Predictive Analytics

“What Happened” Vs “What Will Happen”

Introduction:-

The world we live in today is majorly influenced by Predictive Analysis, even though we don’t realize it. If we have ever used a flight cost predictor like Google Flights or browsed through movie recommendations on Netflix, you’ve benefited from predictive analytics.

Predictive analytics has also made its way into business applications. So many application teams are including predictive analytics capabilities in their software because of the enormous value it offers to end-users and application teams.

Business Context:-

What is Predictive Analytics?

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?” A mathematical model uses historical data to identify key trends and patterns to predict what will happen in the future

What is the value of being Predictive?

For end-users, predictive analytics can give them 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 customer-facing/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 the finance sector, 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/physicians and health care providers can identify high-risk patients to prioritize for screening and recommend preventative treatments. This also consequently reduces hospital readmissions.
• For Businesses, adding machine learning and artificial intelligence into your application sets you apart from the competition and allows end-users to make better decisions, which in turn, gives you the opportunity to increase revenue.

Adoption Strategy:-

Predictive analytics is a complex capability, and therefore implementing it is also complicated and comes with challenges. When companies take a traditional approach to predictive analytics, they often hit roadblocks, such as:

• The need for a data scientist with statistical modeling expertise.
• A multi-step process every time you do an update or release.
• A failure to let users take immediate action from inside the predictive application.
• A steep learning curve, leading to low user adoption.

Just like any other new feature or capability that you introduce through your software, if you want your end-users to use it, you need to meet them where they are in the applications where they already spend their time. If predictive analytics lives as a standalone or separate tool, it will simply never get adopted.

To address challenges around user adoption, distribution of predictive analytics, and closing the insight-to-action gap, you need to embed predictive analytics directly into your application. This will allow end-users to quickly and efficiently see what is going to happen in the future and subsequently act on it without leaving your application.

Even better, some emerging embedded predictive analytics tools are designed specifically for a range of users and do not require expertise in statistical modeling. Moreover, they help reduce the burden on application teams by streamlining a lengthy development.

To maintain and improve model performance, application teams need to build in periodic re-training. But that may just be the start. Any time we want to use the model in a new way—such as in a market where customer behavior is likely to be different from the data we originally used for training and validation—we will need to re-train the models.

Boost Predictive Performance Over Time - The Path Forward

Updates not only prevent accuracy from backsliding, but they can also boost performance going forward. The beauty of machine learning is that as algorithms analyze more and more of your customers’ data over time, they generate smarter and smarter predictive models with each refresh.

Depending on our application, it may be useful to measure additional dimensions of model performance, which could suggest ways to improve further.

Sensitivity measures the number of correct “yes” predictions as a percentage of actual “yes” outcomes. It is an important measure for let's say a banking application, where the model might, for example, be predicting customers at a high risk of retention. A 15 percent error rate—where the model predicts good retention for customers who end up moving to a differential FI —could prevent crucial screening and early retention program enrollments.

Specificity measures the number of correct predictions as a percentage of the actual outcome.

Usually, there is a tradeoff between sensitivity and specificity. So if sensitivity is more important for the application, we can increase it by lowering the threshold for specificity, and vice versa.

One way to make such refinements is through data sampling methods as a correction for data imbalance problems. These same techniques can also be used to refine model performance.

The Key Takeaway would be to understand that predictive analytics is not a static report that describes the past. It uses machine learning to predict future outcomes based on what it can learn from the past. Predictive analytics is only as good as the underlying model and the most recent data, which is generated by people with choices and habits that change frequently. Best in class organizations are constantly monitoring their predictive analytics to improve them and find new opportunities to improve their organization’s performance, and it shows in their financial success.