We understand that it is impossible to look into the future, but this does not mean that it is impossible to predict. The past shapes the future and if you have enough data from the past, as well as predictive analytics tools, you can predict with a high probability what will happen tomorrow, in a year or 5 years. The longer the time period involved, the less accurate the results.
Do you need to explain how predictive analytics is useful in creating PR campaign strategies? Well, it is not difficult—we have already explained 3 key reasons.
What is the basis of predictive analytics for PR campaigns? Data and mathematics. It is mathematical insights into data that allow you to create well-founded proposals about what should or can happen in the foreseeable future.
Step-by-step Plan Predictive Modeling and Analytics in PR
According to Statista estimates, the predictive analytics software market will reach $41.52 billion by 2028. This is an 8-fold increase compared to 5.29 billion U.S. dollars in 2020. What exactly triggered such rapid growth? The emergence of working AI solutions. If you are aiming for a data-driven PR campaign, you cannot do without AI, mathematical calculations and data. The more objective information you can collect, the better.
To understand predictive analytics, let’s break it down into four simple steps:
- Data Collection: Gather data from various sources—this could be sales figures, customer information, weather data, or any relevant details for your prediction.
- Data Preprocessing and Cleaning: Remove errors, duplicates, and irrelevant information from your data. Think of it as tidying up your room before guests arrive.
- Modeling: Use mathematical models to identify patterns in your cleaned data. These models are your tools to understand how things work.
- Validation: Test the accuracy of your predictions using a new set of data that your models haven’t encountered before. This step ensures your predictions are reliable.
Mathematical Insights For Boosting PR Campaigns
Predictive analytics is based on mathematical formulas. In simple terms, it’s just a bunch of math, no magic. High-quality analytics requires deep knowledge of algorithms, integrals, matrix algebra, and that’s not to mention simple operations with numbers. Even if we talk about the rules of predictive modeling algorithms, they are also based on mathematical calculations and if-then-else rules.
When we discussed PR analytics and the importance of mathematics in all this, many thoughts came in that many of us already have difficulty operating complex mathematical constructions. Anyone can build a bad model, but the accuracy will directly depend on the depth of understanding of mathematical equations. Even the calculation skills themselves are not so important, you can just use Math Solver AI, it gives accurate results. But a person (or some algorithm with AI) still needs to create the formula.
Here is an example of tree pruning based on data and predictive analytics:
- Splitting metric (CART style trees, C5 style trees, CHAID style trees, etc.)
- Terminal node minimum size
- Parent node minimum size
- Maximum tree depth
- Pruning options (standard error, Chi-square test p-value threshold, etc.)
The most complex mathematical operation is the splitting metric. CART-styled trees use the Gini Index, C5 trees use Entropy (information gain), and CHAID style trees use the chi-square test as the splitting criterion. Shouldn’t the same be done for PR performance optimization?
Predictive Analytics Techniques
Although the basis is mathematics everywhere, predictive analytics mechanisms work differently. If the results from several methods coincide, you did everything correctly. Although this is not necessary, the same conclusions mean the correctness of the collected and loaded data.
Regression Analysis
Linear regression helps us understand how one variable changes with another. For instance, it can show how house prices increase with size. Polynomial regression can handle more complex relationships, while logistic regression predicts probabilities, like whether a customer will buy a product based on age and browsing behavior.
Feature Scaling and Selection
Feature scaling ensures no single feature dominates, while feature selection picks the most relevant features to create the best result.
Neural Networks
Inspired by the brain’s neurons, neural networks process data through interconnected nodes in layers, each learning a different level of abstraction. They excel at handling complex data such as images, speech, and text. Deep learning, a subset, uses many layers to identify intricate patterns.
Random Forest
Random forests are like consulting a group of friends for advice. Each decision tree in the forest makes a prediction, and the final output is based on the majority vote, reducing overfitting and enhancing reliability.
Support Vector Machines (SVM)
SVMs find the best line or curve (hyperplane) to separate different data classes, perfect for classification tasks like spam detection.
Cross-Validation
Cross-validation tests your model with various data sets to ensure it generalizes well, similar to practicing with different test questions.
Time Series Analysis
Time series analysis examines data points that change over time, such as sales or stock prices. It identifies patterns and predicts future values based on past trends, aiding in business planning.
Decision Trees
Decision trees simplify complex decisions by breaking them down into straightforward questions, similar to playing 20 Questions. Each branch represents a possible outcome based on data features, leading to a prediction.
Clustering
Clustering groups similar data points together, identifying segments within your data, like different customer types or market segments.
Ensemble Methods
Ensemble methods combine multiple models for more accurate predictions, using techniques like bagging and boosting to improve performance.
Conclusion
To sum it up, although math is the basis of all predictive models, PR specialists do not need to get a math degree. It is enough to understand the concepts, having a deep mathematical vision will only improve the result. But in the world of technology, there are enough shells that help you not to interact with formulas directly. Even if you have to solve some integrals, you can still use AI to get the right answers. And remember: “Those who ignore statistics are condemned to reinvent it” – Bradley Efron of Stanford University.
