Implementing a Successful Predictive Analytics-Based Business Strategy

In today’s world of big data, predictive analytics has become indispensable. Empowered by AI, you can turn your existing data into a gold mine. Yet, it can be both challenging and costly to implement. To get around these barriers, consider using AI software as a service. 

Streaming services, such as Netflix and Amazon Prime thrive on big data. Predictive models drive personalized user recommendations, making customers keep coming back for the shows they love. And they are not alone. Most banks use statistical modeling to manage credit and protect their clients from fraud. Healthcare organizations depend on social media analysis models that help identify high-risk patients. All these tools are part of predictive analytics, the practice of analyzing big data to find trends and patterns in user behavior.

Combining predictive analytics with Artificial Intelligence (AI) proves useful. AI-driven predictive analytics can consolidate data in a wide range of formats over multiple platforms. It allows banks and healthcare institutions to develop models that enable risk mitigation strategies. For businesses, it empowers them to create a personalized customer experience, building consumer loyalty. 

Challenges of Implementing Predictive Analytics 

AI-powered predictive analytics provides insights needed to stay ahead of the competition, predict customer behavior, and help businesses tailor services to individual needs. However, AI is still a tool, and it can fail if not used properly.

The most common failures are due to: 

  • Low-quality input data 
  • Choosing the wrong prediction algorithm or tech vendor 
  • Inadequate tools to process the data 
  • Inability to use the output 
  • low predictive ability of a model 

The output from low-quality data may be unusable, but subtle issues can also arise with seemingly viable data. Inconsistent data can exacerbate hidden biases, resulting in discriminatory – and possibly illegal business practices. Inaccurate or outdated data can easily result in false positives in a model’s prediction or bad decisions that result in wasted hours and extra costs. That hints that not only predictive analytics algorithms are important but data preparation, too. 

Always start by identifying which types of data you will be processing. Consolidating the data goes a long way towards reducing low-quality input data but can often be time-consuming. The data scientists then have to balance the need for viable data with the rigid budget constraints set by their department managers. Under such circumstances, it can be tempting to cut corners.

How to Implement Predictive Analytics While Avoiding Its Pitfalls 

Adobe, SAP, and Microsoft have started providing their software under a subscription plan. Some AI companies are following suit, providing predictive analytics as a service. They offer the same AI tools at a predictable, affordable price. At a more affordable price point, AI technology comes within reach without having to cut corners. 

A SaaS solution means more than just cost savings. It overcomes several barriers by providing access to variety and expertise. Natural language processing (NLP) empowers the AI to handle complex data, such as written communication. It can also consolidate duplicate entries and match them with other data across platforms. It frees the data scientists to focus their efforts where it matters the most. It also means they can use data already in the system, creating a veritable goldmine. 

Finally, make sure to monitor the output of your solution. This way, you can catch errors and problems before they grow out of control. It also empowers you to make small, impactful tweaks to improve what is already working. 

Key Takeaways 

Predictive analytics has become a ubiquitous tool for anyone wanting to maintain their competitive advantage, provide a personalized customer experience, or keep people safe. With AI and machine learning, predictive analytics can power up into a fast, reliable tool that can handle a far more comprehensive dataset than before. 

Now it’s time to turn your data into a goldmine: 

  • Define your data needs early to ensure your AI software can handle it 
  • Take advantage of predictive analytics as a service to save time and money 
  • Implement a quality assurance strategy to ensure the output meets expectations

Source: predictive analytics company InData Labs.

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