What Is Predictive Analytics?
Predictive analytics is where an organization uses mathematical or computational models or algorithms to predict future trends or events.
Apps or solutions that use predictive analytics will process historical data with machine learning (ML), statistical modeling, and data mining to identify potential patterns that could be replicated in the future. In short, it can process existing data to forecast potential scenarios, outcomes, and behavior.
Predictive future outcomes in this manner can help to create insights that enterprise stakeholders can use to make better decisions.
How Does Predictive Analytics Work?
There are six main steps when deploying predictive analytics in the enterprise:
- Set an objective: Identify what problem you’re trying to solve and what insights you’re trying to develop. For example, “I want to increase customer acquisition by x%, and I want to understand what factors have the greatest impact on acquiring new customers.”
- Compile your data: Find potential data sources like files, spreadsheets, databases, and sensors and start collecting the data for processing.
- Begin pre-processing: Clean the raw data to remove errors and anomalous entries and then aggregate the sources together as part of a single repository.
- Create a predictive model: Use machine learning, regression models, and decision trees to process the dataset and attempt to solve your initial objective.
- Check the accuracy: Verify the accuracy of the model output and adjust it as necessary, optimizing the parameters and validating the model over time.
- Share the results: Once the output is accurate and usable, share the insights with decision-makers via an app.
It’s important to note that the effectiveness of a predictive analytics solution depends on a number of factors, including the amount of data the tool has access to, the data quality, and the techniques used to process it.
Predictive Analytics Techniques
When looking at predictive analytics models, there are two main types of models organizations use: classification models and regression models.
- Classification models classify data objects under one category or another;
- Regression models attempt to predict continuous data like monthly sales volume or customer satisfaction scores.
There are then three main techniques that can be used to process data:
|Regression analysis||A statistical technique used to continually measure the relationship between variables. Essentially, it allows you to see what type of influence one variable has on another.
For example, you could look at the relationship between how much users buy based on their location, age, demographic, gender, marital status, or device.
|Decision trees||A predictive classification model that classifies input data into a given category. It functions like a flowchart, starting with a single node, which branches out into other nodes mapping other potential outcomes.
It not only shows what decisions can be made but also shows the probability of a potential outcome.
|Neural networks||A subset of the machine learning process where a large network of nodes is connected together to study the structure of a dataset. These neutral networks can be trained continuously on a dataset to recognize patterns and gradually make predictions according to them.
For instance, they can predict sales trends, revenue, customer churn, and other data.
Benefits of Predictive Analytics
Being able to use data to read or prepare for the future effectively has a number of key advantages for organizations:
- Operational planning: With predictive analytics, organizations can check if current resources are adequate to meet future demand or if there are upcoming maintenance costs or supply chain shortages that need to be prepared for. This gives organizations a heads-up to take action to mitigate operational disruption.
- More personalized marketing: Predictive analytics enables organizations to offer customers personalized real-time recommendations, campaigns, and deals that they’re most likely to be interested in. This can not only help boost sales but also increase customer loyalty by offering a more relevant customer experience.
- Better decision-making: Making data-driven decisions based on historic data and correctly anticipating future events leads to better overall decision-making that can grow a business effectively while minimizing operational risk. This can offer a key competitive edge against other organizations.
- Cost-effectiveness: Analyzing data at scale enables an organization to see how to better invest in growth. If running ads on certain marketing channels is significantly more profitable than another, they may opt to reduce spending on channels that generate less ROI.
- Cybersecurity: Organizations can use predictive analytics to identify anomalous or malicious activity within a network early that could indicate a cyber attack early so they can respond before a cybercriminal has a chance to steal or encrypt protected information.
Is Predictive Analytics a Crystal Ball?
With the right data and detailed statistical modeling, organizations can convert unactionable raw data into concrete insights that decision-makers can use to guide the success of the business as a whole.
While predictive analytics solutions can’t predict the future, they can help enterprises to evolve to capitalize on market opportunities if they were to rely on human judgment alone.