Predictive Analysis

Data-driven decision-making to avoid surprises by predicting, identifying and responding to future events before they happen

What's Inside

    What is Predictive Analysis?

    Predictive Analysis is a powerful approach to data-driven decision-making that helps you avoid surprises by identifying and responding to future events before they happen.

    It can be used for automating decisions, such as

    • What products or services should be on your next sales call list
    • Predicting demand spikes for certain items with analytics tools like Google Trending searches or email open rates
    • Analyzing visits at historical locations based on average times spent inside a location relative to traffic volume outside the doorways
    • Calculating when an event will occur (e.g., how many days until Christmas).

    Popular Predictive Analytics Techniques

    - Linear and logistic modeling which are used to model relationships between variables, for example, how much money you spend online is likely related to your age and income level. This type of analysis can be done with common spreadsheet programs like Microsoft Excel or Google Sheets.

    - Classification systems that use machine learning algorithms to predict future outcomes by analyzing past experience. The system finds patterns in historical data such as customer buying habits and applies those same principles when new information becomes available about what customers buy from a company that could help optimize marketing efforts for specific demographics , future risks , and more.

    - Bayesian statistics which is a form of applied probability that uses statistical models to make predictions by updating the strength of beliefs based on new data. Rather than using probabilities, this approach updates existing assumptions with revised information in order to achieve better prediction accuracy .

    - Classification systems are often used for risk analysis because they provide insights into what might happen in the future. A common example is predicting how likely a customer will default on their loan payments or if an airplane could be delayed. Effective classification requires not just historical data but also market intelligence for understanding the most relevant factors driving those outcomes. This can include things like credit score, location, number of dependents, and stability of employment status.