Here’s How to Improve and Maintain Predictive Models Over Time

In the last few years, a lot has changed in terms of technology as we see businesses using different forms of artificial intelligence, automation, and other forms of technology. But, what about the predictive models based on machine learning frameworks to help analyze statistical data for better future decisions? Well, you no longer have to spend as much time or effort in maintaining or improving machine learning-based predictive models in businesses.

Organizations refreshed and updated predictive models only after many years of deployment in the past years. That wasn’t all! The gap between initial deployment and the first few updates was too much, resulting in productive inefficiency and non-valuable outputs.

It has become quite easy to improve and maintain predictive models with recent developments and modern updating techniques. Updates and retesting now only take a couple of months or a few weeks at best.

Model Retesting and Updating Time

Typically, there are many retesting and updating initially deployed predictive models. However, there are only three common methods that modern machine learning experts and framework designers stick to when trying to update predictive models. Here’s how most application teams retest and update predictive models over time without letting the deployment-update game increase:

· Seasonal Retesting and Updates

In several industries, including hospitality and retail, consumer behaviors change seasonally. The two main results of seasonal changes are either spikes in revenue and income or loss of customers for a short period of time. Application teams find it extremely easy/efficient to refresh, retest, and update predictive algorithms during such cyclic patterns and seasonal changes.

· Measurement-Based Retesting and Updates

This method of refreshing, retesting, and updating is based on the accuracy of the predictive models. Let’s say your predictive models right after deployment and shortly after were operating at 90 percent accuracy (theoretically). But over time, the predictive accuracy fell to 70 percent due to various changes. It’s a clear signal that application teams must reach out into their arsenal for retesting and updates to improve their predictive models for higher accuracy.

· Activity-Based Retesting and Updates

This is a very complex yet powerful method of retesting and updating. It involves getting ahead of behavioral changes by taking actions and including a proactive model refresh and update mechanism.

Benefits of Using Predictive Models

The majority of the leading businesses in multiple industries now perceive predictive models as highly helpful in competing with other businesses. Here’re the main benefits of predictive models:

• Reduce customer churn rate
• Timely detection and identification of high-risk and vulnerable patients (healthcare industry)
• Fraudulent invoices and transactions detection
• Targeted promotion and marketing campaigns to boost sales
• Eliminating the chances and duration of machine downtime

Conclusion

You must always remember that predictive analytics isn’t’ about creating static reports to tell you about the past and what happened. The real cause of use for predictive analytics and models involve telling business owners and professionals about the possible future outcomes based on previous data and how an organization proceeds.

Using machine learning algorithms, predictive models suggest what could happen in the future. The output is as good as the sources of input which are typically based on the consumers and businesses with changing needs, preferences, and behaviors.