What is Predictive Analytics?
Predictive analytics is an area of data mining that is related to the overall prediction of future probabilities and trends. It uses historical data, machine learning, and AI to predict what will happen in the future. A business intelligence technology tool produces a predictive score that informs actions that customers should take. Historical data is fed to a mathematical algorithm that looks for trends and patterns in the data, and creates a model for it. The model is then applied to current data to predict what will happen next.
At its core, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?” For example, an insurance company is likely to take into account potential driving safety factors, such as age, gender, and driving record when distributing automobile insurance policies. Predictive analytics can help organizations identify the most efficient marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease interruptions.
What is Predictive Analytics Used For?
Predictive analytics can give end users insights and suggest actions that directly impact operations, revenue, and risk assessment. It applies to business applications for a wide range of use cases across various industries. Some scenarios include:
- Reducing customer churn: A sales application with predictive analytics could analyze regular customer behaviors and alert the sales professional when a customer is likely to churn out. Targeted promotions can then be deployed effectively.
- Detecting fraud: In finance, predictive analytics helps identify potentially fraudulent behavior before it happens. Banks can predict loan defaults, approve credit, or detect suspicious activities.
- Reducing machine downtime: Predictive analytics can improve production capacity and reduce downtime by analyzing historical and online data from production lines.
- Flagging high-risk healthcare patients: Hospitals and physicians can identify high-risk patients to prioritize for screening and recommend preventative treatments. This also consequently reduces hospital readmissions.
How to Get Started With Predictive Analytics
For many application teams, predictive analytics might start as a one-off example to prove it can add value to their application, increase user satisfaction, and prove return on investment for the company. Regardless of the reasons, the steps are essentially the same.
1. Identify a Problem to Solve: Every predictive analytics question should offer measurable and clear value. The PADS framework can help you identify a question that solves critical business needs. PADS stands for: Preventing Problems, Assisting Humans, Detecting Problems, and Streamlining Services.
2. Select and Prepare Your Data: In order to run predictive analytics models, you need a dataset that can generate insights. In most cases, you’ll use both historical data, which is used to train your predictive algorithm how to predict an outcome, and new data, which is where future predictions are done. Most application teams store historical and new data in separate databases. But if you have one database for all your datasets, you can use a time filter (say, six months) on the historical data and a last-day or last-week filter to predict on new data.
3. Involve Others: Start with your organization’s stakeholders, including any executives or team leaders who will need to buy into the project. As stakeholders become advocates, they can help promote the initiative to others, making sure you get the cross-functional data you need and setting your project up for long-term success.
4. Choose Your Predictive Analytics Models: The predictive analytics model you choose will depend on what question you ask. Most predictive analytics questions will use one of these five common models:
- Classification Model: The classification predictive model answers a yes/no question. For example: Is this customer about to churn? Will this loan be approved? Is this a fraudulent transaction?
- Clustering Model: This is useful for sorting data points into groups. For example, you may wish to find the group of customers within your database who are most likely to be unsatisfied. You may need to further sort them into categories based on the kinds of problems they are likely experiencing. The clustering model can quickly sort these individuals with a high degree of accuracy.
- Forecast Model: This model predicts numbers – such as how many customers are likely to convert within a given week, how many support calls your call center will receive per hour, and how much inventory you should keep on hand.
- Outliers Model: This model highlights anomalies in your data. For example, it might record a spike in support calls, which could indicate a product failure that might lead to a recall. It could find anomalous data within an insurance claim and find an example of fraud. It could even find unusual information in your NetOps logs and notice the signs of impending unplanned downtime.
- Time Series Model: A time series is a sequence of data points captured in a timely order. For example, the number of daily calls received in the past three months; sales for the past 20 quarters; and the number of patients who visited a given hospital in the past six weeks. Time series is a powerful way to understand how a metric develops over time, and is often more accurate than other models which don’t consider time as a metric.
5. Close the Gap Between Insights and Actions: Many predictive analytics solutions fail to empower end users. They deliver insights, but don’t tell users what to do with the information—much less let them take action without leaving the application. This disconnect wastes time and interrupts your users’ workflows. For a predictive analytics project, you need to think hard about not just delivering the information, but also empowering your users to act on it. How can you make that information useful? First, you must get it to the right people. Then, you should suggest next steps based on the information, and ideally let users take action without leaving the application.
6. Build Prototypes: Start with something simple, and give it to your end users and stakeholders to beta test. These will be the first users of your product, and their feedback will shape the direction of your predictive analytics solution.
7. Iterate Regularly: As your project evolves, continuously engage with your testing group to review progress and incorporate new feedback.