (independent variables) and the target value (dependent variable). For instance, a business might use regression to forecast future sales revenue based on past performance and marketing efforts, or a real estate analyst might use it to estimate the market price of a house considering its features and location.

Predicting product demand and optimising resource allocation based on expected traffic are other key uses. (Linear Regression is a foundational example.)

Clustering Models

Distinct from classification, clustering models aim to discover natural groupings within data without relying on predefined labels.

They explore the data to group similar items together into 'clusters', answering the question, "What are the natural segments in this data?". The algorithms identify data points that share common characteristics, separating them from other dissimilar points. This technique is highly valuable for customer segmentation, where businesses can locate groups of customers with similar behaviours for targeted marketing campaigns.

Other uses include grouping related documents or research papers based on content, and sometimes identifying unusual data points that don't fit well into any cluster, which can be a form of anomaly detection. (K-Means is a widely used clustering algorithm.)

Time Series Models

Predictive modelling often involves forecasting future values based on past performance over time. This is the speciality of time series models, which analyse sequences of data points collected at regular time intervals (e.g., daily, monthly, yearly).

They are uniquely designed to account for the temporal dependencies within the data, identifying patterns such as long-term trends, predictable seasonal variations (seasonality), and other time-related cycles.

This makes them essential for tasks like financial forecasting (predicting stock prices or exchange rates), weather prediction, resource planning (estimating future call centre volume or energy demand), and optimising inventory management by anticipating future sales. (Models like ARIMA and Exponential Smoothing (ETS) are commonly used.)

How does predictive analytics work?

Predictive analytics is a systematic process that transforms raw data into valuable future insights using supervised machine learning models. While the specific tools and techniques can be complex, the overall workflow generally follows a series of logical steps, pulling data from a data warehouse or data lakehouse, often requiring iteration and refinement along the way.

1. Defining the objective: The journey begins with a clear understanding of the business problem or question. What specific outcome do we want to predict? Defining a precise objective is crucial because it guides every subsequent step, from data collection to model selection and evaluation.
    
2. Data collection: Once the goal is clear, the next step is to gather the necessary data. This involves identifying relevant data sources, which could include customer relationship management (CRM) systems, transaction databases, or sensor readings.
    
3. Data preparation: Raw data is rarely ready for analysis immediately. This stage, often the most time-consuming, involves cleaning and transforming the data. Data cleaning addresses issues like missing values, errors, duplicates, and inconsistencies. The goal is to create a clean, reliable dataset that accurately reflects the factors influencing the outcome you want to predict.
    
4. Model selection: With prepared data, the focus shifts to choosing the right predictive model. The choice depends heavily on the objective defined in step one. Often, data scientists experiment with multiple models to find the best fit.
    
5. Model training: This is where the learning happens. The prepared historical data (typically a large portion of it, known as the 'training set') is fed into the selected algorithm(s). The algorithm processes this data, identifying patterns, relationships, and correlations linked to the outcomes of interest.
    
6. Model evaluation: A model trained on data isn't useful unless its predictive accuracy can be verified. The model's performance is tested using a separate portion of the historical data it hasn't seen before (the 'test set'). Various statistical metrics are used to evaluate how accurately the model predicts outcomes compared to the actual results in the test data.
    
7. Model deployment: Once validated, the predictive model is ready to be put into action. Deployment involves integrating the model into the relevant operational systems or business processes.

The predictive analytics process doesn't end with deployment. Data patterns can change over time (a concept known as 'model drift'), potentially reducing the model's accuracy.

Therefore, it's crucial to continuously monitor the model's performance in the live environment. Regular maintenance, which might involve retraining the model with fresh data or even redesigning it if fundamental patterns shift, ensures the predictions remain accurate and relevant over time.

Overall, it’s a structured, iterative process – from defining the business need through data handling, modelling, and ongoing monitoring — that forms the backbone of how predictive analytics effectively turns historical data into actionable foresight.